139 changed files with 831 additions and 22905 deletions
--- a/.env.example
+++ b/.env.example
@ -21,12 +21,10 @@ DATA_DIR=./data
 # IP this machine advertises to the coordinator (must be reachable from coordinator host)
 # CF_ORCH_ADVERTISE_HOST=10.1.10.71
-# GPU inference server (cf-orch coordinator for recipe scan, LLM generation, etc.)
+# CF-core hosted coordinator (managed cloud GPU inference — Paid+ tier)
-# GPU_SERVER_URL: set to your local cf-orch coordinator (self-hosted rack).
+# Set CF_ORCH_URL to use a hosted cf-orch coordinator instead of self-hosting.
-#   CF_ORCH_URL is the backward-compat alias — both are honoured.
+# CF_LICENSE_KEY is read automatically by CFOrchClient for bearer auth.
-# Paid+ default: when CF_LICENSE_KEY is present and neither URL is set,
+# CF_ORCH_URL=https://orch.circuitforge.tech
 #   the app automatically points to https://orch.circuitforge.tech.
 # GPU_SERVER_URL=http://10.1.10.71:7700
 # CF_LICENSE_KEY=CFG-KIWI-xxxx-xxxx-xxxx
 # LLM backend — env-var auto-config (no llm.yaml needed for bare-metal users)
@ -53,15 +51,6 @@ ENABLE_OCR=false
 DEBUG=false
 CLOUD_MODE=false
 DEMO_MODE=false
 # Product identifier reported in cf-orch coordinator analytics for per-app breakdown
 CF_APP_NAME=kiwi
 # USE_ORCH_SCHEDULER: use coordinator-aware multi-GPU scheduler instead of local FIFO.
 # Unset = auto-detect: true if CLOUD_MODE or circuitforge_orch is installed (paid+ local).
 # Set false to force LocalScheduler even when cf-orch is present.
 # USE_ORCH_SCHEDULER=false
 # GPU_SERVER_URL: cf-orch coordinator endpoint. Required for recipe scan (cf-docuvision)
 # and LLM features on a self-hosted rack. CF_ORCH_URL is the backward-compat alias.
 # GPU_SERVER_URL=http://10.1.10.71:7700
 # Cloud mode (set in compose.cloud.yml; also set here for reference)
 # CLOUD_DATA_ROOT=/devl/kiwi-cloud-data
--- a/.gitignore
+++ b/.gitignore
@ -23,9 +23,6 @@ dist/
 # Data directories
 data/
 # Local dev database
 *.db
 # Test artifacts (MagicMock sqlite files from pytest)
 <MagicMock*
--- a/.gitleaks.toml
+++ b/.gitleaks.toml
@ -3,16 +3,6 @@
 [extend]
 path = "/Library/Development/CircuitForge/circuitforge-hooks/gitleaks.toml"
 # ── Global allowlist ──────────────────────────────────────────────────────────
 # Amazon grocery department IDs (rh=n:<10-digit>) false-positive as phone
 # numbers. locale_config.py is a static lookup table with no secrets.
 [allowlist]
 # Amazon grocery dept IDs (rh=n:<digits>) false-positive as phone numbers.
 regexes = [
    '''rh=n:\d{8,12}''',
 ]
 # ── Test fixture allowlists ───────────────────────────────────────────────────
 [[rules]]
--- a/6
+++ b/6
@ -11,9 +11,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 COPY circuitforge-core/ ./circuitforge-core/
 RUN conda run -n base pip install --no-cache-dir -e ./circuitforge-core
 # Install circuitforge-orch — needed for the cf-orch-agent sidecar (compose.override.yml)
 COPY circuitforge-orch/ ./circuitforge-orch/
 # Create kiwi conda env and install app
 COPY kiwi/environment.yml .
 RUN conda env create -f environment.yml
@ -25,9 +22,8 @@ COPY kiwi/ ./kiwi/
 # they never end up in the cloud image regardless of .dockerignore placement.
 RUN rm -f /app/kiwi/.env
-# Install cf-core and cf-orch into the kiwi env BEFORE installing kiwi
+# Install cf-core into the kiwi env BEFORE installing kiwi (kiwi lists it as a dep)
 RUN conda run -n kiwi pip install --no-cache-dir -e /app/circuitforge-core
 RUN conda run -n kiwi pip install --no-cache-dir -e /app/circuitforge-orch
 WORKDIR /app/kiwi
 RUN conda run -n kiwi pip install --no-cache-dir -e .
--- a/README.md
+++ b/README.md
@ -1,58 +1,42 @@
-<!-- Logo coming soon — replace docs/kiwi-logo.svg when final icon ships -->
+# 🥝 Kiwi
 <div align="center">
  <img src="docs/kiwi-logo.svg" alt="Kiwi logo" width="96" height="96" />
-  # Kiwi
+> *Part of the CircuitForge LLC "AI for the tasks the system made hard on purpose" suite.*
-  **Pantry tracking and recipe suggestions — with or without an LLM.**
+**Pantry tracking and leftover recipe suggestions.**
-  [![License: MIT/BSL](https://img.shields.io/badge/license-MIT%20%2F%20BSL%201.1-blue)](#license)
+Scan barcodes, photograph receipts, and get recipe ideas based on what you already have — before it expires.
  [![CI](https://git.opensourcesolarpunk.com/Circuit-Forge/kiwi/badges/workflows/ci.yml/badge.svg)](https://git.opensourcesolarpunk.com/Circuit-Forge/kiwi/actions)
  [![Version](https://img.shields.io/badge/version-0.6.0-green)](https://git.opensourcesolarpunk.com/Circuit-Forge/kiwi/releases)
-  [Documentation](https://docs.circuitforge.tech/kiwi) · [Live demo](https://menagerie.circuitforge.tech/kiwi) · [circuitforge.tech](https://circuitforge.tech)
+**LLM support is optional.** Inventory tracking, barcode scanning, expiry alerts, CSV export, and receipt upload all work without any LLM configured. AI features (receipt OCR, recipe suggestions, meal planning) activate when a backend is available and are BYOK-unlockable at any tier.
-  *Part of the CircuitForge LLC suite — "AI for the tasks the system made hard on purpose."*
+**Status:** Beta · CircuitForge LLC
-</div>
+
 **[Documentation](https://docs.circuitforge.tech/kiwi/)** · [circuitforge.tech](https://circuitforge.tech)
 ---
-> **The LLM is optional.** Barcode scanning, receipt upload, expiry alerts, the full 200k+ recipe browser, and CSV export all work with zero LLM configured. Recipe suggestions and receipt OCR activate when a backend is available, and are BYOK-unlockable at any tier. You are never forced to send your data anywhere.
+## What it does
---
+- **Inventory tracking** — add items by barcode scan, receipt upload, or manually
 - **Expiry alerts** — know what's about to go bad
 - **Recipe browser** — browse the full recipe corpus by cuisine, meal type, dietary preference, or main ingredient; pantry match percentage shown inline (Free)
 - **Saved recipes** — bookmark any recipe with notes, a 0–5 star rating, and free-text style tags (Free); organize into named collections (Paid)
 - **Receipt OCR** — extract line items from receipt photos automatically (Paid tier, BYOK-unlockable)
 - **Recipe suggestions** — four levels from pantry-match to full LLM generation (Paid tier, BYOK-unlockable)
 - **Style auto-classifier** — LLM suggests style tags (comforting, hands-off, quick, etc.) for saved recipes (Paid tier, BYOK-unlockable)
 - **Leftover mode** — prioritize nearly-expired items in recipe ranking (Free, 5/day; unlimited at Paid+)
 - **LLM backend config** — configure inference via `circuitforge-core` env-var system; BYOK unlocks Paid AI features at any tier
 - **Feedback FAB** — in-app feedback button; status probed on load, hidden if CF feedback endpoint unreachable
-## What Kiwi does
+## Stack
-| Feature | Notes |
+- **Frontend:** Vue 3 SPA (Vite + TypeScript)
-|---|---|
+- **Backend:** FastAPI + SQLite (via `circuitforge-core`)
-| **Inventory tracking** | Add items by barcode scan, receipt upload, or manually |
+- **Auth:** CF session cookie → Directus JWT (cloud mode)
-| **Expiry alerts** | Know what is about to go bad before it does |
+- **Licensing:** Heimdall (free tier auto-provisioned at signup)
 | **Recipe browser** | 200k+ recipes — filter by cuisine, meal type, dietary preference, or main ingredient; pantry match percentage shown inline |
 | **Leftover mode** | Prioritizes nearly-expired items in recipe ranking (5/day free, unlimited at Paid+) |
 | **Recipe suggestions** | Four levels: direct corpus match, substitution/swap, cuisine-style adapter, full LLM generation |
 | **Meal planning** | Plan meals for the week; pull from saved recipes or suggestions |
 | **Saved recipes** | Bookmark any recipe with notes, 0-5 star rating, and free-text style tags; organize into named collections (Paid) |
 | **Receipt OCR** | Extract line items from receipt photos automatically |
 | **Dietary profiles** | Vegan, gluten-free, diabetic, and other constraints respected throughout |
 | **Style auto-classifier** | LLM suggests style tags (comforting, hands-off, quick, etc.) for saved recipes |
 | **Community feed** | Browse and share recipes with other Kiwi users |
 | **CSV export** | Full pantry export, always available, no tier gate |
---
+## Running locally
 ## Quick start
 **One-line install (self-hosted, Docker required):**
 ```bash
 bash <(curl -fsSL https://git.opensourcesolarpunk.com/Circuit-Forge/kiwi/raw/branch/main/install.sh)
 ```
 **Or clone and run manually:**
 ```bash
 git clone https://git.opensourcesolarpunk.com/Circuit-Forge/kiwi.git
 cd kiwi
 cp .env.example .env
 ./manage.sh build
 ./manage.sh start
@ -60,59 +44,37 @@ cp .env.example .env
 # API: http://localhost:8512
 ```
-**Live cloud instance** (free account required):
+## Cloud instance
 [menagerie.circuitforge.tech/kiwi](https://menagerie.circuitforge.tech/kiwi)
-Full setup and configuration guide: [docs.circuitforge.tech/kiwi](https://docs.circuitforge.tech/kiwi)
+```bash
-
+./manage.sh cloud-build
---
+./manage.sh cloud-start
 # Served at menagerie.circuitforge.tech/kiwi (JWT-gated)
 ```
 ## Tiers
 | Feature | Free | Paid | Premium |
-|---|:---:|:---:|:---:|
+|---------|------|------|---------|
-| Inventory CRUD | Yes | Yes | Yes |
+| Inventory CRUD | ✓ | ✓ | ✓ |
-| Barcode scan | Yes | Yes | Yes |
+| Barcode scan | ✓ | ✓ | ✓ |
-| Receipt upload | Yes | Yes | Yes |
+| Receipt upload | ✓ | ✓ | ✓ |
-| Expiry alerts | Yes | Yes | Yes |
+| Expiry alerts | ✓ | ✓ | ✓ |
-| CSV export | Yes | Yes | Yes |
+| CSV export | ✓ | ✓ | ✓ |
-| Recipe browser (200k+ recipes) | Yes | Yes | Yes |
+| Recipe browser (domain/category) | ✓ | ✓ | ✓ |
-| Save recipes + notes + star rating | Yes | Yes | Yes |
+| Save recipes + notes + star rating | ✓ | ✓ | ✓ |
-| Style tags (manual, free-text) | Yes | Yes | Yes |
+| Style tags (manual, free-text) | ✓ | ✓ | ✓ |
-| Leftover mode (5/day) | Yes | Yes | Yes |
+| Receipt OCR | BYOK | ✓ | ✓ |
-| Receipt OCR | BYOK | Yes | Yes |
+| Recipe suggestions (L1–L4) | BYOK | ✓ | ✓ |
-| Recipe suggestions (L1–L4) | BYOK | Yes | Yes |
+| Named recipe collections | — | ✓ | ✓ |
-| Named recipe collections | — | Yes | Yes |
+| LLM style auto-classifier | — | BYOK | ✓ |
-| LLM style auto-classifier | — | BYOK | Yes |
+| Meal planning | — | ✓ | ✓ |
-| Meal planning | — | Yes | Yes |
+| Multi-household | — | — | ✓ |
-| Multi-household | — | — | Yes |
+| Leftover mode (5/day) | ✓ | ✓ | ✓ |
-**BYOK** = bring your own LLM backend. Configure `~/.config/circuitforge/llm.yaml` to unlock AI features at any tier without a paid subscription.
+BYOK = bring your own LLM backend (configure `~/.config/circuitforge/llm.yaml`)
 ---
 ## Stack
 - **Frontend:** Vue 3 SPA (Vite + TypeScript), served on port 8511
 - **Backend:** FastAPI + SQLite via `circuitforge-core`, API on port 8512
 - **Auth:** CircuitForge session cookie (cloud mode); local mode requires no account
 - **Licensing:** Heimdall — free tier auto-provisioned at signup
 ---
 ## Forgejo-primary
 Kiwi is developed and maintained on Forgejo at [git.opensourcesolarpunk.com/Circuit-Forge/kiwi](https://git.opensourcesolarpunk.com/Circuit-Forge/kiwi). GitHub and Codeberg are read-only mirrors. File issues and submit pull requests on Forgejo.
 ---
 ## License
-Kiwi uses a split license:
+Discovery/pipeline layer: MIT
-
+AI features: BSL 1.1 (free for personal non-commercial self-hosting)
 - **Discovery and inventory pipeline** (barcode scan, expiry tracking, pantry CRUD, CSV export, recipe browser): [MIT](LICENSE-MIT)
 - **AI features** (receipt OCR, LLM recipe suggestions, style auto-classifier): [BSL 1.1](LICENSE-BSL) — free for personal non-commercial self-hosting; commercial use or SaaS re-hosting requires a paid license. Converts to MIT after 4 years.
 Humans own design, architecture, code review, testing, and verification. LLMs are part of our development workflow. [Our positions on LLM use →](https://circuitforge.tech/positions)
 Privacy · Safety · Accessibility — co-equal, non-negotiable across all CircuitForge products.
--- a/app/api/endpoints/activitypub.py
+++ b/app/api/endpoints/activitypub.py
@ -1,332 +0,0 @@
 # app/api/endpoints/activitypub.py
 # MIT License
 #
 # ActivityPub endpoints for Kiwi instances:
 #   GET  /.well-known/webfinger   — WebFinger JRD
 #   GET  /ap/actor                — Instance actor document
 #   POST /ap/actor/inbox          — Incoming activities
 #   GET  /ap/outbox               — Outgoing activities (OrderedCollection)
 #   GET  /ap/posts/{slug}         — Individual AP Note
 #   GET  /ap/followers            — Followers collection (count only)
 #   GET  /ap/following            — Following collection (empty stub)
 #
 # All endpoints are no-ops / 404 when AP_ENABLED=false or actor not loaded.
 # The WebFinger and well-known routes are mounted at the root app level (not
 # under /api/v1) — see main.py.
 from __future__ import annotations
 import asyncio
 import json
 import logging
 from datetime import datetime, timezone
 from fastapi import APIRouter, HTTPException, Request, Response
 from fastapi.responses import JSONResponse
 from app.core.config import settings
 from app.services.ap.keys import get_actor
 logger = logging.getLogger(__name__)
 # ── Two routers: one for well-known (root mount), one for /ap prefix ─────────
 webfinger_router = APIRouter(tags=["activitypub"])
 ap_router = APIRouter(prefix="/ap", tags=["activitypub"])
 _AP_CONTENT_TYPE = "application/activity+json"
 _JRD_CONTENT_TYPE = "application/jrd+json"
 def _actor_required():
    actor = get_actor()
    if actor is None:
        raise HTTPException(status_code=404, detail="ActivityPub not enabled on this instance.")
    return actor
 # ── WebFinger ─────────────────────────────────────────────────────────────────
@webfinger_router.get("/.well-known/webfinger")
 async def webfinger(resource: str | None = None):
    actor = get_actor()
    if actor is None:
        raise HTTPException(status_code=404, detail="ActivityPub not enabled.")
    expected = f"acct:kiwi@{settings.AP_HOST}"
    if resource and resource != expected:
        raise HTTPException(status_code=404, detail=f"Resource {resource!r} not found.")
    jrd = {
        "subject": expected,
        "links": [
            {
                "rel": "self",
                "type": _AP_CONTENT_TYPE,
                "href": actor.actor_id,
            }
        ],
    }
    return Response(
        content=json.dumps(jrd),
        media_type=_JRD_CONTENT_TYPE,
    )
 # ── Actor ─────────────────────────────────────────────────────────────────────
@ap_router.get("/actor")
 async def get_actor_doc():
    actor = _actor_required()
    return Response(
        content=json.dumps(actor.to_ap_dict()),
        media_type=_AP_CONTENT_TYPE,
    )
 # ── Inbox (mounted via make_inbox_router below) ───────────────────────────────
 async def _on_follow(activity: dict, headers: dict) -> None:
    """Accept Follow: add to ap_followers, send Accept(Follow) back."""
    actor_url = activity.get("actor", "")
    if not actor_url:
        return
    from app.db.store import Store
    from app.core.config import settings as _settings
    db_path = _settings.DB_PATH
    inbox_url, shared_inbox = await asyncio.to_thread(_resolve_inbox, actor_url)
    if inbox_url is None:
        return
    import sqlite3
    conn = sqlite3.connect(str(db_path))
    try:
        conn.execute(
            """INSERT OR REPLACE INTO ap_followers
               (actor_id, inbox_url, shared_inbox, followed_at, active)
               VALUES (?, ?, ?, ?, 1)""",
            (actor_url, inbox_url, shared_inbox, datetime.now(timezone.utc).isoformat()),
        )
        conn.commit()
    finally:
        conn.close()
    actor = get_actor()
    if actor is None:
        return
    accept = {
        "@context": "https://www.w3.org/ns/activitystreams",
        "id": f"{actor.actor_id}/accepts/{activity.get('id', 'unknown')}",
        "type": "Accept",
        "actor": actor.actor_id,
        "object": activity,
    }
    from circuitforge_core.activitypub import deliver_activity
    await asyncio.to_thread(deliver_activity, accept, inbox_url, actor, 10.0)
 async def _on_undo(activity: dict, headers: dict) -> None:
    """Handle Undo(Follow): deactivate the follower row."""
    inner = activity.get("object", {})
    if isinstance(inner, dict) and inner.get("type") == "Follow":
        actor_url = activity.get("actor", "")
        if actor_url:
            import sqlite3
            conn = sqlite3.connect(str(settings.DB_PATH))
            try:
                conn.execute(
                    "UPDATE ap_followers SET active = 0 WHERE actor_id = ?", (actor_url,)
                )
                conn.commit()
            finally:
                conn.close()
 async def _dedup_activity(activity_id: str | None) -> bool:
    """Return True (already seen) if activity_id is in ap_received; otherwise insert it."""
    if not activity_id:
        return False
    import sqlite3
    conn = sqlite3.connect(str(settings.DB_PATH))
    try:
        try:
            conn.execute(
                "INSERT INTO ap_received (activity_id) VALUES (?)", (activity_id,)
            )
            conn.commit()
            return False
        except sqlite3.IntegrityError:
            return True
    finally:
        conn.close()
 def _build_inbox_router():
    from circuitforge_core.activitypub.inbox import make_inbox_router
    async def on_follow(activity: dict, headers: dict) -> None:
        if await _dedup_activity(activity.get("id")):
            return
        await _on_follow(activity, headers)
    async def on_undo(activity: dict, headers: dict) -> None:
        if await _dedup_activity(activity.get("id")):
            return
        await _on_undo(activity, headers)
    return make_inbox_router(
        handlers={"Follow": on_follow, "Undo": on_undo},
        verify_key_fetcher=None,   # Signature verification enabled in prod when actor is loaded
        path="/inbox",
    )
 # Mount inbox at /ap/actor/inbox (AP spec: inbox is a sub-resource of the actor)
 try:
    _inbox_sub = _build_inbox_router()
    ap_router.include_router(_inbox_sub, prefix="/actor")
 except Exception as _e:
    logger.warning("AP inbox router not available: %s", _e)
 # ── Outbox ────────────────────────────────────────────────────────────────────
@ap_router.get("/outbox")
 async def get_outbox(page: int | None = None, request: Request = None):
    actor = _actor_required()
    from app.api.endpoints.community import _get_community_store
    store = _get_community_store()
    base = f"https://{settings.AP_HOST}"
    if store is None:
        collection = {
            "@context": "https://www.w3.org/ns/activitystreams",
            "id": f"{actor.outbox_url}",
            "type": "OrderedCollection",
            "totalItems": 0,
            "orderedItems": [],
        }
        return Response(content=json.dumps(collection), media_type=_AP_CONTENT_TYPE)
    PAGE_SIZE = 20
    offset = ((page or 1) - 1) * PAGE_SIZE
    posts = await asyncio.to_thread(store.list_posts, limit=PAGE_SIZE, offset=offset)
    items = [_post_to_ap_note(p, actor, base) for p in posts]
    collection = {
        "@context": "https://www.w3.org/ns/activitystreams",
        "id": actor.outbox_url + (f"?page={page}" if page else ""),
        "type": "OrderedCollectionPage" if page else "OrderedCollection",
        "orderedItems": items,
    }
    return Response(content=json.dumps(collection), media_type=_AP_CONTENT_TYPE)
 # ── Individual post ───────────────────────────────────────────────────────────
@ap_router.get("/posts/{slug}")
 async def get_ap_post(slug: str):
    actor = _actor_required()
    from app.api.endpoints.community import _get_community_store
    store = _get_community_store()
    if store is None:
        raise HTTPException(status_code=404, detail="Community DB not available.")
    post = await asyncio.to_thread(store.get_post_by_slug, slug)
    if post is None:
        raise HTTPException(status_code=404, detail="Post not found.")
    base = f"https://{settings.AP_HOST}"
    note = _post_to_ap_note(post, actor, base)
    return Response(content=json.dumps(note), media_type=_AP_CONTENT_TYPE)
 # ── Followers / Following ─────────────────────────────────────────────────────
@ap_router.get("/followers")
 async def get_followers():
    actor = _actor_required()
    import sqlite3
    count = 0
    try:
        conn = sqlite3.connect(str(settings.DB_PATH))
        row = conn.execute("SELECT COUNT(*) FROM ap_followers WHERE active = 1").fetchone()
        conn.close()
        count = row[0] if row else 0
    except Exception:
        pass
    collection = {
        "@context": "https://www.w3.org/ns/activitystreams",
        "id": f"{actor.actor_id}/followers",
        "type": "OrderedCollection",
        "totalItems": count,
    }
    return Response(content=json.dumps(collection), media_type=_AP_CONTENT_TYPE)
@ap_router.get("/following")
 async def get_following():
    actor = _actor_required()
    collection = {
        "@context": "https://www.w3.org/ns/activitystreams",
        "id": f"{actor.actor_id}/following",
        "type": "OrderedCollection",
        "totalItems": 0,
        "orderedItems": [],
    }
    return Response(content=json.dumps(collection), media_type=_AP_CONTENT_TYPE)
 # ── Helpers ───────────────────────────────────────────────────────────────────
 def _post_to_ap_note(post, actor, base_url: str) -> dict:
    from circuitforge_core.activitypub import make_note
    from app.services.community.ap_compat import _build_content
    diet_tags: list[str] = list(getattr(post, "dietary_tags", []) or [])
    hashtags = [{"type": "Hashtag", "name": "#Kiwi", "href": f"{base_url}/ap/tags/kiwi"}]
    for tag in diet_tags[:4]:
        ht = "".join(w.capitalize() for w in tag.replace("-", " ").split())
        hashtags.append({"type": "Hashtag", "name": f"#{ht}"})
    content = _build_content(
        {
            "title": post.title,
            "description": getattr(post, "description", None),
            "outcome_notes": getattr(post, "outcome_notes", None),
            "dietary_tags": diet_tags,
        }
    )
    published = post.published
    note = make_note(
        actor_id=actor.actor_id,
        content=content,
        tag=hashtags,
        published=published if isinstance(published, datetime) else None,
    )
    note["id"] = f"{base_url}/ap/posts/{post.slug}"
    return note
 def _resolve_inbox(actor_url: str) -> tuple[str | None, str | None]:
    """Fetch an AP actor document and extract inbox + sharedInbox URLs."""
    try:
        import httpx
        resp = httpx.get(
            actor_url,
            headers={"Accept": "application/activity+json"},
            timeout=8.0,
            follow_redirects=True,
        )
        resp.raise_for_status()
        doc = resp.json()
        inbox = doc.get("inbox")
        shared = doc.get("endpoints", {}).get("sharedInbox")
        return inbox, shared
    except Exception as exc:
        logger.debug("Could not resolve actor %s: %s", actor_url, exc)
        return None, None
--- a/app/api/endpoints/community.py
+++ b/app/api/endpoints/community.py
@ -167,54 +167,6 @@ def _validate_publish_body(body: dict) -> None:
        raise HTTPException(status_code=422, detail="photo_url must be an https:// URL.")
@router.post("/check-similar")
 async def check_similar(body: dict, session: CloudUser = Depends(get_session)):
    """Pre-submission dedup check: return similar existing posts for the given title/recipe_id.
    Safe to call with no community store configured — returns empty list rather than 503.
    """
    store = _get_community_store()
    if store is None:
        return {"similar_posts": []}
    title = (body.get("title") or "").strip()
    recipe_id = body.get("recipe_id")
    post_type = body.get("post_type")
    if not title:
        return {"similar_posts": []}
    candidates = await asyncio.to_thread(
        store.search_similar_posts,
        title,
        recipe_id,
        post_type,
        8,
    )
    if not candidates:
        return {"similar_posts": []}
    from app.services.community.dedup import build_similar_post_result, fetch_recipe_ingredients
    incoming_ingredients = await asyncio.to_thread(
        fetch_recipe_ingredients, session.db, recipe_id
    )
    results = []
    for post in candidates:
        result = await asyncio.to_thread(
            build_similar_post_result,
            post,
            recipe_id,
            incoming_ingredients,
            session.db,
        )
        if result["similarity_tier"] != "different":
            results.append(result)
    return {"similar_posts": results[:5]}
@router.post("/posts", status_code=201)
 async def publish_post(body: dict, session: CloudUser = Depends(get_session)):
    from app.tiers import can_use
@ -262,8 +214,6 @@ async def publish_post(body: dict, session: CloudUser = Depends(get_session)):
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    slug = f"kiwi-{_post_type_prefix(post_type)}-{pseudonym.lower().replace(' ', '')}-{today}-{slug_title}"[:120]
    similar_to_ref = body.get("similar_to_ref") or None
    from circuitforge_core.community.models import CommunityPost
    post = CommunityPost(
        slug=slug,
@ -291,7 +241,6 @@ async def publish_post(body: dict, session: CloudUser = Depends(get_session)):
        fat_pct=snapshot.fat_pct,
        protein_pct=snapshot.protein_pct,
        moisture_pct=snapshot.moisture_pct,
        similar_to_ref=similar_to_ref,
    )
    try:
@ -301,41 +250,7 @@ async def publish_post(body: dict, session: CloudUser = Depends(get_session)):
            status_code=409,
            detail="A post with this title already exists today. Try a different title.",
        ) from exc
-
+    return _post_to_dict(inserted)
    post_dict = _post_to_dict(inserted)
    # AP delivery + Mastodon post (Paid tier, AP_ENABLED, opted-in)
    from app.core.config import settings as _settings
    if _settings.AP_ENABLED and session.tier in ("paid", "premium", "ultra"):
        from circuitforge_core.activitypub import make_create, make_note, PUBLIC
        from app.services.ap.keys import get_actor
        from app.services.ap.delivery import deliver_to_followers
        _ap_actor = get_actor()
        if _ap_actor is not None:
            base = f"https://{_settings.AP_HOST}"
            from app.api.endpoints.activitypub import _post_to_ap_note
            _note = _post_to_ap_note(inserted, _ap_actor, base)
            _activity = make_create(_ap_actor, _note)
            asyncio.create_task(
                asyncio.to_thread(
                    deliver_to_followers, inserted.slug, _activity, session.db
                )
            )
        # Mastodon post if user has connected account and opted in
        if body.get("post_to_mastodon"):
            from app.services.ap.mastodon import build_post_content, get_token, post_status
            _masto = await asyncio.to_thread(
                get_token, session.db, session.user_id, _settings.AP_TOKEN_ENCRYPTION_KEY
            )
            if _masto:
                _masto_url, _masto_token = _masto
                _content = build_post_content(post_dict)
                asyncio.create_task(
                    asyncio.to_thread(post_status, _masto_url, _masto_token, _content)
                )
    return post_dict
@router.delete("/posts/{slug}", status_code=204)
@ -436,7 +351,6 @@ def _post_to_dict(post) -> dict:
        "fat_pct": post.fat_pct,
        "protein_pct": post.protein_pct,
        "moisture_pct": post.moisture_pct,
        "similar_to_ref": getattr(post, "similar_to_ref", None),
    }
--- a/app/api/endpoints/corrections.py
+++ b/app/api/endpoints/corrections.py
@ -1,5 +0,0 @@
 # app/api/endpoints/corrections.py — user corrections to LLM output for SFT training
 from circuitforge_core.api import make_corrections_router
 from app.db.session import get_db
 router = make_corrections_router(get_db=get_db, product="kiwi")
--- a/app/api/endpoints/household.py
+++ b/app/api/endpoints/household.py
@ -11,8 +11,7 @@ import sqlite3
 import requests
 from fastapi import APIRouter, Depends, HTTPException
-from app.cloud_session import CloudUser, CLOUD_DATA_ROOT, get_session
+from app.cloud_session import CloudUser, CLOUD_DATA_ROOT, HEIMDALL_URL, HEIMDALL_ADMIN_TOKEN, get_session
 from app.services.heimdall_orch import HEIMDALL_URL, HEIMDALL_ADMIN_TOKEN
 from app.db.store import Store
 from app.models.schemas.household import (
    HouseholdAcceptRequest,
--- a/app/api/endpoints/inventory.py
+++ b/app/api/endpoints/inventory.py
@ -37,21 +37,14 @@ from app.models.schemas.inventory import (
    TagCreate,
    TagResponse,
 )
 from app.models.schemas.label_capture import LabelConfirmRequest
 router = APIRouter()
 # ── Helpers ───────────────────────────────────────────────────────────────────
-def _user_constraints(store) -> list[str]:
+def _enrich_item(item: dict) -> dict:
-    """Load active dietary constraints from user settings (comma-separated string)."""
+    """Attach computed fields: opened_expiry_date, secondary_state/uses/warning."""
    raw = store.get_setting("dietary_constraints") or ""
    return [c.strip() for c in raw.split(",") if c.strip()]
 def _enrich_item(item: dict, user_constraints: list[str] | None = None) -> dict:
    """Attach computed fields: opened_expiry_date, secondary_state/uses/warning/discard_signs."""
    from datetime import date, timedelta
    opened = item.get("opened_date")
    if opened:
@ -65,16 +58,13 @@ def _enrich_item(item: dict, user_constraints: list[str] | None = None) -> dict:
    if "opened_expiry_date" not in item:
        item = {**item, "opened_expiry_date": None}
-    # Secondary use window — check sell-by date (not opened expiry).
+    # Secondary use window — check sell-by date (not opened expiry)
    # Apply dietary constraint filter (e.g. wine suppressed for halal/alcohol-free).
    sec = _predictor.secondary_state(item.get("category"), item.get("expiration_date"))
    sec = _predictor.filter_secondary_by_constraints(sec, user_constraints or [])
    item = {
        **item,
        "secondary_state": sec["label"] if sec else None,
        "secondary_uses": sec["uses"] if sec else None,
        "secondary_warning": sec["warning"] if sec else None,
        "secondary_discard_signs": sec["discard_signs"] if sec else None,
    }
    return item
@ -181,10 +171,7 @@ async def create_inventory_item(
        notes=body.notes,
        source=body.source,
    )
-    # RETURNING * omits joined columns (product_name, barcode, category).
+    return InventoryItemResponse.model_validate(item)
    # Re-fetch with the products JOIN so the response is fully populated (#99).
    full_item = await asyncio.to_thread(store.get_inventory_item, item["id"])
    return InventoryItemResponse.model_validate(full_item)
@router.post("/items/bulk-add-by-name", response_model=BulkAddByNameResponse)
@ -222,15 +209,13 @@ async def list_inventory_items(
    store: Store = Depends(get_store),
 ):
    items = await asyncio.to_thread(store.list_inventory, location, item_status)
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return [InventoryItemResponse.model_validate(_enrich_item(i)) for i in items]
    return [InventoryItemResponse.model_validate(_enrich_item(i, constraints)) for i in items]
@router.get("/items/expiring", response_model=List[InventoryItemResponse])
 async def get_expiring_items(days: int = 7, store: Store = Depends(get_store)):
    items = await asyncio.to_thread(store.expiring_soon, days)
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return [InventoryItemResponse.model_validate(_enrich_item(i)) for i in items]
    return [InventoryItemResponse.model_validate(_enrich_item(i, constraints)) for i in items]
@router.get("/items/{item_id}", response_model=InventoryItemResponse)
@ -238,8 +223,7 @@ async def get_inventory_item(item_id: int, store: Store = Depends(get_store)):
    item = await asyncio.to_thread(store.get_inventory_item, item_id)
    if not item:
        raise HTTPException(status_code=404, detail="Inventory item not found")
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return InventoryItemResponse.model_validate(_enrich_item(item))
    return InventoryItemResponse.model_validate(_enrich_item(item, constraints))
@router.patch("/items/{item_id}", response_model=InventoryItemResponse)
@ -256,8 +240,7 @@ async def update_inventory_item(
    item = await asyncio.to_thread(store.update_inventory_item, item_id, **updates)
    if not item:
        raise HTTPException(status_code=404, detail="Inventory item not found")
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return InventoryItemResponse.model_validate(_enrich_item(item))
    return InventoryItemResponse.model_validate(_enrich_item(item, constraints))
@router.post("/items/{item_id}/open", response_model=InventoryItemResponse)
@ -271,8 +254,7 @@ async def mark_item_opened(item_id: int, store: Store = Depends(get_store)):
    )
    if not item:
        raise HTTPException(status_code=404, detail="Inventory item not found")
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return InventoryItemResponse.model_validate(_enrich_item(item))
    return InventoryItemResponse.model_validate(_enrich_item(item, constraints))
@router.post("/items/{item_id}/consume", response_model=InventoryItemResponse)
@ -301,8 +283,7 @@ async def consume_item(
        )
    if not item:
        raise HTTPException(status_code=404, detail="Inventory item not found")
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return InventoryItemResponse.model_validate(_enrich_item(item))
    return InventoryItemResponse.model_validate(_enrich_item(item, constraints))
@router.post("/items/{item_id}/discard", response_model=InventoryItemResponse)
@ -326,8 +307,7 @@ async def discard_item(
    )
    if not item:
        raise HTTPException(status_code=404, detail="Inventory item not found")
-    constraints = await asyncio.to_thread(_user_constraints, store)
+    return InventoryItemResponse.model_validate(_enrich_item(item))
    return InventoryItemResponse.model_validate(_enrich_item(item, constraints))
@router.delete("/items/{item_id}", status_code=status.HTTP_204_NO_CONTENT)
@ -350,31 +330,6 @@ class BarcodeScanTextRequest(BaseModel):
    auto_add_to_inventory: bool = True
 def _captured_to_product_info(row: dict) -> dict:
    """Convert a captured_products row to the product_info dict shape used by
    the barcode scan flow (mirrors what OpenFoodFactsService returns)."""
    macros: dict = {}
    for field in ("calories", "fat_g", "saturated_fat_g", "carbs_g", "sugar_g",
                  "fiber_g", "protein_g", "sodium_mg", "serving_size_g"):
        if row.get(field) is not None:
            macros[field] = row[field]
    return {
        "name": row.get("product_name") or row.get("barcode", "Unknown Product"),
        "brand": row.get("brand"),
        "category": None,
        "nutrition_data": macros,
        "ingredient_names": row.get("ingredient_names") or [],
        "allergens": row.get("allergens") or [],
        "source": "visual_capture",
    }
 def _gap_message(tier: str, has_visual_capture: bool) -> str:
    if has_visual_capture:
        return "We couldn't find this product. Photograph the nutrition label to add it."
    return "Not found in any product database — add manually"
@router.post("/scan/text", response_model=BarcodeScanResponse)
 async def scan_barcode_text(
    body: BarcodeScanTextRequest,
@ -385,21 +340,10 @@ async def scan_barcode_text(
    log.info("scan auth=%s tier=%s barcode=%r", _auth_label(session.user_id), session.tier, body.barcode)
    from app.services.openfoodfacts import OpenFoodFactsService
    from app.services.expiration_predictor import ExpirationPredictor
    from app.tiers import can_use
    predictor = ExpirationPredictor()
    has_visual_capture = can_use("visual_label_capture", session.tier, session.has_byok)
    # 1. Check local captured-products cache before hitting FDC/OFF
    cached = await asyncio.to_thread(store.get_captured_product, body.barcode)
    if cached and cached.get("confirmed_by_user"):
        product_info: dict | None = _captured_to_product_info(cached)
        product_source = "visual_capture"
    else:
    off = OpenFoodFactsService()
    predictor = ExpirationPredictor()
    product_info = await off.lookup_product(body.barcode)
        product_source = "openfoodfacts"
    inventory_item = None
    if product_info and body.auto_add_to_inventory:
@ -410,7 +354,7 @@ async def scan_barcode_text(
            brand=product_info.get("brand"),
            category=product_info.get("category"),
            nutrition_data=product_info.get("nutrition_data", {}),
-            source=product_source,
+            source="openfoodfacts",
            source_data=product_info,
        )
        exp = predictor.predict_expiration(
@ -436,7 +380,6 @@ async def scan_barcode_text(
        result_product = None
    product_found = product_info is not None
    needs_capture = not product_found and has_visual_capture
    return BarcodeScanResponse(
        success=True,
        barcodes_found=1,
@ -446,9 +389,8 @@ async def scan_barcode_text(
            "product": result_product,
            "inventory_item": InventoryItemResponse.model_validate(inventory_item) if inventory_item else None,
            "added_to_inventory": inventory_item is not None,
-            "needs_manual_entry": not product_found and not needs_capture,
+            "needs_manual_entry": not product_found,
-            "needs_visual_capture": needs_capture,
+            "message": "Added to inventory" if inventory_item else "Not found in any product database — add manually",
            "message": "Added to inventory" if inventory_item else _gap_message(session.tier, needs_capture),
        }],
        message="Barcode processed",
    )
@ -465,9 +407,6 @@ async def scan_barcode_image(
 ):
    """Scan a barcode from an uploaded image. Requires Phase 2 scanner integration."""
    log.info("scan_image auth=%s tier=%s", _auth_label(session.user_id), session.tier)
    from app.tiers import can_use
    has_visual_capture = can_use("visual_label_capture", session.tier, session.has_byok)
    temp_dir = Path("/tmp/kiwi_barcode_scans")
    temp_dir.mkdir(parents=True, exist_ok=True)
    temp_file = temp_dir / f"{uuid.uuid4()}_{file.filename}"
@ -478,8 +417,7 @@ async def scan_barcode_image(
        from app.services.openfoodfacts import OpenFoodFactsService
        from app.services.expiration_predictor import ExpirationPredictor
-        image_bytes = temp_file.read_bytes()
+        barcodes = await asyncio.to_thread(BarcodeScanner().scan_image, temp_file)
        barcodes = await asyncio.to_thread(BarcodeScanner().scan_from_bytes, image_bytes)
        if not barcodes:
            return BarcodeScanResponse(
                success=False, barcodes_found=0, results=[],
@ -491,30 +429,19 @@ async def scan_barcode_image(
        results = []
        for bc in barcodes:
            code = bc["data"]
            # Check local visual-capture cache before hitting FDC/OFF
            cached = await asyncio.to_thread(store.get_captured_product, code)
            if cached and cached.get("confirmed_by_user"):
                product_info: dict | None = _captured_to_product_info(cached)
                product_source = "visual_capture"
            else:
            product_info = await off.lookup_product(code)
                product_source = "openfoodfacts"
            db_product = None
            inventory_item = None
-            if product_info:
+            if product_info and auto_add_to_inventory:
-                db_product, _ = await asyncio.to_thread(
+                product, _ = await asyncio.to_thread(
                    store.get_or_create_product,
                    product_info.get("name", code),
                    code,
                    brand=product_info.get("brand"),
                    category=product_info.get("category"),
                    nutrition_data=product_info.get("nutrition_data", {}),
-                    source=product_source,
+                    source="openfoodfacts",
                    source_data=product_info,
                )
                if auto_add_to_inventory:
                exp = predictor.predict_expiration(
                    product_info.get("category", ""),
                    location,
@ -526,23 +453,19 @@ async def scan_barcode_image(
                resolved_unit = product_info.get("pack_unit") or "count"
                inventory_item = await asyncio.to_thread(
                    store.add_inventory_item,
-                        db_product["id"], location,
+                    product["id"], location,
                    quantity=resolved_qty,
                    unit=resolved_unit,
                    expiration_date=str(exp) if exp else None,
                    source="barcode_scan",
                )
            product_found = db_product is not None
            needs_capture = not product_found and has_visual_capture
            results.append({
                "barcode": code,
                "barcode_type": bc.get("type", "unknown"),
-                "product": ProductResponse.model_validate(db_product) if db_product else None,
+                "product": ProductResponse.model_validate(product) if product_info else None,
                "inventory_item": InventoryItemResponse.model_validate(inventory_item) if inventory_item else None,
                "added_to_inventory": inventory_item is not None,
-                "needs_manual_entry": not product_found and not needs_capture,
+                "message": "Added to inventory" if inventory_item else "Barcode scanned",
                "needs_visual_capture": needs_capture,
                "message": "Added to inventory" if inventory_item else _gap_message(session.tier, needs_capture),
            })
        return BarcodeScanResponse(
            success=True, barcodes_found=len(barcodes), results=results,
@ -553,143 +476,6 @@ async def scan_barcode_image(
            temp_file.unlink()
 # ── Visual label capture (kiwi#79) ────────────────────────────────────────────
@router.post("/scan/label-capture")
 async def capture_nutrition_label(
    file: UploadFile = File(...),
    barcode: str = Form(...),
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Photograph a nutrition label for an unenriched product (paid tier).
    Sends the image to the vision model and returns structured nutrition data
    for user review.  Fields extracted with confidence < 0.7 should be
    highlighted in amber in the UI.
    """
    from app.tiers import can_use
    from app.models.schemas.label_capture import LabelCaptureResponse
    from app.services.label_capture import extract_label, needs_review as _needs_review
    if not can_use("visual_label_capture", session.tier, session.has_byok):
        raise HTTPException(status_code=403, detail="Visual label capture requires a Paid tier or higher.")
    log.info("label_capture tier=%s barcode=%r", session.tier, barcode)
    image_bytes = await file.read()
    extraction = await asyncio.to_thread(extract_label, image_bytes)
    return LabelCaptureResponse(
        barcode=barcode,
        product_name=extraction.get("product_name"),
        brand=extraction.get("brand"),
        serving_size_g=extraction.get("serving_size_g"),
        calories=extraction.get("calories"),
        fat_g=extraction.get("fat_g"),
        saturated_fat_g=extraction.get("saturated_fat_g"),
        carbs_g=extraction.get("carbs_g"),
        sugar_g=extraction.get("sugar_g"),
        fiber_g=extraction.get("fiber_g"),
        protein_g=extraction.get("protein_g"),
        sodium_mg=extraction.get("sodium_mg"),
        ingredient_names=extraction.get("ingredient_names") or [],
        allergens=extraction.get("allergens") or [],
        confidence=extraction.get("confidence", 0.0),
        needs_review=_needs_review(extraction),
    )
@router.post("/scan/label-confirm")
 async def confirm_nutrition_label(
    body: LabelConfirmRequest,
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Confirm and save a user-reviewed label extraction.
    Saves the product to the local cache so future scans of the same barcode
    resolve instantly without another capture.  Optionally adds the item to
    the user's inventory.
    """
    from app.tiers import can_use
    from app.models.schemas.label_capture import LabelConfirmResponse
    from app.services.expiration_predictor import ExpirationPredictor
    if not can_use("visual_label_capture", session.tier, session.has_byok):
        raise HTTPException(status_code=403, detail="Visual label capture requires a Paid tier or higher.")
    log.info("label_confirm tier=%s barcode=%r", session.tier, body.barcode)
    # Persist to local visual-capture cache
    await asyncio.to_thread(
        store.save_captured_product,
        body.barcode,
        product_name=body.product_name,
        brand=body.brand,
        serving_size_g=body.serving_size_g,
        calories=body.calories,
        fat_g=body.fat_g,
        saturated_fat_g=body.saturated_fat_g,
        carbs_g=body.carbs_g,
        sugar_g=body.sugar_g,
        fiber_g=body.fiber_g,
        protein_g=body.protein_g,
        sodium_mg=body.sodium_mg,
        ingredient_names=body.ingredient_names,
        allergens=body.allergens,
        confidence=body.confidence,
        confirmed_by_user=True,
    )
    product_id: int | None = None
    inventory_item_id: int | None = None
    if body.auto_add:
        predictor = ExpirationPredictor()
        nutrition = {}
        for field in ("calories", "fat_g", "saturated_fat_g", "carbs_g", "sugar_g",
                      "fiber_g", "protein_g", "sodium_mg", "serving_size_g"):
            val = getattr(body, field, None)
            if val is not None:
                nutrition[field] = val
        product, _ = await asyncio.to_thread(
            store.get_or_create_product,
            body.product_name or body.barcode,
            body.barcode,
            brand=body.brand,
            category=None,
            nutrition_data=nutrition,
            source="visual_capture",
            source_data={},
        )
        product_id = product["id"]
        exp = predictor.predict_expiration(
            "",
            body.location,
            product_name=body.product_name or body.barcode,
            tier=session.tier,
            has_byok=session.has_byok,
        )
        inv_item = await asyncio.to_thread(
            store.add_inventory_item,
            product_id, body.location,
            quantity=body.quantity,
            unit="count",
            expiration_date=str(exp) if exp else None,
            source="visual_capture",
        )
        inventory_item_id = inv_item["id"]
    return LabelConfirmResponse(
        ok=True,
        barcode=body.barcode,
        product_id=product_id,
        inventory_item_id=inventory_item_id,
        message="Product saved" + (" and added to inventory" if body.auto_add else ""),
    )
 # ── Tags ──────────────────────────────────────────────────────────────────────
@router.post("/tags", response_model=TagResponse, status_code=status.HTTP_201_CREATED)
--- a/app/api/endpoints/mastodon_oauth.py
+++ b/app/api/endpoints/mastodon_oauth.py
@ -1,133 +0,0 @@
 # app/api/endpoints/mastodon_oauth.py
 # MIT License
 #
 # Mastodon OAuth flow endpoints:
 #   POST   /social/mastodon/connect     — Start OAuth (dynamic app registration)
 #   GET    /social/mastodon/callback    — OAuth callback, exchange code for token
 #   DELETE /social/mastodon/disconnect  — Revoke and remove stored token
 #   GET    /social/mastodon/status      — Check connection status
 from __future__ import annotations
 import asyncio
 import logging
 from urllib.parse import urlencode
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import RedirectResponse
 from app.cloud_session import CloudUser, get_session
 from app.core.config import settings
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/social/mastodon", tags=["mastodon"])
 def _redirect_uri() -> str:
    host = settings.AP_HOST or "localhost:8512"
    return f"https://{host}/api/v1/social/mastodon/callback"
 # In-memory pending state: maps state_token → {instance_url, client_id, client_secret, user_id}
 # A real deployment would persist this in a short-TTL cache or DB.
 _pending: dict[str, dict] = {}
@router.post("/connect")
 async def connect_mastodon(body: dict, session: CloudUser = Depends(get_session)):
    """Start the Mastodon OAuth flow.
    Body: {"instance_url": "https://mastodon.social"}
    Returns: {"authorize_url": "..."}
    """
    import secrets
    from app.services.ap.mastodon import build_authorize_url, register_app
    instance_url = (body.get("instance_url") or "").strip().rstrip("/")
    if not instance_url.startswith("https://"):
        raise HTTPException(status_code=422, detail="instance_url must be an https:// URL.")
    redirect_uri = _redirect_uri()
    try:
        app_creds = await asyncio.to_thread(register_app, instance_url, redirect_uri)
    except Exception as exc:
        raise HTTPException(
            status_code=502, detail=f"Could not register with Mastodon instance: {exc}"
        ) from exc
    state = secrets.token_urlsafe(24)
    _pending[state] = {
        "instance_url": instance_url,
        "client_id": app_creds["client_id"],
        "client_secret": app_creds["client_secret"],
        "user_id": session.user_id,
    }
    authorize_url = build_authorize_url(
        instance_url=instance_url,
        client_id=app_creds["client_id"],
        redirect_uri=redirect_uri + f"?state={state}",
    )
    return {"authorize_url": authorize_url, "state": state}
@router.get("/callback")
 async def mastodon_callback(code: str | None = None, state: str | None = None):
    """OAuth callback. Exchanges auth code for access token and stores it."""
    if not code or not state:
        raise HTTPException(status_code=400, detail="Missing code or state parameter.")
    pending = _pending.pop(state, None)
    if pending is None:
        raise HTTPException(status_code=400, detail="Unknown or expired OAuth state.")
    from app.services.ap.mastodon import exchange_code, store_token
    redirect_uri = _redirect_uri() + f"?state={state}"
    try:
        access_token = await asyncio.to_thread(
            exchange_code,
            pending["instance_url"],
            pending["client_id"],
            pending["client_secret"],
            code,
            redirect_uri,
        )
    except Exception as exc:
        raise HTTPException(status_code=502, detail=f"Token exchange failed: {exc}") from exc
    await asyncio.to_thread(
        store_token,
        settings.DB_PATH,
        pending["user_id"],
        pending["instance_url"],
        access_token,
        settings.AP_TOKEN_ENCRYPTION_KEY,
    )
    # Redirect to frontend settings page after successful connect
    return RedirectResponse(url="/#/settings?mastodon=connected", status_code=302)
@router.delete("/disconnect", status_code=204)
 async def disconnect_mastodon(session: CloudUser = Depends(get_session)):
    """Remove the stored Mastodon token."""
    from app.services.ap.mastodon import delete_token
    await asyncio.to_thread(delete_token, settings.DB_PATH, session.user_id)
@router.get("/status")
 async def mastodon_status(session: CloudUser = Depends(get_session)):
    """Return connection status and instance URL (no token value)."""
    from app.services.ap.mastodon import get_token
    result = await asyncio.to_thread(
        get_token,
        settings.DB_PATH,
        session.user_id,
        settings.AP_TOKEN_ENCRYPTION_KEY,
    )
    if result is None:
        return {"connected": False, "instance_url": None}
    instance_url, _ = result
    return {"connected": True, "instance_url": instance_url}
--- a/app/api/endpoints/recipe_scan.py
+++ b/app/api/endpoints/recipe_scan.py
@ -1,371 +0,0 @@
 """Recipe scanner endpoints (kiwi#9).
 POST /recipes/scan       -- scan photo(s) -> structured recipe JSON (not saved)
 POST /recipes/scan/save  -- save a confirmed scanned recipe to user_recipes
 GET  /recipes/user       -- list user-created recipes
 GET  /recipes/user/{id}  -- get a single user recipe
 DELETE /recipes/user/{id} -- delete a user recipe
 BSL 1.1 -- recipe_scan requires Paid tier or BYOK.
 """
 from __future__ import annotations
 import asyncio
 import json as _json
 import logging
 import uuid
 from pathlib import Path
 from typing import Annotated
 import aiofiles
 from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
 from fastapi.responses import JSONResponse, StreamingResponse
 from app.cloud_session import CloudUser, get_session
 from app.core.config import settings
 from app.db.session import get_store
 from app.db.store import Store
 from app.models.schemas.recipe_scan import (
    ScannedIngredientSchema,
    ScannedRecipeResponse,
    ScannedRecipeSaveRequest,
    UserRecipeResponse,
 )
 from app.tiers import can_use
 logger = logging.getLogger(__name__)
 router = APIRouter()
 _ALLOWED_MIME_TYPES = {
    "image/jpeg", "image/jpg", "image/png", "image/webp", "image/heic", "image/heif"
 }
 _MAX_FILE_SIZE_MB = 20
 async def _save_upload_temp(file: UploadFile) -> Path:
    """Write upload to a temp path under UPLOAD_DIR. Caller is responsible for cleanup."""
    settings.ensure_dirs()
    dest = settings.UPLOAD_DIR / f"scan_{uuid.uuid4()}_{file.filename}"
    async with aiofiles.open(dest, "wb") as f:
        await f.write(await file.read())
    return dest
 def _result_to_response(result) -> ScannedRecipeResponse:
    """Convert ScannedRecipeResult (dataclass) to Pydantic response schema."""
    return ScannedRecipeResponse(
        title=result.title,
        subtitle=result.subtitle,
        servings=result.servings,
        cook_time=result.cook_time,
        source_note=result.source_note,
        ingredients=[
            ScannedIngredientSchema(
                name=i.name,
                qty=i.qty,
                unit=i.unit,
                raw=i.raw,
                in_pantry=i.in_pantry,
            )
            for i in result.ingredients
        ],
        steps=result.steps,
        notes=result.notes,
        tags=result.tags,
        pantry_match_pct=result.pantry_match_pct,
        confidence=result.confidence,
        warnings=result.warnings,
    )
 def _row_to_user_recipe(row: dict) -> UserRecipeResponse:
    """Convert a store row dict to UserRecipeResponse."""
    return UserRecipeResponse(
        id=row["id"],
        title=row["title"],
        subtitle=row.get("subtitle"),
        servings=row.get("servings"),
        cook_time=row.get("cook_time"),
        source_note=row.get("source_note"),
        ingredients=[
            ScannedIngredientSchema(**i) if isinstance(i, dict) else i
            for i in (row.get("ingredients") or [])
        ],
        steps=row.get("steps") or [],
        notes=row.get("notes"),
        tags=row.get("tags") or [],
        source=row.get("source", "manual"),
        pantry_match_pct=row.get("pantry_match_pct"),
        created_at=row["created_at"],
    )
 # ── Scan endpoint ──────────────────────────────────────────────────────────────
@router.post("/scan", response_model=ScannedRecipeResponse)
 async def scan_recipe(
    files: Annotated[list[UploadFile], File(...)],
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Scan one or more recipe photos and return a structured recipe for review.
    Accepts 1-4 images. Multi-page recipes (e.g. ingredients on page 1,
    directions on page 2) work best when all pages are submitted together.
    The response is NOT saved automatically -- the user reviews and edits it,
    then calls POST /recipes/scan/save to persist.
    Tier: Paid (or BYOK).
    """
    if not can_use("recipe_scan", session.tier, session.has_byok):
        raise HTTPException(
            status_code=403,
            detail=(
                "Recipe scanning requires Paid tier or a configured vision backend (BYOK). "
                "Set ANTHROPIC_API_KEY or connect to a cf-orch vision service."
            ),
        )
    if not files:
        raise HTTPException(status_code=422, detail="At least one image file is required.")
    if len(files) > 4:
        raise HTTPException(status_code=422, detail="Maximum 4 images per scan request.")
    for f in files:
        ct = (f.content_type or "").lower()
        if ct and ct not in _ALLOWED_MIME_TYPES:
            raise HTTPException(
                status_code=422,
                detail=f"Unsupported file type: {ct}. Supported: JPEG, PNG, WebP, HEIC.",
            )
    # Save uploads to temp files
    saved_paths: list[Path] = []
    try:
        for f in files:
            saved_paths.append(await _save_upload_temp(f))
        # Get pantry item names for cross-reference
        inventory = await asyncio.to_thread(store.list_inventory)
        pantry_names = [item["product_name"] for item in inventory if item.get("product_name")]
        # Run scanner (blocks on VLM -- use to_thread)
        from app.services.recipe.recipe_scanner import RecipeScanner
        def _run_scan():
            scanner = RecipeScanner()
            return scanner.scan(saved_paths, pantry_names=pantry_names)
        try:
            result = await asyncio.to_thread(_run_scan)
        except ValueError as exc:
            msg = str(exc)
            if "not_a_recipe" in msg:
                raise HTTPException(
                    status_code=422,
                    detail="The image does not appear to contain a recipe. "
                           "Please photograph a recipe card, cookbook page, or handwritten note.",
                )
            raise HTTPException(status_code=422, detail=msg)
        except RuntimeError as exc:
            msg = str(exc)
            logger.warning("Recipe scanner unavailable: %s", msg)
            raise HTTPException(
                status_code=503,
                detail=(
                    "The recipe scanner is temporarily unavailable — "
                    "no vision backend could be reached. "
                    "Try again in a few minutes, or contact support if this persists."
                ),
            )
        return _result_to_response(result)
    finally:
        # Clean up temp files
        for p in saved_paths:
            try:
                p.unlink(missing_ok=True)
            except Exception:
                pass
 # ── SSE scan endpoint ─────────────────────────────────────────────────────────
 async def _scan_recipe_sse(saved_paths: list[Path], pantry_names: list[str]):
    """Async generator yielding SSE events for a recipe scan.
    Emits progress events while the vision service allocates and runs, then a
    final "done" event containing the full recipe payload (same shape as the
    ScannedRecipeResponse from POST /scan).
    Events:
      {"status": "allocating", "message": "..."}
      {"status": "scanning",   "message": "..."}
      {"status": "structuring","message": "..."}
      {"status": "done",       "recipe": {...}}
      {"status": "error",      "message": "..."}
    """
    queue: asyncio.Queue = asyncio.Queue()
    loop = asyncio.get_running_loop()
    def _run() -> None:
        def cb(status: str, message: str) -> None:
            loop.call_soon_threadsafe(queue.put_nowait, {"status": status, "message": message})
        try:
            from app.services.recipe.recipe_scanner import RecipeScanner
            result = RecipeScanner().scan(saved_paths, pantry_names=pantry_names, progress_cb=cb)
            recipe_dict = _result_to_response(result).model_dump()
            loop.call_soon_threadsafe(queue.put_nowait, {"status": "done", "recipe": recipe_dict})
        except ValueError as exc:
            loop.call_soon_threadsafe(queue.put_nowait, {"status": "error", "message": str(exc)})
        except RuntimeError as exc:
            loop.call_soon_threadsafe(queue.put_nowait, {"status": "error", "message": str(exc)})
        except Exception as exc:
            logger.exception("Unexpected error in recipe scan thread")
            loop.call_soon_threadsafe(queue.put_nowait, {"status": "error", "message": "Scan failed unexpectedly."})
    scan_task = asyncio.ensure_future(asyncio.to_thread(_run))
    try:
        while True:
            try:
                event = await asyncio.wait_for(queue.get(), timeout=180.0)
            except asyncio.TimeoutError:
                yield f"data: {_json.dumps({'status': 'error', 'message': 'Scan timed out after 3 minutes.'})}\n\n"
                break
            yield f"data: {_json.dumps(event)}\n\n"
            if event["status"] in ("done", "error"):
                break
    finally:
        if not scan_task.done():
            scan_task.cancel()
@router.post("/scan/stream")
 async def scan_recipe_stream(
    files: Annotated[list[UploadFile], File(...)],
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Scan recipe photos and stream SSE progress events during model load.
    Use this endpoint instead of POST /scan when you need live feedback during
    cold-start model loading (first request after a GPU-idle period can take
    30-60 seconds for cf-docuvision to warm up).
    Tier: Paid (or BYOK) — same gate as POST /scan.
    """
    if not can_use("recipe_scan", session.tier, session.has_byok):
        raise HTTPException(
            status_code=403,
            detail=(
                "Recipe scanning requires Paid tier or a configured vision backend (BYOK). "
                "Set ANTHROPIC_API_KEY or connect to a cf-orch vision service."
            ),
        )
    if not files:
        raise HTTPException(status_code=422, detail="At least one image file is required.")
    if len(files) > 4:
        raise HTTPException(status_code=422, detail="Maximum 4 images per scan request.")
    for f in files:
        ct = (f.content_type or "").lower()
        if ct and ct not in _ALLOWED_MIME_TYPES:
            raise HTTPException(
                status_code=422,
                detail=f"Unsupported file type: {ct}. Supported: JPEG, PNG, WebP, HEIC.",
            )
    saved_paths: list[Path] = []
    for f in files:
        saved_paths.append(await _save_upload_temp(f))
    inventory = await asyncio.to_thread(store.list_inventory)
    pantry_names = [item["product_name"] for item in inventory if item.get("product_name")]
    async def generate():
        try:
            async for chunk in _scan_recipe_sse(saved_paths, pantry_names):
                yield chunk
        finally:
            for p in saved_paths:
                try:
                    p.unlink(missing_ok=True)
                except Exception:
                    pass
    return StreamingResponse(generate(), media_type="text/event-stream")
 # ── Save endpoint ──────────────────────────────────────────────────────────────
@router.post("/scan/save", response_model=UserRecipeResponse, status_code=201)
 async def save_scanned_recipe(
    body: ScannedRecipeSaveRequest,
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Save a user-reviewed (possibly edited) scanned recipe.
    The body is the ScannedRecipeResponse (or a user-edited version of it).
    Returns the persisted UserRecipe with an assigned ID.
    Tier: Free (saving your own recipe doesn't require vision access).
    """
    def _save():
        return store.create_user_recipe(
            title=body.title,
            subtitle=body.subtitle,
            servings=body.servings,
            cook_time=body.cook_time,
            source_note=body.source_note,
            ingredients=[i.model_dump() for i in body.ingredients],
            steps=body.steps,
            notes=body.notes,
            tags=body.tags,
            source=body.source,
            pantry_match_pct=None,
        )
    row = await asyncio.to_thread(_save)
    return _row_to_user_recipe(row)
 # ── User recipe list / get / delete ───────────────────────────────────────────
@router.get("/user", response_model=list[UserRecipeResponse])
 async def list_user_recipes(
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """List all user-created recipes (scanned + manually entered), newest first."""
    rows = await asyncio.to_thread(store.list_user_recipes)
    return [_row_to_user_recipe(r) for r in rows]
@router.get("/user/{recipe_id}", response_model=UserRecipeResponse)
 async def get_user_recipe(
    recipe_id: int,
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Get a single user recipe by ID."""
    row = await asyncio.to_thread(store.get_user_recipe, recipe_id)
    if not row:
        raise HTTPException(status_code=404, detail="User recipe not found.")
    return _row_to_user_recipe(row)
@router.delete("/user/{recipe_id}", status_code=204)
 async def delete_user_recipe(
    recipe_id: int,
    store: Store = Depends(get_store),
    session: CloudUser = Depends(get_session),
 ):
    """Delete a user recipe by ID."""
    deleted = await asyncio.to_thread(store.delete_user_recipe, recipe_id)
    if not deleted:
        raise HTTPException(status_code=404, detail="User recipe not found.")
    return JSONResponse(status_code=204, content=None)
--- a/app/api/endpoints/recipe_tags.py
+++ b/app/api/endpoints/recipe_tags.py
@ -1,166 +0,0 @@
 # app/api/endpoints/recipe_tags.py
 """Community subcategory tagging for corpus recipes.
 Users can tag a recipe they're viewing with a domain/category/subcategory
 from the browse taxonomy. Tags require a community pseudonym and reach
 public visibility once two independent users have tagged the same recipe
 to the same location (upvotes >= 2).
 All tiers may submit and upvote tags — community contribution is free.
 """
 from __future__ import annotations
 import logging
 from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel
 from app.api.endpoints.community import _get_community_store
 from app.api.endpoints.session import get_session
 from app.cloud_session import CloudUser
 from app.services.recipe.browser_domains import DOMAINS
 logger = logging.getLogger(__name__)
 router = APIRouter()
 ACCEPT_THRESHOLD = 2
 # ── Request / response models ──────────────────────────────────────────────────
 class TagSubmitBody(BaseModel):
    recipe_id: int
    domain: str
    category: str
    subcategory: str | None = None
    pseudonym: str
 class TagResponse(BaseModel):
    id: int
    recipe_id: int
    domain: str
    category: str
    subcategory: str | None
    pseudonym: str
    upvotes: int
    accepted: bool
 def _to_response(row: dict) -> TagResponse:
    return TagResponse(
        id=row["id"],
        recipe_id=int(row["recipe_ref"]),
        domain=row["domain"],
        category=row["category"],
        subcategory=row.get("subcategory"),
        pseudonym=row["pseudonym"],
        upvotes=row["upvotes"],
        accepted=row["upvotes"] >= ACCEPT_THRESHOLD,
    )
 def _validate_location(domain: str, category: str, subcategory: str | None) -> None:
    """Raise 422 if (domain, category, subcategory) isn't in the known taxonomy."""
    if domain not in DOMAINS:
        raise HTTPException(status_code=422, detail=f"Unknown domain '{domain}'.")
    cats = DOMAINS[domain].get("categories", {})
    if category not in cats:
        raise HTTPException(
            status_code=422,
            detail=f"Unknown category '{category}' in domain '{domain}'.",
        )
    if subcategory is not None:
        subcats = cats[category].get("subcategories", {})
        if subcategory not in subcats:
            raise HTTPException(
                status_code=422,
                detail=f"Unknown subcategory '{subcategory}' in '{domain}/{category}'.",
            )
 # ── Endpoints ──────────────────────────────────────────────────────────────────
@router.get("/recipes/community-tags/{recipe_id}", response_model=list[TagResponse])
 async def list_recipe_tags(
    recipe_id: int,
    session: CloudUser = Depends(get_session),
 ) -> list[TagResponse]:
    """Return all community tags for a corpus recipe, accepted ones first."""
    store = _get_community_store()
    if store is None:
        return []
    tags = store.list_tags_for_recipe(recipe_id)
    return [_to_response(r) for r in tags]
@router.post("/recipes/community-tags", response_model=TagResponse, status_code=201)
 async def submit_recipe_tag(
    body: TagSubmitBody,
    session: CloudUser = Depends(get_session),
 ) -> TagResponse:
    """Tag a corpus recipe with a browse taxonomy location.
    Requires the user to have a community pseudonym set. Returns 409 if this
    user has already tagged this recipe to this exact location.
    """
    store = _get_community_store()
    if store is None:
        raise HTTPException(
            status_code=503,
            detail="Community features are not available on this instance.",
        )
    _validate_location(body.domain, body.category, body.subcategory)
    try:
        import psycopg2.errors  # type: ignore[import]
        row = store.submit_recipe_tag(
            recipe_id=body.recipe_id,
            domain=body.domain,
            category=body.category,
            subcategory=body.subcategory,
            pseudonym=body.pseudonym,
        )
        return _to_response(row)
    except Exception as exc:
        if "unique" in str(exc).lower() or "UniqueViolation" in type(exc).__name__:
            raise HTTPException(
                status_code=409,
                detail="You have already tagged this recipe to this location.",
            )
        logger.error("submit_recipe_tag failed: %s", exc)
        raise HTTPException(status_code=500, detail="Failed to submit tag.")
@router.post("/recipes/community-tags/{tag_id}/upvote", response_model=TagResponse)
 async def upvote_recipe_tag(
    tag_id: int,
    pseudonym: str,
    session: CloudUser = Depends(get_session),
 ) -> TagResponse:
    """Upvote an existing community tag.
    Returns 409 if this pseudonym has already voted on this tag.
    Returns 404 if the tag doesn't exist.
    """
    store = _get_community_store()
    if store is None:
        raise HTTPException(status_code=503, detail="Community features unavailable.")
    tag_row = store.get_recipe_tag_by_id(tag_id)
    if tag_row is None:
        raise HTTPException(status_code=404, detail=f"Tag {tag_id} not found.")
    try:
        new_upvotes = store.upvote_recipe_tag(tag_id, pseudonym)
    except ValueError:
        raise HTTPException(status_code=404, detail=f"Tag {tag_id} not found.")
    except Exception as exc:
        if "unique" in str(exc).lower() or "UniqueViolation" in type(exc).__name__:
            raise HTTPException(status_code=409, detail="You have already voted on this tag.")
        logger.error("upvote_recipe_tag failed: %s", exc)
        raise HTTPException(status_code=500, detail="Failed to upvote tag.")
    tag_row["upvotes"] = new_upvotes
    return _to_response(tag_row)
--- a/app/api/endpoints/recipes.py
+++ b/app/api/endpoints/recipes.py
@ -6,9 +6,7 @@ import logging
 from pathlib import Path
 from typing import Annotated
 import json as _json_mod
 from fastapi import APIRouter, Depends, HTTPException, Query
 from fastapi.responses import StreamingResponse
 from app.cloud_session import CloudUser, _auth_label, get_session
@ -16,22 +14,13 @@ log = logging.getLogger(__name__)
 from app.db.session import get_store
 from app.db.store import Store
 from app.models.schemas.recipe import (
    AskRequest,
    AskResponse,
    AskRecipeHit,
    AssemblyTemplateOut,
    BuildRequest,
    LeftoversResponse,
    RecipeJobStatus,
    RecipeRequest,
    RecipeResult,
    RecipeSuggestion,
    RoleCandidatesResponse,
    StreamTokenRequest,
    StreamTokenResponse,
 )
 from app.services.coordinator_proxy import CoordinatorError, coordinator_authorize
 from app.api.endpoints.imitate import _build_recipe_prompt
 from app.services.recipe.assembly_recipes import (
    build_from_selection,
    get_role_candidates,
@ -39,16 +28,11 @@ from app.services.recipe.assembly_recipes import (
 )
 from app.services.recipe.browser_domains import (
    DOMAINS,
    category_has_subcategories,
    get_category_names,
    get_domain_labels,
    get_keywords_for_category,
    get_keywords_for_subcategory,
    get_subcategory_names,
 )
 from app.services.recipe.recipe_engine import RecipeEngine
 from app.services.recipe.time_effort import parse_time_effort
 from app.services.recipe.sensory import build_sensory_exclude
 from app.services.heimdall_orch import check_orch_budget
 from app.tiers import can_use
@ -70,122 +54,12 @@ def _suggest_in_thread(db_path: Path, req: RecipeRequest) -> RecipeResult:
        store.close()
-def _build_stream_prompt(db_path: Path, level: int) -> str:
+@router.post("/suggest", response_model=RecipeResult)
    """Fetch pantry + user settings from DB and build the recipe prompt.
    Runs in a thread (called via asyncio.to_thread) so it can use sync Store.
    """
    import datetime
    store = Store(db_path)
    try:
        items = store.list_inventory(status="available")
        pantry_names = [i["product_name"] for i in items if i.get("product_name")]
        today = datetime.date.today()
        expiring_names = [
            i["product_name"]
            for i in items
            if i.get("product_name")
            and i.get("expiry_date")
            and (datetime.date.fromisoformat(i["expiry_date"]) - today).days <= 3
        ]
        settings: dict = {}
        try:
            rows = store.conn.execute("SELECT key, value FROM user_settings").fetchall()
            settings = {r["key"]: r["value"] for r in rows}
        except Exception:
            pass
        constraints_raw = settings.get("dietary_constraints", "")
        constraints = [c.strip() for c in constraints_raw.split(",") if c.strip()] if constraints_raw else []
        allergies_raw = settings.get("allergies", "")
        allergies = [a.strip() for a in allergies_raw.split(",") if a.strip()] if allergies_raw else []
        return _build_recipe_prompt(pantry_names, expiring_names, constraints, allergies, level)
    finally:
        store.close()
 async def _stream_recipe_sse(db_path: Path, req: RecipeRequest):
    """Async generator that yields SSE events for a streaming recipe request.
    Phase 1 (thread): classify pantry items using a temporary Store.
    Phase 2 (async):  stream tokens from LLM via LLMRecipeGenerator.stream_generate().
    """
    def _prep(db_path: Path) -> tuple[list, list[str]]:
        from app.services.recipe.element_classifier import IngredientClassifier
        store = Store(db_path)
        try:
            classifier = IngredientClassifier(store)
            profiles = classifier.classify_batch(req.pantry_items)
            gaps = classifier.identify_gaps(profiles)
            return profiles, gaps
        finally:
            store.close()
    try:
        profiles, gaps = await asyncio.to_thread(_prep, db_path)
    except Exception as exc:
        yield f"data: {_json_mod.dumps({'error': str(exc)})}\n\n"
        return
    from app.services.recipe.llm_recipe import LLMRecipeGenerator
    gen = LLMRecipeGenerator(None)
    try:
        async for token in gen.stream_generate(req, profiles, gaps):
            yield f"data: {_json_mod.dumps({'chunk': token})}\n\n"
        yield f"data: {_json_mod.dumps({'done': True})}\n\n"
    except Exception as exc:
        yield f"data: {_json_mod.dumps({'error': str(exc)})}\n\n"
 async def _enqueue_recipe_job(session: CloudUser, req: RecipeRequest):
    """Queue an async recipe_llm job and return 202 with job_id.
    Falls back to synchronous generation in CLOUD_MODE (scheduler polls only
    the shared settings DB, not per-user DBs — see snipe#45 / kiwi backlog).
    """
    import json
    import uuid
    from fastapi.responses import JSONResponse
    from app.cloud_session import CLOUD_MODE
    from app.tasks.runner import insert_task
    if CLOUD_MODE:
        log.warning("recipe_llm async jobs not supported in CLOUD_MODE — falling back to sync")
        result = await asyncio.to_thread(_suggest_in_thread, session.db, req)
        return result
    job_id = f"rec_{uuid.uuid4().hex}"
    def _create(db_path: Path) -> int:
        store = Store(db_path)
        try:
            row = store.create_recipe_job(job_id, session.user_id, req.model_dump_json())
            return row["id"]
        finally:
            store.close()
    int_id = await asyncio.to_thread(_create, session.db)
    params_json = json.dumps({"job_id": job_id})
    task_id, is_new = insert_task(session.db, "recipe_llm", int_id, params=params_json)
    if is_new:
        from app.tasks.scheduler import get_scheduler
        get_scheduler(session.db).enqueue(task_id, "recipe_llm", int_id, params_json)
    return JSONResponse(content={"job_id": job_id, "status": "queued"}, status_code=202)
@router.post("/suggest")
 async def suggest_recipes(
    req: RecipeRequest,
    async_mode: bool = Query(default=False, alias="async"),
    stream: bool = Query(default=False),
    session: CloudUser = Depends(get_session),
    store: Store = Depends(get_store),
-):
+) -> RecipeResult:
    log.info("recipes auth=%s tier=%s level=%s", _auth_label(session.user_id), session.tier, req.level)
    # Inject session-authoritative tier/byok immediately — client-supplied values are ignored.
    # Also read stored unit_system preference; default to metric if not set.
@ -218,92 +92,12 @@ async def suggest_recipes(
            req = req.model_copy(update={"level": 2})
            orch_fallback = True
    if stream and req.level in (3, 4):
        return StreamingResponse(
            _stream_recipe_sse(session.db, req),
            media_type="text/event-stream",
            headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
        )
    if req.level in (3, 4) and async_mode:
        return await _enqueue_recipe_job(session, req)
    result = await asyncio.to_thread(_suggest_in_thread, session.db, req)
    if orch_fallback:
        result = result.model_copy(update={"orch_fallback": True})
    return result
@router.post("/stream-token", response_model=StreamTokenResponse)
 async def get_stream_token(
    req: StreamTokenRequest,
    session: CloudUser = Depends(get_session),
 ) -> StreamTokenResponse:
    """Issue a one-time stream token for LLM recipe generation.
    Tier-gated (Paid or BYOK). Builds the prompt from pantry + user settings,
    then calls the cf-orch coordinator to obtain a stream URL. Returns
    immediately — the frontend opens EventSource to the stream URL directly.
    """
    if not can_use("recipe_suggestions", session.tier, session.has_byok):
        raise HTTPException(
            status_code=403,
            detail="Streaming recipe generation requires Paid tier or a configured LLM backend.",
        )
    if req.level == 4 and not req.wildcard_confirmed:
        raise HTTPException(
            status_code=400,
            detail="Level 4 (Wildcard) streaming requires wildcard_confirmed=true.",
        )
    prompt = await asyncio.to_thread(_build_stream_prompt, session.db, req.level)
    try:
        result = await coordinator_authorize(prompt=prompt, caller="kiwi-recipe", ttl_s=300)
    except CoordinatorError as exc:
        raise HTTPException(status_code=exc.status_code, detail=str(exc))
    return StreamTokenResponse(
        stream_url=result.stream_url,
        token=result.token,
        expires_in_s=result.expires_in_s,
    )
@router.get("/jobs/{job_id}", response_model=RecipeJobStatus)
 async def get_recipe_job_status(
    job_id: str,
    session: CloudUser = Depends(get_session),
 ) -> RecipeJobStatus:
    """Poll the status of an async recipe generation job.
    Returns 404 when job_id is unknown or belongs to a different user.
    On status='done' with suggestions=[], the LLM returned empty — client
    should show a 'no recipe generated, try again' message.
    """
    def _get(db_path: Path) -> dict | None:
        store = Store(db_path)
        try:
            return store.get_recipe_job(job_id, session.user_id)
        finally:
            store.close()
    row = await asyncio.to_thread(_get, session.db)
    if row is None:
        raise HTTPException(status_code=404, detail="Job not found.")
    result = None
    if row["status"] == "done" and row["result"]:
        result = RecipeResult.model_validate_json(row["result"])
    return RecipeJobStatus(
        job_id=row["job_id"],
        status=row["status"],
        result=result,
        error=row["error"],
    )
@router.get("/browse/domains")
 async def list_browse_domains(
    session: CloudUser = Depends(get_session),
@ -321,42 +115,15 @@ async def list_browse_categories(
    if domain not in DOMAINS:
        raise HTTPException(status_code=404, detail=f"Unknown domain '{domain}'.")
-    cat_names = get_category_names(domain)
+    keywords_by_category = {
-    keywords_by_category = {cat: get_keywords_for_category(domain, cat) for cat in cat_names}
+        cat: get_keywords_for_category(domain, cat)
-    has_subs = {cat: category_has_subcategories(domain, cat) for cat in cat_names}
+        for cat in get_category_names(domain)
    def _get(db_path: Path) -> list[dict]:
        store = Store(db_path)
        try:
            return store.get_browser_categories(domain, keywords_by_category, has_subs)
        finally:
            store.close()
    return await asyncio.to_thread(_get, session.db)
@router.get("/browse/{domain}/{category}/subcategories")
 async def list_browse_subcategories(
    domain: str,
    category: str,
    session: CloudUser = Depends(get_session),
 ) -> list[dict]:
    """Return [{subcategory, recipe_count}] for a category that supports subcategories."""
    if domain not in DOMAINS:
        raise HTTPException(status_code=404, detail=f"Unknown domain '{domain}'.")
    if not category_has_subcategories(domain, category):
        return []
    subcat_names = get_subcategory_names(domain, category)
    keywords_by_subcat = {
        sub: get_keywords_for_subcategory(domain, category, sub)
        for sub in subcat_names
    }
    def _get(db_path: Path) -> list[dict]:
        store = Store(db_path)
        try:
-            return store.get_browser_subcategories(domain, keywords_by_subcat)
+            return store.get_browser_categories(domain, keywords_by_category)
        finally:
            store.close()
@ -370,33 +137,16 @@ async def browse_recipes(
    page: Annotated[int, Query(ge=1)] = 1,
    page_size: Annotated[int, Query(ge=1, le=100)] = 20,
    pantry_items: Annotated[str | None, Query()] = None,
    subcategory: Annotated[str | None, Query()] = None,
    q: Annotated[str | None, Query(max_length=200)] = None,
    sort: Annotated[str, Query(pattern="^(default|alpha|alpha_desc|match)$")] = "default",
    required_ingredient: Annotated[str | None, Query(max_length=100)] = None,
    session: CloudUser = Depends(get_session),
 ) -> dict:
    """Return a paginated list of recipes for a domain/category.
-    Pass pantry_items as a comma-separated string to receive match_pct badges.
+    Pass pantry_items as a comma-separated string to receive match_pct
-    Pass subcategory to narrow within a category that has subcategories.
+    badges on each result.
    Pass q to filter by title substring. Pass sort for ordering (default/alpha/alpha_desc/match).
    sort=match orders by pantry coverage DESC; falls back to default when no pantry_items.
    Pass required_ingredient to restrict results to recipes that must include that ingredient.
    """
    if domain not in DOMAINS:
        raise HTTPException(status_code=404, detail=f"Unknown domain '{domain}'.")
    if category == "_all":
        keywords = None  # unfiltered browse
    elif subcategory:
        keywords = get_keywords_for_subcategory(domain, category, subcategory)
        if not keywords:
            raise HTTPException(
                status_code=404,
                detail=f"Unknown subcategory '{subcategory}' in '{category}'.",
            )
    else:
    keywords = get_keywords_for_category(domain, category)
    if not keywords:
        raise HTTPException(
@ -413,90 +163,12 @@ async def browse_recipes(
    def _browse(db_path: Path) -> dict:
        store = Store(db_path)
        try:
            # Load sensory preferences
            sensory_prefs_json = store.get_setting("sensory_preferences")
            sensory_exclude = build_sensory_exclude(sensory_prefs_json)
            result = store.browse_recipes(
                keywords=keywords,
                page=page,
                page_size=page_size,
                pantry_items=pantry_list,
                q=q or None,
                sort=sort,
                sensory_exclude=sensory_exclude,
                required_ingredient=required_ingredient or None,
            )
            # ── Attach time/effort signals to each browse result ────────────────
            import json as _json
            for recipe_row in result.get("recipes", []):
                directions_raw = recipe_row.get("directions") or []
                if isinstance(directions_raw, str):
                    try:
                        directions_raw = _json.loads(directions_raw)
                    except Exception:
                        directions_raw = []
                if directions_raw:
                    _profile = parse_time_effort(
                        directions_raw,
                        ingredients=recipe_row.get("ingredients") or [],
                        ingredient_names=recipe_row.get("ingredient_names") or [],
                    )
                    recipe_row["active_min"] = _profile.active_min
                    recipe_row["passive_min"] = _profile.passive_min
                else:
                    recipe_row["active_min"] = None
                    recipe_row["passive_min"] = None
                # Remove directions from browse payload — not needed by the card UI
                recipe_row.pop("directions", None)
            # Community tag fallback: if FTS returned nothing for a subcategory,
            # check whether accepted community tags exist for this location and
            # fetch those corpus recipes directly by ID.
            if result["total"] == 0 and subcategory and keywords:
                try:
                    from app.api.endpoints.community import _get_community_store
                    cs = _get_community_store()
                    if cs is not None:
                        community_ids = cs.get_accepted_recipe_ids_for_subcategory(
                            domain=domain,
                            category=category,
                            subcategory=subcategory,
                        )
                        if community_ids:
                            offset = (page - 1) * page_size
                            paged_ids = community_ids[offset: offset + page_size]
                            recipes = store.fetch_recipes_by_ids(paged_ids, pantry_list)
                            import json as _json_c
                            for recipe_row in recipes:
                                directions_raw = recipe_row.get("directions") or []
                                if isinstance(directions_raw, str):
                                    try:
                                        directions_raw = _json_c.loads(directions_raw)
                                    except Exception:
                                        directions_raw = []
                                if directions_raw:
                                    _profile = parse_time_effort(
                                        directions_raw,
                                        ingredients=recipe_row.get("ingredients") or [],
                                        ingredient_names=recipe_row.get("ingredient_names") or [],
                                    )
                                    recipe_row["active_min"] = _profile.active_min
                                    recipe_row["passive_min"] = _profile.passive_min
                                else:
                                    recipe_row["active_min"] = None
                                    recipe_row["passive_min"] = None
                                recipe_row.pop("directions", None)
                            result = {
                                "recipes": recipes,
                                "total": len(community_ids),
                                "page": page,
                                "community_tagged": True,
                            }
                except Exception as exc:
                    logger.warning("community tag fallback failed: %s", exc)
            store.log_browser_telemetry(
                domain=domain,
                category=category,
@ -600,137 +272,6 @@ async def build_recipe(
    return result
 _ASK_STOPWORDS: frozenset[str] = frozenset({
    "what", "can", "make", "with", "have", "some", "the", "and", "for",
    "that", "this", "these", "those", "how", "about", "are", "there",
    "give", "show", "find", "want", "need", "like", "any", "good",
    "quick", "easy", "simple", "fast", "using", "use", "from", "into",
    "more", "much", "just", "only", "my", "please", "could", "would",
    "should", "something", "anything", "everything", "ideas", "idea",
    "suggest", "meal", "food", "dish", "dishes", "today", "tonight",
    "tomorrow", "now", "here", "there", "recipes", "recipe", "dinner",
    "lunch", "breakfast", "snack", "under", "minutes", "hours", "time",
    "left", "over", "also", "some", "make", "cook", "made", "cooked",
 })
 import re as _re
 def _extract_ask_keywords(question: str) -> list[str]:
    """Extract food-relevant keywords from a natural language question."""
    tokens = _re.findall(r"[a-zA-Z]+", question.lower())
    return [t for t in tokens if len(t) > 3 and t not in _ASK_STOPWORDS]
 def _ask_in_thread(db_path: Path, question: str, pantry_items: list[str]) -> AskResponse:
    """Run Ask logic in a worker thread.
    Free tier: keyword extraction + FTS ingredient search.
    Paid tier path: same search, then LLM synthesis over results.
    The caller handles tier gating and LLM synthesis outside this thread
    to avoid importing LLMRouter in a sync context.
    """
    import json as _json
    store = Store(db_path)
    try:
        keywords = _extract_ask_keywords(question)
        ingredient_hits: list[dict] = []
        if keywords:
            ingredient_hits = store.search_recipes_by_ingredients(keywords, limit=15)
        # Also search by title using the full question text as a substring hint.
        # browse_recipes q= does title LIKE %q%. Extract the longest keyword
        # from the question as the title probe (most likely to appear in a title).
        title_hits: list[dict] = []
        title_probe = max(keywords, key=len) if keywords else None
        if title_probe:
            browse_result = store.browse_recipes(
                keywords=None,
                page=1,
                page_size=12,
                pantry_items=pantry_items or None,
                q=title_probe,
                sort="match" if pantry_items else "default",
            )
            title_hits = browse_result.get("recipes", [])
        # Merge by ID; ingredient hits come first (more semantically relevant).
        seen: set[int] = set()
        merged: list[dict] = []
        for row in ingredient_hits + title_hits:
            rid = row.get("id")
            if rid is not None and rid not in seen:
                seen.add(rid)
                merged.append(row)
        # Compute pantry match_pct if caller sent pantry items.
        pantry_set = {p.lower() for p in pantry_items} if pantry_items else set()
        hits: list[AskRecipeHit] = []
        for row in merged[:12]:
            match_pct: float | None = None
            if pantry_set:
                raw_names = row.get("ingredient_names") or []
                if isinstance(raw_names, str):
                    try:
                        raw_names = _json.loads(raw_names)
                    except Exception:
                        raw_names = []
                if raw_names:
                    covered = sum(
                        1 for n in raw_names
                        if any(p in n.lower() for p in pantry_set)
                    )
                    match_pct = round(covered / len(raw_names), 2)
            hits.append(AskRecipeHit(
                id=row["id"],
                title=row.get("title", ""),
                category=row.get("category"),
                match_pct=match_pct,
            ))
        return AskResponse(answer=None, recipes=hits, tier="free")
    finally:
        store.close()
@router.post("/ask", response_model=AskResponse)
 async def ask_recipes(
    req: AskRequest,
    session: CloudUser = Depends(get_session),
 ) -> AskResponse:
    """Natural-language recipe search with optional LLM synthesis.
    Free tier: keyword extraction from question → FTS ingredient + title search.
    Paid tier / BYOK: same search, then LLM synthesizes a short conversational answer.
    """
    result = await asyncio.to_thread(_ask_in_thread, session.db, req.question, req.pantry_items)
    # LLM synthesis: only for paid/premium/ultra tiers, not "local" dev tier.
    # Wrapped in wait_for so an unresponsive model degrades gracefully to recipe list only.
    paid_tier = session.tier in ("paid", "premium", "ultra")
    if (paid_tier or session.has_byok) and result.recipes:
        recipe_titles = ", ".join(r.title for r in result.recipes[:6])
        prompt = (
            f'You are a helpful kitchen assistant. The user asked: "{req.question}"\n\n'
            f"Matching recipes: {recipe_titles}\n\n"
            f"Write a brief, friendly 1–2 sentence response suggesting which of these "
            f"recipes might best fit the question. Be specific and natural."
        )
        try:
            from circuitforge_core.llm.router import LLMRouter
            answer = await asyncio.wait_for(
                asyncio.to_thread(LLMRouter().complete, prompt),
                timeout=8.0,
            )
            result = result.model_copy(update={"answer": answer.strip() or None, "tier": "paid"})
        except (Exception, asyncio.TimeoutError) as exc:
            log.warning("Ask LLM synthesis skipped: %s", exc)
    return result
@router.get("/{recipe_id}")
 async def get_recipe(recipe_id: int, session: CloudUser = Depends(get_session)) -> dict:
    def _get(db_path: Path, rid: int) -> dict | None:
@ -743,111 +284,4 @@ async def get_recipe(recipe_id: int, session: CloudUser = Depends(get_session))
    recipe = await asyncio.to_thread(_get, session.db, recipe_id)
    if not recipe:
        raise HTTPException(status_code=404, detail="Recipe not found.")
-
+    return recipe
    # Normalize corpus record into RecipeSuggestion shape so RecipeDetailPanel
    # can render it without knowing it came from a direct DB lookup.
    ingredient_names = recipe.get("ingredient_names") or []
    if isinstance(ingredient_names, str):
        import json as _json
        try:
            ingredient_names = _json.loads(ingredient_names)
        except Exception:
            ingredient_names = []
    _directions_for_te = recipe.get("directions") or []
    if isinstance(_directions_for_te, str):
        import json as _json2
        try:
            _directions_for_te = _json2.loads(_directions_for_te)
        except Exception:
            _directions_for_te = []
    _ingredients_for_te = recipe.get("ingredients") or []
    if isinstance(_ingredients_for_te, str):
        import json as _json3
        try:
            _ingredients_for_te = _json3.loads(_ingredients_for_te)
        except Exception:
            _ingredients_for_te = []
    _ingredient_names_for_te = recipe.get("ingredient_names") or []
    if isinstance(_ingredient_names_for_te, str):
        import json as _json4
        try:
            _ingredient_names_for_te = _json4.loads(_ingredient_names_for_te)
        except Exception:
            _ingredient_names_for_te = []
    if _directions_for_te:
        _te = parse_time_effort(
            _directions_for_te,
            ingredients=_ingredients_for_te,
            ingredient_names=_ingredient_names_for_te,
        )
        _time_effort_out: dict | None = {
            "active_min": _te.active_min,
            "passive_min": _te.passive_min,
            "total_min": _te.total_min,
            "effort_label": _te.effort_label,
            "equipment": _te.equipment,
            "step_analyses": [
                {
                    "is_passive": sa.is_passive,
                    "detected_minutes": sa.detected_minutes,
                    "prep_min": sa.prep_min,
                }
                for sa in _te.step_analyses
            ],
        }
    else:
        _time_effort_out = None
    return {
        "id": recipe.get("id"),
        "title": recipe.get("title", ""),
        "match_count": 0,
        "matched_ingredients": ingredient_names,
        "missing_ingredients": [],
        "directions": recipe.get("directions") or [],
        "prep_notes": [],
        "swap_candidates": [],
        "element_coverage": {},
        "notes": recipe.get("notes") or "",
        "level": 1,
        "is_wildcard": False,
        "nutrition": None,
        "source_url": recipe.get("source_url") or None,
        "complexity": None,
        "estimated_time_min": None,
        "time_effort": _time_effort_out,
    }
@router.post("/{recipe_id}/leftovers", response_model=LeftoversResponse)
 async def get_leftovers_shelf_life(
    recipe_id: int,
    session: CloudUser = Depends(get_session),
 ) -> LeftoversResponse:
    """Return cooked-leftover shelf-life estimate for a recipe.
    Free tier: deterministic lookup (FDA/USDA table).
    Deterministic path always runs; no tier gate needed.
    """
    def _get(db_path: Path, rid: int) -> LeftoversResponse:
        from app.services.leftovers_predictor import predict_leftovers_from_row
        store = Store(db_path)
        try:
            recipe = store.get_recipe(rid)
        finally:
            store.close()
        if recipe is None:
            raise HTTPException(status_code=404, detail="Recipe not found.")
        result = predict_leftovers_from_row(recipe)
        return LeftoversResponse(
            fridge_days=result.fridge_days,
            freeze_days=result.freeze_days,
            freeze_by_day=result.freeze_by_day,
            storage_advice=result.storage_advice,
        )
    return await asyncio.to_thread(_get, session.db, recipe_id)
--- a/app/api/endpoints/saved_recipes.py
+++ b/app/api/endpoints/saved_recipes.py
@ -5,7 +5,6 @@ import asyncio
 from pathlib import Path
 from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel
 from app.cloud_session import CloudUser, get_session
 from app.db.store import Store
@ -17,13 +16,8 @@ from app.models.schemas.saved_recipe import (
    SaveRecipeRequest,
    UpdateSavedRecipeRequest,
 )
 from app.services.magpie_hook import fire_recipe_signal
 from app.tiers import can_use
 class StyleClassifyResponse(BaseModel):
    suggested_tags: list[str]
 router = APIRouter()
@ -41,7 +35,7 @@ def _to_summary(row: dict, store: Store) -> SavedRecipeSummary:
    return SavedRecipeSummary(
        id=row["id"],
        recipe_id=row["recipe_id"],
-        title=row.get("title") or "",
+        title=row.get("title", ""),
        saved_at=row["saved_at"],
        notes=row.get("notes"),
        rating=row.get("rating"),
@ -61,9 +55,7 @@ async def save_recipe(
        row = store.save_recipe(req.recipe_id, req.notes, req.rating)
        return _to_summary(row, store)
-    result = await asyncio.to_thread(_in_thread, session.db, _run)
+    return await asyncio.to_thread(_in_thread, session.db, _run)
    asyncio.create_task(fire_recipe_signal(session.db, req.recipe_id, req.rating, []))
    return result
@router.delete("/{recipe_id}", status_code=204)
@ -90,11 +82,7 @@ async def update_saved_recipe(
        )
        return _to_summary(row, store)
-    result = await asyncio.to_thread(_in_thread, session.db, _run)
+    return await asyncio.to_thread(_in_thread, session.db, _run)
    asyncio.create_task(
        fire_recipe_signal(session.db, recipe_id, req.rating, req.style_tags or [])
    )
    return result
@router.get("", response_model=list[SavedRecipeSummary])
@ -110,37 +98,14 @@ async def list_saved_recipes(
    return await asyncio.to_thread(_in_thread, session.db, _run)
 # ── style classifier (Paid / BYOK) ───────────────────────────────────────────
@router.post("/{recipe_id}/classify-style", response_model=StyleClassifyResponse)
 async def classify_style(
    recipe_id: int,
    session: CloudUser = Depends(get_session),
 ) -> StyleClassifyResponse:
    if not can_use("style_classifier", session.tier, getattr(session, "has_byok", False)):
        raise HTTPException(status_code=403, detail="Style classifier requires Paid tier or BYOK.")
    def _run(store: Store) -> StyleClassifyResponse:
        recipe = store.get_recipe(recipe_id)
        if recipe is None:
            raise HTTPException(status_code=404, detail="Recipe not found.")
        from app.services.recipe.style_classifier import classify_style as _classify
        tags = _classify(recipe)
        return StyleClassifyResponse(suggested_tags=tags)
    return await asyncio.to_thread(_in_thread, session.db, _run)
 # ── collections (Paid) ────────────────────────────────────────────────────────
@router.get("/collections", response_model=list[CollectionSummary])
 async def list_collections(
    session: CloudUser = Depends(get_session),
 ) -> list[CollectionSummary]:
    # Free users can list (they'll always have zero — creating requires Paid).
    # Returning 403 here breaks savedStore.load() via Promise.all for non-Paid users.
    if not can_use("recipe_collections", session.tier):
-        return []
+        raise HTTPException(status_code=403, detail="Collections require Paid tier.")
    rows = await asyncio.to_thread(
        _in_thread, session.db, lambda s: s.get_collections()
    )
--- a/app/api/endpoints/session.py
+++ b/app/api/endpoints/session.py
@ -10,7 +10,6 @@ import logging
 from fastapi import APIRouter, Depends
 from app.cloud_session import CloudUser, _auth_label, get_session
 from app.core.config import settings
 router = APIRouter()
 log = logging.getLogger(__name__)
@ -23,13 +22,8 @@ def session_bootstrap(session: CloudUser = Depends(get_session)) -> dict:
    Expected log output:
        INFO:app.api.endpoints.session: session auth=authed tier=paid
        INFO:app.api.endpoints.session: session auth=anon tier=free
    E2E test sessions (E2E_TEST_USER_ID) are logged at DEBUG so they don't
    pollute analytics counts while still being visible when DEBUG=true.
    """
-    is_test = bool(settings.E2E_TEST_USER_ID and session.user_id == settings.E2E_TEST_USER_ID)
+    log.info("session auth=%s tier=%s", _auth_label(session.user_id), session.tier)
    logger = log.debug if is_test else log.info
    logger("session auth=%s tier=%s%s", _auth_label(session.user_id), session.tier, " e2e=true" if is_test else "")
    return {
        "auth": _auth_label(session.user_id),
        "tier": session.tier,
--- a/app/api/endpoints/settings.py
+++ b/app/api/endpoints/settings.py
@ -10,7 +10,7 @@ from app.db.store import Store
 router = APIRouter()
-_ALLOWED_KEYS = frozenset({"cooking_equipment", "unit_system", "shopping_locale", "sensory_preferences", "time_first_layout"})
+_ALLOWED_KEYS = frozenset({"cooking_equipment", "unit_system"})
 class SettingBody(BaseModel):
--- a/app/api/endpoints/shopping.py
+++ b/app/api/endpoints/shopping.py
@ -57,18 +57,12 @@ def _in_thread(db_path, fn):
 # ── List ──────────────────────────────────────────────────────────────────────
 def _locale_from_store(store: Store) -> str:
    return store.get_setting("shopping_locale") or "us"
@router.get("", response_model=list[ShoppingItemResponse])
 async def list_shopping_items(
    include_checked: bool = True,
    session: CloudUser = Depends(get_session),
    store: Store = Depends(get_store),
 ):
-    locale = await asyncio.to_thread(_in_thread, session.db, _locale_from_store)
+    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok)
    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok, locale=locale)
    items = await asyncio.to_thread(
        _in_thread, session.db, lambda s: s.list_shopping_items(include_checked)
    )
@ -81,9 +75,8 @@ async def list_shopping_items(
 async def add_shopping_item(
    body: ShoppingItemCreate,
    session: CloudUser = Depends(get_session),
    store: Store = Depends(get_store),
 ):
-    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok, locale=_locale_from_store(store))
+    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok)
    item = await asyncio.to_thread(
        _in_thread,
        session.db,
@ -107,7 +100,6 @@ async def add_shopping_item(
 async def add_from_recipe(
    body: BulkAddFromRecipeRequest,
    session: CloudUser = Depends(get_session),
    store: Store = Depends(get_store),
 ):
    """Add missing ingredients from a recipe to the shopping list.
@ -140,7 +132,7 @@ async def add_from_recipe(
            added.append(item)
        return added
-    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok, locale=_locale_from_store(store))
+    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok)
    items = await asyncio.to_thread(_in_thread, session.db, _run)
    return [_enrich(i, builder) for i in items]
@ -152,9 +144,8 @@ async def update_shopping_item(
    item_id: int,
    body: ShoppingItemUpdate,
    session: CloudUser = Depends(get_session),
    store: Store = Depends(get_store),
 ):
-    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok, locale=_locale_from_store(store))
+    builder = GroceryLinkBuilder(tier=session.tier, has_byok=session.has_byok)
    item = await asyncio.to_thread(
        _in_thread,
        session.db,
--- a/app/api/routes.py
+++ b/app/api/routes.py
@ -1,10 +1,6 @@
 from fastapi import APIRouter
 from app.api.endpoints import health, receipts, export, inventory, ocr, recipes, settings, staples, feedback, feedback_attach, household, saved_recipes, imitate, meal_plans, orch_usage, session, shopping
 from app.api.endpoints.community import router as community_router
 from app.api.endpoints.corrections import router as corrections_router
 from app.api.endpoints.mastodon_oauth import router as mastodon_router
 from app.api.endpoints.recipe_scan import router as recipe_scan_router
 from app.api.endpoints.recipe_tags import router as recipe_tags_router
 api_router = APIRouter()
@ -15,9 +11,6 @@ api_router.include_router(ocr.router,            prefix="/receipts",       tags=
 api_router.include_router(export.router,                                   tags=["export"])
 api_router.include_router(inventory.router,      prefix="/inventory",      tags=["inventory"])
 api_router.include_router(saved_recipes.router,  prefix="/recipes/saved",  tags=["saved-recipes"])
 # recipe_scan_router registered BEFORE recipes.router so /recipes/scan and /recipes/user
 # take priority over /recipes/{recipe_id} (which would otherwise match them as int IDs).
 api_router.include_router(recipe_scan_router,    prefix="/recipes",        tags=["recipe-scan"])
 api_router.include_router(recipes.router,        prefix="/recipes",        tags=["recipes"])
 api_router.include_router(settings.router,       prefix="/settings",       tags=["settings"])
 api_router.include_router(staples.router,        prefix="/staples",        tags=["staples"])
@ -29,6 +22,3 @@ api_router.include_router(meal_plans.router,     prefix="/meal-plans",     tags=
 api_router.include_router(orch_usage.router,     prefix="/orch-usage",     tags=["orch-usage"])
 api_router.include_router(shopping.router,       prefix="/shopping",       tags=["shopping"])
 api_router.include_router(community_router)
 api_router.include_router(recipe_tags_router)
 api_router.include_router(corrections_router,    prefix="/corrections",    tags=["corrections"])
 api_router.include_router(mastodon_router)
--- a/app/cloud_session.py
+++ b/app/cloud_session.py
@ -1,9 +1,11 @@
 """Cloud session resolution for Kiwi FastAPI.
-Delegates JWT validation, Heimdall provisioning, tier resolution, and guest
+Local mode (CLOUD_MODE unset/false): returns a local CloudUser with no auth
-session management to circuitforge_core.CloudSessionFactory. Kiwi-specific
+checks, full tier access, and DB path pointing to settings.DB_PATH.
-CloudUser (per-user DB path, household data, BYOK flag) and DB helpers are
+
-kept here.
+Cloud mode (CLOUD_MODE=true): validates the cf_session JWT injected by Caddy
 as X-CF-Session, resolves user_id, auto-provisions a free Heimdall license on
 first visit, fetches the tier, and returns a per-user DB path.
 FastAPI usage:
    @app.get("/api/v1/inventory/items")
@ -15,10 +17,16 @@ from __future__ import annotations
 import logging
 import os
 import re
 import time
 from dataclasses import dataclass
 from pathlib import Path
-from circuitforge_core.cloud_session import CloudSessionFactory as _CoreFactory, detect_byok
+import uuid
 import jwt as pyjwt
 import requests
 import yaml
 from fastapi import Depends, HTTPException, Request, Response
 log = logging.getLogger(__name__)
@ -27,12 +35,53 @@ log = logging.getLogger(__name__)
 CLOUD_MODE: bool = os.environ.get("CLOUD_MODE", "").lower() in ("1", "true", "yes")
 CLOUD_DATA_ROOT: Path = Path(os.environ.get("CLOUD_DATA_ROOT", "/devl/kiwi-cloud-data"))
 DIRECTUS_JWT_SECRET: str = os.environ.get("DIRECTUS_JWT_SECRET", "")
 HEIMDALL_URL: str = os.environ.get("HEIMDALL_URL", "https://license.circuitforge.tech")
 HEIMDALL_ADMIN_TOKEN: str = os.environ.get("HEIMDALL_ADMIN_TOKEN", "")
 # Dev bypass: comma-separated IPs or CIDR ranges that skip JWT auth.
 # NEVER set this in production. Intended only for LAN developer testing when
 # the request doesn't pass through Caddy (which normally injects X-CF-Session).
 # Example: CLOUD_AUTH_BYPASS_IPS=10.1.10.0/24,127.0.0.1
 import ipaddress as _ipaddress
 _BYPASS_RAW: list[str] = [
    e.strip()
    for e in os.environ.get("CLOUD_AUTH_BYPASS_IPS", "").split(",")
    if e.strip()
 ]
 _BYPASS_NETS: list[_ipaddress.IPv4Network | _ipaddress.IPv6Network] = []
 _BYPASS_IPS: frozenset[str] = frozenset()
 if _BYPASS_RAW:
    _nets, _ips = [], set()
    for entry in _BYPASS_RAW:
        try:
            _nets.append(_ipaddress.ip_network(entry, strict=False))
        except ValueError:
            _ips.add(entry)  # treat non-parseable entries as bare IPs
    _BYPASS_NETS = _nets
    _BYPASS_IPS = frozenset(_ips)
 def _is_bypass_ip(ip: str) -> bool:
    if not ip:
        return False
    if ip in _BYPASS_IPS:
        return True
    try:
        addr = _ipaddress.ip_address(ip)
        return any(addr in net for net in _BYPASS_NETS)
    except ValueError:
        return False
 _LOCAL_KIWI_DB: Path = Path(os.environ.get("KIWI_DB", "data/kiwi.db"))
-TIERS = ["free", "paid", "premium", "ultra"]
+_TIER_CACHE: dict[str, tuple[dict, float]] = {}
 _TIER_CACHE_TTL = 300  # 5 minutes
-_core = _CoreFactory(product="kiwi", byok_detector=detect_byok)
+TIERS = ["free", "paid", "premium", "ultra"]
 def _auth_label(user_id: str) -> str:
@ -57,7 +106,73 @@ class CloudUser:
    license_key: str | None = None  # key_display for lifetime/founders keys; None for subscription/free
-# ── DB path helpers ───────────────────────────────────────────────────────────
+# ── JWT validation ─────────────────────────────────────────────────────────────
 def _extract_session_token(header_value: str) -> str:
    m = re.search(r'(?:^|;)\s*cf_session=([^;]+)', header_value)
    return m.group(1).strip() if m else header_value.strip()
 def validate_session_jwt(token: str) -> str:
    """Validate cf_session JWT and return the Directus user_id."""
    try:
        payload = pyjwt.decode(
            token,
            DIRECTUS_JWT_SECRET,
            algorithms=["HS256"],
            options={"require": ["id", "exp"]},
        )
        return payload["id"]
    except Exception as exc:
        log.debug("JWT validation failed: %s", exc)
        raise HTTPException(status_code=401, detail="Session invalid or expired")
 # ── Heimdall integration ──────────────────────────────────────────────────────
 def _ensure_provisioned(user_id: str) -> None:
    if not HEIMDALL_ADMIN_TOKEN:
        return
    try:
        requests.post(
            f"{HEIMDALL_URL}/admin/provision",
            json={"directus_user_id": user_id, "product": "kiwi", "tier": "free"},
            headers={"Authorization": f"Bearer {HEIMDALL_ADMIN_TOKEN}"},
            timeout=5,
        )
    except Exception as exc:
        log.warning("Heimdall provision failed for user %s: %s", user_id, exc)
 def _fetch_cloud_tier(user_id: str) -> tuple[str, str | None, bool, str | None]:
    """Returns (tier, household_id | None, is_household_owner, license_key | None)."""
    now = time.monotonic()
    cached = _TIER_CACHE.get(user_id)
    if cached and (now - cached[1]) < _TIER_CACHE_TTL:
        entry = cached[0]
        return entry["tier"], entry.get("household_id"), entry.get("is_household_owner", False), entry.get("license_key")
    if not HEIMDALL_ADMIN_TOKEN:
        return "free", None, False, None
    try:
        resp = requests.post(
            f"{HEIMDALL_URL}/admin/cloud/resolve",
            json={"directus_user_id": user_id, "product": "kiwi"},
            headers={"Authorization": f"Bearer {HEIMDALL_ADMIN_TOKEN}"},
            timeout=5,
        )
        data = resp.json() if resp.ok else {}
        tier = data.get("tier", "free")
        household_id = data.get("household_id")
        is_owner = data.get("is_household_owner", False)
        license_key = data.get("key_display")
    except Exception as exc:
        log.warning("Heimdall tier resolve failed for user %s: %s", user_id, exc)
        tier, household_id, is_owner, license_key = "free", None, False, None
    _TIER_CACHE[user_id] = ({"tier": tier, "household_id": household_id, "is_household_owner": is_owner, "license_key": license_key}, now)
    return tier, household_id, is_owner, license_key
 def _user_db_path(user_id: str, household_id: str | None = None) -> Path:
    if household_id:
@ -79,45 +194,112 @@ def _anon_guest_db_path(guest_id: str) -> Path:
    return path
 # ── BYOK detection ────────────────────────────────────────────────────────────
 _LLM_CONFIG_PATH = Path.home() / ".config" / "circuitforge" / "llm.yaml"
 def _detect_byok(config_path: Path = _LLM_CONFIG_PATH) -> bool:
    """Return True if at least one enabled non-vision LLM backend is configured.
    Reads the same llm.yaml that LLMRouter uses. Local (Ollama, vLLM) and
    API-key backends both count — the policy is "user is supplying compute",
    regardless of where that compute lives.
    """
    try:
        with open(config_path) as f:
            cfg = yaml.safe_load(f) or {}
        return any(
            b.get("enabled", True) and b.get("type") != "vision_service"
            for b in cfg.get("backends", {}).values()
        )
    except Exception:
        return False
 # ── FastAPI dependency ────────────────────────────────────────────────────────
-def get_session(request: Request, response: Response) -> CloudUser:
+_GUEST_COOKIE = "kiwi_guest_id"
-    """FastAPI dependency — resolves the current user from the request.
+_GUEST_COOKIE_MAX_AGE = 60 * 60 * 24 * 90  # 90 days
    Delegates auth/tier resolution to cf-core CloudSessionFactory, then maps
    the result to Kiwi's CloudUser with per-user DB path and household data.
-    Local mode: fully-privileged "local" user pointing at local DB.
+def _resolve_guest_session(request: Request, response: Response, has_byok: bool) -> CloudUser:
-    Cloud mode: validates X-CF-Session JWT, provisions license, resolves tier.
+    """Return a per-session anonymous CloudUser, creating a guest UUID cookie if needed."""
-    Dev bypass: CLOUD_AUTH_BYPASS_IPS match returns a "local-dev" session.
+    guest_id = request.cookies.get(_GUEST_COOKIE, "").strip()
-    Anonymous: per-session UUID cookie (cf_guest_id) isolates each guest's data.
+    is_new = not guest_id
-    """
+    if is_new:
-    core_user = _core.resolve(request, response)
+        guest_id = str(uuid.uuid4())
-    uid, tier, has_byok = core_user.user_id, core_user.tier, core_user.has_byok
+        log.debug("New guest session assigned: anon-%s", guest_id[:8])
-
+    # Secure flag only when the request actually arrived over HTTPS
-    if not CLOUD_MODE or uid in ("local", "local-dev"):
+    # (Caddy sets X-Forwarded-Proto=https in cloud; absent on direct port access).
-        # local-dev gets a writable path under CLOUD_DATA_ROOT; local uses KIWI_DB
+    # Avoids losing the session cookie on HTTP direct-port testing of the cloud stack.
-        db = _user_db_path(uid) if uid == "local-dev" else _LOCAL_KIWI_DB
+    is_https = request.headers.get("x-forwarded-proto", "http").lower() == "https"
-        return CloudUser(user_id=uid, tier=tier, db=db, has_byok=has_byok)
+    response.set_cookie(
-
+        key=_GUEST_COOKIE,
-    if uid.startswith("anon-"):
+        value=guest_id,
-        guest_id = uid[len("anon-"):]
+        max_age=_GUEST_COOKIE_MAX_AGE,
        httponly=True,
        samesite="lax",
        secure=is_https,
    )
    return CloudUser(
-            user_id=uid, tier=tier,
+        user_id=f"anon-{guest_id}",
        tier="free",
        db=_anon_guest_db_path(guest_id),
        has_byok=has_byok,
    )
-    household_id = core_user.meta.get("household_id")
+
-    is_owner = core_user.meta.get("is_household_owner", False)
+def get_session(request: Request, response: Response) -> CloudUser:
-    license_key = core_user.meta.get("license_key")
+    """FastAPI dependency — resolves the current user from the request.
-    log.debug("Resolved %s session uid=%s tier=%s household=%s", _auth_label(uid), uid[:8], tier, household_id)
+
    Local mode: fully-privileged "local" user pointing at local DB.
    Cloud mode: validates X-CF-Session JWT, provisions license, resolves tier.
    Dev bypass: if CLOUD_AUTH_BYPASS_IPS is set and the client IP matches,
      returns a "local" session without JWT validation (dev/LAN use only).
    Anonymous: per-session UUID cookie isolates each guest visitor's data.
    """
    has_byok = _detect_byok()
    if not CLOUD_MODE:
        return CloudUser(user_id="local", tier="local", db=_LOCAL_KIWI_DB, has_byok=has_byok)
    # Prefer X-Real-IP (set by Caddy from the actual client address) over the
    # TCP peer address (which is nginx's container IP when behind the proxy).
    client_ip = (
        request.headers.get("x-real-ip", "")
        or (request.client.host if request.client else "")
    )
    if (_BYPASS_IPS or _BYPASS_NETS) and _is_bypass_ip(client_ip):
        log.debug("CLOUD_AUTH_BYPASS_IPS match for %s — returning local session", client_ip)
        # Use a dev DB under CLOUD_DATA_ROOT so the container has a writable path.
        dev_db = _user_db_path("local-dev")
        return CloudUser(user_id="local-dev", tier="local", db=dev_db, has_byok=has_byok)
    # Resolve cf_session JWT: prefer the explicit header injected by Caddy, then
    # fall back to the cf_session cookie value.  Other cookies (e.g. kiwi_guest_id)
    # must never be treated as auth tokens.
    raw_session = request.headers.get("x-cf-session", "").strip()
    if not raw_session:
        raw_session = request.cookies.get("cf_session", "").strip()
    if not raw_session:
        return _resolve_guest_session(request, response, has_byok)
    token = _extract_session_token(raw_session)  # gitleaks:allow — function name, not a secret
    if not token:
        return _resolve_guest_session(request, response, has_byok)
    user_id = validate_session_jwt(token)
    _ensure_provisioned(user_id)
    tier, household_id, is_household_owner, license_key = _fetch_cloud_tier(user_id)
    return CloudUser(
-        user_id=uid, tier=tier,
+        user_id=user_id,
-        db=_user_db_path(uid, household_id=household_id),
+        tier=tier,
        db=_user_db_path(user_id, household_id=household_id),
        has_byok=has_byok,
        household_id=household_id,
-        is_household_owner=is_owner,
+        is_household_owner=is_household_owner,
        license_key=license_key,
    )
--- a/app/core/config.py
+++ b/app/core/config.py
@ -35,18 +35,6 @@ class Settings:
    # Database
    DB_PATH: Path = Path(os.environ.get("DB_PATH", str(DATA_DIR / "kiwi.db")))
    # Pre-computed browse counts cache (small SQLite, separate from corpus).
    # Written by the nightly refresh task and by infer_recipe_tags.py.
    # Set BROWSE_COUNTS_PATH to a bind-mounted path if you want the host
    # pipeline to share counts with the container without re-running FTS.
    BROWSE_COUNTS_PATH: Path = Path(
        os.environ.get("BROWSE_COUNTS_PATH", str(DATA_DIR / "browse_counts.db"))
    )
    # Magpie data flywheel — ingest endpoint for anonymized recipe signals
    # Set MAGPIE_INGEST_URL to enable; leave unset (or None) to disable silently.
    MAGPIE_INGEST_URL: str | None = os.environ.get("MAGPIE_INGEST_URL") or None
    # Community feature settings
    COMMUNITY_DB_URL: str | None = os.environ.get("COMMUNITY_DB_URL") or None
    COMMUNITY_PSEUDONYM_SALT: str = os.environ.get(
@ -65,52 +53,15 @@ class Settings:
    # Quality
    MIN_QUALITY_SCORE: float = float(os.environ.get("MIN_QUALITY_SCORE", "50.0"))
-    # CF-core resource coordinator (VRAM lease management — lease broker, not inference)
+    # CF-core resource coordinator (VRAM lease management)
    COORDINATOR_URL: str = os.environ.get("COORDINATOR_URL", "http://localhost:7700")
    # GPU inference server URL
    # Priority: GPU_SERVER_URL env var → CF_ORCH_URL env var (backward compat)
    #           → https://orch.circuitforge.tech when CF_LICENSE_KEY is present (Paid+)
    # Resolved value is written back to os.environ["CF_ORCH_URL"] at startup so
    # all service-layer callers that read CF_ORCH_URL directly see the right URL.
    GPU_SERVER_URL: str | None = (
        os.environ.get("GPU_SERVER_URL")
        or os.environ.get("CF_ORCH_URL")
        or (
            "https://orch.circuitforge.tech"
            if os.environ.get("CF_LICENSE_KEY")
            else None
        )
    )
    # Hosted cf-orch coordinator — bearer token for managed cloud GPU inference (Paid+)
    # CFOrchClient reads CF_LICENSE_KEY automatically; exposed here for startup validation.
    CF_LICENSE_KEY: str | None = os.environ.get("CF_LICENSE_KEY")
    # E2E test account — analytics logging is suppressed for this user_id so test
    # runs don't pollute session counts.  Set to the Directus UUID of the test user.
    E2E_TEST_USER_ID: str | None = os.environ.get("E2E_TEST_USER_ID") or None
    # ActivityPub federation (optional; disabled by default)
    AP_ENABLED: bool = os.environ.get("AP_ENABLED", "false").lower() in ("1", "true", "yes")
    AP_HOST: str = os.environ.get("AP_HOST", "")          # e.g. kiwi.circuitforge.tech
    CLOUD_DATA_ROOT: Path = Path(os.environ.get("CLOUD_DATA_ROOT", "/devl/kiwi-cloud-data"))
    AP_KEY_PATH: Path = Path(
        os.environ.get("AP_KEY_PATH", str(CLOUD_DATA_ROOT / "ap_keys" / "instance.pem"))
    )
    # Fernet key for Mastodon access token encryption (base64-urlsafe, 32 bytes)
    # Leave unset to skip encryption (dev only)
    AP_TOKEN_ENCRYPTION_KEY: str | None = os.environ.get("AP_TOKEN_ENCRYPTION_KEY") or None
    # Feature flags
    ENABLE_OCR: bool = os.environ.get("ENABLE_OCR", "false").lower() in ("1", "true", "yes")
    # Use OrchestratedScheduler (coordinator-aware, multi-GPU fan-out) instead of
    # LocalScheduler. Defaults to true in CLOUD_MODE; can be set independently
    # for multi-GPU local rigs that don't need full cloud auth.
    USE_ORCH_SCHEDULER: bool | None = (
        None if os.environ.get("USE_ORCH_SCHEDULER") is None
        else os.environ.get("USE_ORCH_SCHEDULER", "").lower() in ("1", "true", "yes")
    )
    # Runtime
    DEBUG: bool = os.environ.get("DEBUG", "false").lower() in ("1", "true", "yes")
@ -123,9 +74,3 @@ class Settings:
 settings = Settings()
 # Normalise GPU_SERVER_URL into CF_ORCH_URL so every service-layer caller that
 # reads os.environ.get("CF_ORCH_URL") sees the resolved value, including the
 # Paid+ cloud default injected above.
 if settings.GPU_SERVER_URL:
    os.environ["CF_ORCH_URL"] = settings.GPU_SERVER_URL
--- a/app/db/migrations/034_recipe_jobs.sql
+++ b/app/db/migrations/034_recipe_jobs.sql
@ -1,14 +0,0 @@
 -- Migration 034: async recipe generation job queue
 CREATE TABLE IF NOT EXISTS recipe_jobs (
    id          INTEGER PRIMARY KEY AUTOINCREMENT,
    job_id      TEXT    NOT NULL UNIQUE,
    user_id     TEXT    NOT NULL,
    status      TEXT    NOT NULL DEFAULT 'queued',
    request     TEXT    NOT NULL,
    result      TEXT,
    error       TEXT,
    created_at  TEXT    NOT NULL DEFAULT (datetime('now')),
    updated_at  TEXT    NOT NULL DEFAULT (datetime('now'))
 );
 CREATE INDEX IF NOT EXISTS idx_recipe_jobs_job_id  ON recipe_jobs (job_id);
 CREATE INDEX IF NOT EXISTS idx_recipe_jobs_user_id ON recipe_jobs (user_id, created_at DESC);
--- a/app/db/migrations/035_sensory_tags.sql
+++ b/app/db/migrations/035_sensory_tags.sql
@ -1,12 +0,0 @@
 -- Migration 035: add sensory_tags column for sensory profile filtering
 --
 -- sensory_tags holds a JSON object with texture, smell, and noise signals:
 --   {"textures": ["mushy", "creamy"], "smell": "pungent", "noise": "moderate"}
 --
 -- Empty object '{}' means untagged — these recipes pass ALL sensory filters
 -- (graceful degradation when tag_sensory_profiles.py has not yet been run).
 --
 -- Populated offline by: python scripts/tag_sensory_profiles.py [path/to/kiwi.db]
 -- No FTS rebuild needed — sensory_tags is filtered in Python after candidate fetch.
 ALTER TABLE recipes ADD COLUMN sensory_tags TEXT NOT NULL DEFAULT '{}';
--- a/app/db/migrations/036_captured_products.sql
+++ b/app/db/migrations/036_captured_products.sql
@ -1,26 +0,0 @@
 -- Migration 036: captured_products local cache
 -- Products captured via visual label scanning (kiwi#79).
 -- Keyed by barcode; checked before FDC/OFF on future scans so each product
 -- is only captured once per device.
 CREATE TABLE IF NOT EXISTS captured_products (
    id                INTEGER PRIMARY KEY AUTOINCREMENT,
    barcode           TEXT UNIQUE NOT NULL,
    product_name      TEXT,
    brand             TEXT,
    serving_size_g    REAL,
    calories          REAL,
    fat_g             REAL,
    saturated_fat_g   REAL,
    carbs_g           REAL,
    sugar_g           REAL,
    fiber_g           REAL,
    protein_g         REAL,
    sodium_mg         REAL,
    ingredient_names  TEXT NOT NULL DEFAULT '[]',   -- JSON array
    allergens         TEXT NOT NULL DEFAULT '[]',   -- JSON array
    confidence        REAL,
    source            TEXT NOT NULL DEFAULT 'visual_capture',
    captured_at       TEXT NOT NULL DEFAULT (datetime('now')),
    confirmed_by_user INTEGER NOT NULL DEFAULT 0
 );
--- a/app/db/migrations/037_products_source_visual_capture.sql
+++ b/app/db/migrations/037_products_source_visual_capture.sql
@ -1,34 +0,0 @@
 -- Migration 037: add 'visual_capture' to products.source CHECK constraint
 -- SQLite cannot ALTER a CHECK constraint, so we rebuild the table.
 PRAGMA foreign_keys = OFF;
 BEGIN;
 CREATE TABLE products_new (
    id              INTEGER PRIMARY KEY AUTOINCREMENT,
    barcode         TEXT UNIQUE,
    name            TEXT NOT NULL,
    brand           TEXT,
    category        TEXT,
    description     TEXT,
    image_url       TEXT,
    nutrition_data  TEXT NOT NULL DEFAULT '{}',
    source          TEXT NOT NULL DEFAULT 'openfoodfacts'
                         CHECK (source IN ('openfoodfacts', 'manual', 'receipt_ocr', 'visual_capture')),
    source_data     TEXT,
    created_at      TEXT NOT NULL DEFAULT (datetime('now')),
    updated_at      TEXT NOT NULL DEFAULT (datetime('now'))
 );
 INSERT INTO products_new
    SELECT id, barcode, name, brand, category, description, image_url,
           nutrition_data, source, source_data, created_at, updated_at
    FROM products;
 DROP TABLE products;
 ALTER TABLE products_new RENAME TO products;
 COMMIT;
 PRAGMA foreign_keys = ON;
--- a/app/db/migrations/038_inventory_items_source_visual_capture.sql
+++ b/app/db/migrations/038_inventory_items_source_visual_capture.sql
@ -1,43 +0,0 @@
 -- Migration 038: add 'visual_capture' to inventory_items.source CHECK constraint
 -- SQLite cannot ALTER a CHECK constraint, so we rebuild the table.
 PRAGMA foreign_keys = OFF;
 BEGIN;
 CREATE TABLE inventory_items_new (
    id              INTEGER PRIMARY KEY AUTOINCREMENT,
    product_id      INTEGER NOT NULL
                        REFERENCES products (id) ON DELETE RESTRICT,
    receipt_id      INTEGER
                        REFERENCES receipts (id) ON DELETE SET NULL,
    quantity        REAL NOT NULL DEFAULT 1 CHECK (quantity > 0),
    unit            TEXT NOT NULL DEFAULT 'count',
    location        TEXT NOT NULL,
    sublocation     TEXT,
    purchase_date   TEXT,
    expiration_date TEXT,
    status          TEXT NOT NULL DEFAULT 'available'
                        CHECK (status IN ('available', 'consumed', 'expired', 'discarded')),
    consumed_at     TEXT,
    notes           TEXT,
    source          TEXT NOT NULL DEFAULT 'manual'
                        CHECK (source IN ('barcode_scan', 'manual', 'receipt', 'visual_capture')),
    created_at      TEXT NOT NULL DEFAULT (datetime('now')),
    updated_at      TEXT NOT NULL DEFAULT (datetime('now')),
    opened_date     TEXT,
    disposal_reason TEXT
 );
 INSERT INTO inventory_items_new
    SELECT id, product_id, receipt_id, quantity, unit, location, sublocation,
           purchase_date, expiration_date, status, consumed_at, notes, source,
           created_at, updated_at, opened_date, disposal_reason
    FROM inventory_items;
 DROP TABLE inventory_items;
 ALTER TABLE inventory_items_new RENAME TO inventory_items;
 COMMIT;
 PRAGMA foreign_keys = ON;
--- a/app/db/migrations/039_saved_recipes_drop_fk.sql
+++ b/app/db/migrations/039_saved_recipes_drop_fk.sql
@ -1,31 +0,0 @@
 -- Migration 039: Drop FK constraint on saved_recipes.recipe_id.
 --
 -- In cloud mode the recipe corpus is ATTACHed as a separate database.
 -- SQLite FK constraints only resolve against the `main` schema, so
 -- `REFERENCES recipes(id)` was always failing for cloud saves (the
 -- main.recipes table is empty; all data lives in corpus.recipes).
 -- The corpus is read-only and never modified by the app, so cascade-on-delete
 -- is meaningless anyway. Remove the constraint without changing any data.
 PRAGMA foreign_keys = OFF;
 CREATE TABLE saved_recipes_new (
    id          INTEGER PRIMARY KEY AUTOINCREMENT,
    recipe_id   INTEGER NOT NULL,
    saved_at    TEXT    NOT NULL DEFAULT (datetime('now')),
    notes       TEXT,
    rating      INTEGER CHECK (rating IS NULL OR (rating >= 0 AND rating <= 5)),
    style_tags  TEXT    NOT NULL DEFAULT '[]',
    UNIQUE (recipe_id)
 );
 INSERT INTO saved_recipes_new SELECT * FROM saved_recipes;
 DROP TABLE saved_recipes;
 ALTER TABLE saved_recipes_new RENAME TO saved_recipes;
 CREATE INDEX IF NOT EXISTS idx_saved_recipes_saved_at ON saved_recipes (saved_at DESC);
 CREATE INDEX IF NOT EXISTS idx_saved_recipes_rating   ON saved_recipes (rating);
 PRAGMA foreign_keys = ON;
--- a/app/db/migrations/040_corrections.sql
+++ b/app/db/migrations/040_corrections.sql
@ -1,21 +0,0 @@
 -- 040_corrections.sql — corrections table for SFT training data
 -- Schema from circuitforge_core.api.corrections.CORRECTIONS_MIGRATION_SQL
 CREATE TABLE IF NOT EXISTS corrections (
    id               INTEGER PRIMARY KEY AUTOINCREMENT,
    item_id          TEXT    NOT NULL DEFAULT '',
    product          TEXT    NOT NULL,
    correction_type  TEXT    NOT NULL,
    input_text       TEXT    NOT NULL,
    original_output  TEXT    NOT NULL,
    corrected_output TEXT    NOT NULL DEFAULT '',
    rating           TEXT    NOT NULL DEFAULT 'down',
    context          TEXT    NOT NULL DEFAULT '{}',
    opted_in         INTEGER NOT NULL DEFAULT 0,
    created_at       TEXT    NOT NULL DEFAULT (datetime('now'))
 );
 CREATE INDEX IF NOT EXISTS idx_corrections_product
    ON corrections (product);
 CREATE INDEX IF NOT EXISTS idx_corrections_opted_in
    ON corrections (opted_in);
--- a/app/db/migrations/041_user_recipes.sql
+++ b/app/db/migrations/041_user_recipes.sql
@ -1,23 +0,0 @@
 -- Migration 041: user_recipes table for user-scanned and manually-entered recipes.
 --
 -- Separate from the food.com corpus (recipes table) -- user recipes are personal,
 -- not curated, and need different fields (servings as string, cook_time as string).
 CREATE TABLE IF NOT EXISTS user_recipes (
    id          INTEGER PRIMARY KEY AUTOINCREMENT,
    title       TEXT NOT NULL,
    subtitle    TEXT,
    servings    TEXT,           -- kept as string: "2", "4-6", "serves 8"
    cook_time   TEXT,           -- kept as string: "25 min", "1 hour"
    source_note TEXT,           -- e.g. "Purple Carrot", "Betty Crocker"
    ingredients TEXT NOT NULL DEFAULT '[]', -- JSON: [{name, qty, unit, raw}]
    steps       TEXT NOT NULL DEFAULT '[]', -- JSON: ["step 1", "step 2", ...]
    notes       TEXT,
    tags        TEXT DEFAULT '[]',          -- JSON: ["vegan", "quick"]
    source      TEXT NOT NULL DEFAULT 'manual', -- 'scan' | 'manual'
    pantry_match_pct INTEGER,   -- 0-100, computed at scan time; null for manual
    created_at  TEXT NOT NULL DEFAULT (datetime('now')),
    updated_at  TEXT NOT NULL DEFAULT (datetime('now'))
 );
 CREATE INDEX IF NOT EXISTS idx_user_recipes_created ON user_recipes (created_at DESC);
--- a/app/db/migrations/042_activitypub.sql
+++ b/app/db/migrations/042_activitypub.sql
@ -1,47 +0,0 @@
 -- 042_activitypub.sql
 -- ActivityPub federation tables: follower registry, delivery log, dedup, Mastodon tokens.
 -- Follower registry: AP actors that Follow this Kiwi instance
 CREATE TABLE IF NOT EXISTS ap_followers (
    id          INTEGER PRIMARY KEY,
    actor_id    TEXT NOT NULL UNIQUE,       -- AP actor URL
    inbox_url   TEXT NOT NULL,
    shared_inbox TEXT,
    followed_at TEXT NOT NULL DEFAULT (datetime('now')),
    active      INTEGER NOT NULL DEFAULT 1
 );
 CREATE INDEX IF NOT EXISTS idx_ap_followers_active
    ON ap_followers (active) WHERE active = 1;
 -- Outgoing delivery log: one row per (post_slug, target_inbox) attempt
 CREATE TABLE IF NOT EXISTS ap_deliveries (
    id          INTEGER PRIMARY KEY,
    post_slug   TEXT NOT NULL,
    target_inbox TEXT NOT NULL,
    status      TEXT NOT NULL DEFAULT 'pending', -- pending | delivered | failed
    attempts    INTEGER NOT NULL DEFAULT 0,
    last_error  TEXT,
    created_at  TEXT NOT NULL DEFAULT (datetime('now')),
    delivered_at TEXT
 );
 CREATE INDEX IF NOT EXISTS idx_ap_deliveries_status
    ON ap_deliveries (status) WHERE status != 'delivered';
 -- Incoming activity dedup: prevents replay attacks and double-processing
 CREATE TABLE IF NOT EXISTS ap_received (
    activity_id TEXT PRIMARY KEY,
    received_at TEXT NOT NULL DEFAULT (datetime('now'))
 );
 -- Mastodon OAuth tokens: per-user, encrypted at rest
 -- Stored in the user's local kiwi.db (CLOUD_MODE: per-user DB tree)
 CREATE TABLE IF NOT EXISTS mastodon_tokens (
    id              INTEGER PRIMARY KEY,
    directus_user_id TEXT NOT NULL UNIQUE,
    instance_url    TEXT NOT NULL,
    access_token    TEXT NOT NULL,   -- Fernet-encrypted when AP_TOKEN_ENCRYPTION_KEY set
    created_at      TEXT NOT NULL DEFAULT (datetime('now')),
    updated_at      TEXT NOT NULL DEFAULT (datetime('now'))
 );
--- a/app/db/session.py
+++ b/app/db/session.py
@ -6,8 +6,6 @@ Cloud mode: opens a Store at the per-user DB path from the CloudUser session.
 """
 from __future__ import annotations
 import sqlite3
 from collections.abc import Iterator
 from typing import Generator
 from fastapi import Depends
@ -23,16 +21,3 @@ def get_store(session: CloudUser = Depends(get_session)) -> Generator[Store, Non
        yield store
    finally:
        store.close()
 def get_db(session: CloudUser = Depends(get_session)) -> Iterator[sqlite3.Connection]:
    """FastAPI dependency — yields the raw sqlite3.Connection for the current user.
    Used by make_corrections_router() from circuitforge-core, which expects a
    dependency that yields a sqlite3.Connection directly.
    """
    store = Store(session.db)
    try:
        yield store.conn
    finally:
        store.close()
--- a/app/db/store.py
+++ b/app/db/store.py
@ -11,7 +11,6 @@ from typing import Any
 from circuitforge_core.db.base import get_connection
 from circuitforge_core.db.migrations import run_migrations
 from app.services.recipe.sensory import SensoryExclude, passes_sensory_filter
 MIGRATIONS_DIR = Path(__file__).parent / "migrations"
@ -60,11 +59,7 @@ class Store:
                    # saved recipe columns
                    "style_tags",
                    # meal plan columns
-                    "meal_types",
+                    "meal_types"):
                    # user_recipes columns
                    "steps", "tags",
                    # captured_products columns
                    "allergens"):
            if key in d and isinstance(d[key], str):
                try:
                    d[key] = json.loads(d[key])
@ -741,41 +736,6 @@ class Store:
            row = self._fetch_one("SELECT * FROM recipes WHERE id = ?", (recipe_id,))
        return row
    # --- Async recipe jobs ---
    def create_recipe_job(self, job_id: str, user_id: str, request_json: str) -> sqlite3.Row:
        return self._insert_returning(
            "INSERT INTO recipe_jobs (job_id, user_id, status, request) VALUES (?,?,?,?) RETURNING *",
            (job_id, user_id, "queued", request_json),
        )
    def get_recipe_job(self, job_id: str, user_id: str) -> sqlite3.Row | None:
        return self._fetch_one(
            "SELECT * FROM recipe_jobs WHERE job_id=? AND user_id=?",
            (job_id, user_id),
        )
    def update_recipe_job_running(self, job_id: str) -> None:
        self.conn.execute(
            "UPDATE recipe_jobs SET status='running', updated_at=datetime('now') WHERE job_id=?",
            (job_id,),
        )
        self.conn.commit()
    def complete_recipe_job(self, job_id: str, result_json: str) -> None:
        self.conn.execute(
            "UPDATE recipe_jobs SET status='done', result=?, updated_at=datetime('now') WHERE job_id=?",
            (result_json, job_id),
        )
        self.conn.commit()
    def fail_recipe_job(self, job_id: str, error: str) -> None:
        self.conn.execute(
            "UPDATE recipe_jobs SET status='failed', error=?, updated_at=datetime('now') WHERE job_id=?",
            (error, job_id),
        )
        self.conn.commit()
    def upsert_built_recipe(
        self,
        external_id: str,
@ -1091,38 +1051,17 @@ class Store:
    # ── recipe browser ────────────────────────────────────────────────────
    def get_browser_categories(
-        self,
+        self, domain: str, keywords_by_category: dict[str, list[str]]
        domain: str,
        keywords_by_category: dict[str, list[str]],
        has_subcategories_by_category: dict[str, bool] | None = None,
    ) -> list[dict]:
-        """Return [{category, recipe_count, has_subcategories}] for each category.
+        """Return [{category, recipe_count}] for each category in the domain.
-        keywords_by_category maps category name → keyword list for counting.
+        keywords_by_category maps category name to the keyword list used to
-        has_subcategories_by_category maps category name → bool (optional;
+        match against recipes.category and recipes.keywords.
        defaults to False for all categories when omitted).
        """
        results = []
        for category, keywords in keywords_by_category.items():
            count = self._count_recipes_for_keywords(keywords)
-            results.append({
+            results.append({"category": category, "recipe_count": count})
                "category": category,
                "recipe_count": count,
                "has_subcategories": (has_subcategories_by_category or {}).get(category, False),
            })
        return results
    def get_browser_subcategories(
        self, domain: str, keywords_by_subcategory: dict[str, list[str]]
    ) -> list[dict]:
        """Return [{subcategory, recipe_count}] for each subcategory.
        Mirrors get_browser_categories but for the second level.
        """
        results = []
        for subcat, keywords in keywords_by_subcategory.items():
            count = self._count_recipes_for_keywords(keywords)
            results.append({"subcategory": subcat, "recipe_count": count})
        return results
    @staticmethod
@ -1131,19 +1070,6 @@ class Store:
        phrases = ['"' + kw.replace('"', '""') + '"' for kw in keywords]
        return " OR ".join(phrases)
    @staticmethod
    def _ingredient_fts_term(ingredient: str) -> str:
        """Build an FTS5 ingredient_names column prefix-filter.
        Returns e.g. 'ingredient_names : "potato"*' which matches any recipe whose
        ingredient_names column contains a token starting with that word. Prefix
        matching (*) means "potato" also matches "potatoes", "sweet potato", etc.
        Apostrophes are stripped because the FTS5 tokenizer drops them.
        """
        cleaned = ingredient.replace("'", "").strip()
        escaped = cleaned.replace('"', '""')
        return f'ingredient_names : "{escaped}"*'
    def _count_recipes_for_keywords(self, keywords: list[str]) -> int:
        if not keywords:
            return 0
@ -1165,141 +1091,45 @@ class Store:
    def browse_recipes(
        self,
-        keywords: list[str] | None,
+        keywords: list[str],
        page: int,
        page_size: int,
        pantry_items: list[str] | None = None,
        q: str | None = None,
        sort: str = "default",
        sensory_exclude: SensoryExclude | None = None,
        required_ingredient: str | None = None,
    ) -> dict:
        """Return a page of recipes matching the keyword set.
        Pass keywords=None to browse all recipes without category filtering.
        Each recipe row includes match_pct (float | None) when pantry_items
        is provided. match_pct is the fraction of ingredient_names covered by
        the pantry set — computed deterministically, no LLM needed.
        q:                    optional title substring filter (case-insensitive LIKE).
        sort:                 "default" (corpus order) | "alpha" (A→Z) | "alpha_desc" (Z→A)
                              | "match" (pantry coverage DESC — falls back to default when no pantry).
        required_ingredient:  when set, only return recipes whose ingredient_names contain
                              this substring (case-insensitive). "must include" filter.
        """
-        if keywords is not None and not keywords:
+        if not keywords:
            return {"recipes": [], "total": 0, "page": page}
        match_expr = self._browser_fts_query(keywords)
        offset = (page - 1) * page_size
        c = self._cp
        pantry_set = {p.lower() for p in pantry_items} if pantry_items else None
        # "match" sort requires pantry items; fall back gracefully when absent.
        effective_sort = sort if (sort != "match" or pantry_set) else "default"
        order_clause = {
            "alpha":      "ORDER BY title ASC",
            "alpha_desc": "ORDER BY title DESC",
        }.get(effective_sort, "ORDER BY id ASC")
        q_param = f"%{q.strip()}%" if q and q.strip() else None
        # ── required-ingredient FTS filter (must-include) ─────────────────────
        # FTS5 column prefix-filter avoids the full table scan that LIKE '%X%' would do.
        req_fts_term = (
            self._ingredient_fts_term(required_ingredient) if required_ingredient else ""
        )
        # ── match sort: push match_pct computation into SQL so ORDER BY works ──
        if effective_sort == "match" and pantry_set:
            return self._browse_by_match(
                keywords, page, page_size, offset, pantry_set, q_param, c,
                sensory_exclude=sensory_exclude,
                required_ingredient=required_ingredient,
            )
        cols = (
            f"SELECT id, title, category, keywords, ingredient_names,"
            f"       calories, fat_g, protein_g, sodium_mg, directions, sensory_tags FROM {c}recipes"
        )
        fts_sub = f"id IN (SELECT rowid FROM {c}recipe_browser_fts WHERE recipe_browser_fts MATCH ?)"
        if keywords is None:
            if req_fts_term:
                # Ingredient filter: use FTS index — much faster than LIKE on full table
                if q_param:
                    total = self.conn.execute(
                        f"SELECT COUNT(*) FROM {c}recipes WHERE {fts_sub} AND LOWER(title) LIKE LOWER(?)",
                        (req_fts_term, q_param),
                    ).fetchone()[0]
                    rows = self._fetch_all(
                        f"{cols} WHERE {fts_sub} AND LOWER(title) LIKE LOWER(?) {order_clause} LIMIT ? OFFSET ?",
                        (req_fts_term, q_param, page_size, offset),
                    )
                else:
                    total = self.conn.execute(
                        f"SELECT COUNT(*) FROM {c}recipes WHERE {fts_sub}",
                        (req_fts_term,),
                    ).fetchone()[0]
                    rows = self._fetch_all(
                        f"{cols} WHERE {fts_sub} {order_clause} LIMIT ? OFFSET ?",
                        (req_fts_term, page_size, offset),
                    )
            elif q_param:
                total = self.conn.execute(
                    f"SELECT COUNT(*) FROM {c}recipes WHERE LOWER(title) LIKE LOWER(?)",
                    (q_param,),
                ).fetchone()[0]
                rows = self._fetch_all(
                    f"{cols} WHERE LOWER(title) LIKE LOWER(?) {order_clause} LIMIT ? OFFSET ?",
                    (q_param, page_size, offset),
                )
            else:
                total = self.conn.execute(f"SELECT COUNT(*) FROM {c}recipes").fetchone()[0]
                rows = self._fetch_all(
                    f"{cols} {order_clause} LIMIT ? OFFSET ?",
                    (page_size, offset),
                )
        else:
            keywords_expr = self._browser_fts_query(keywords)
            # Combine keywords + ingredient into one FTS MATCH to use a single index pass
            combined_match = (
                f"({keywords_expr}) AND {req_fts_term}" if req_fts_term else keywords_expr
            )
            if q_param:
                total = self.conn.execute(
                    f"SELECT COUNT(*) FROM {c}recipes WHERE {fts_sub} AND LOWER(title) LIKE LOWER(?)",
                    (combined_match, q_param),
                ).fetchone()[0]
                rows = self._fetch_all(
                    f"{cols} WHERE {fts_sub} AND LOWER(title) LIKE LOWER(?) {order_clause} LIMIT ? OFFSET ?",
                    (combined_match, q_param, page_size, offset),
                )
            else:
                if required_ingredient:
                    total = self.conn.execute(
                        f"SELECT COUNT(*) FROM {c}recipes WHERE {fts_sub}",
                        (combined_match,),
                    ).fetchone()[0]
                else:
        # Reuse cached count — avoids a second index scan on every page turn.
        total = self._count_recipes_for_keywords(keywords)
                rows = self._fetch_all(
                    f"{cols} WHERE {fts_sub} {order_clause} LIMIT ? OFFSET ?",
                    (combined_match, page_size, offset),
                )
                # Community tag fallback: if FTS found nothing, check whether
                # community-tagged recipe IDs exist for this keyword context.
                # browse_recipes doesn't know domain/category directly, so the
                # fallback is triggered by the caller via community_ids= when needed.
                # (See browse_recipes_with_community_fallback in the endpoint layer.)
        c = self._cp
        rows = self._fetch_all(
            f"""
            SELECT id, title, category, keywords, ingredient_names,
                   calories, fat_g, protein_g, sodium_mg
            FROM {c}recipes
            WHERE id IN (
                SELECT rowid FROM {c}recipe_browser_fts
                WHERE recipe_browser_fts MATCH ?
            )
            ORDER BY id ASC
            LIMIT ? OFFSET ?
            """,
            (match_expr, page_size, offset),
        )
        pantry_set = {p.lower() for p in pantry_items} if pantry_items else None
        recipes = []
        for r in rows:
            # Apply sensory filter -- untagged recipes (empty {}) always pass
            if sensory_exclude and not sensory_exclude.is_empty():
                if not passes_sensory_filter(r.get("sensory_tags"), sensory_exclude):
                    continue
            entry = {
                "id":         r["id"],
                "title":      r["title"],
@ -1309,197 +1139,14 @@ class Store:
            if pantry_set:
                names = r.get("ingredient_names") or []
                if names:
-                    matched = sum(1 for n in names if n.lower() in pantry_set)
+                    matched = sum(
                        1 for n in names if n.lower() in pantry_set
                    )
                    entry["match_pct"] = round(matched / len(names), 3)
            recipes.append(entry)
        return {"recipes": recipes, "total": total, "page": page}
    def fetch_recipes_by_ids(
        self,
        recipe_ids: list[int],
        pantry_items: list[str] | None = None,
    ) -> list[dict]:
        """Fetch a specific set of corpus recipes by ID for community tag fallback.
        Returns recipes in the same shape as browse_recipes rows, with match_pct
        populated when pantry_items are provided.
        """
        if not recipe_ids:
            return []
        c = self._cp
        pantry_set = {p.lower() for p in pantry_items} if pantry_items else None
        ph = ",".join("?" * len(recipe_ids))
        rows = self._fetch_all(
            f"SELECT id, title, category, keywords, ingredient_names,"
            f"       calories, fat_g, protein_g, sodium_mg, directions"
            f" FROM {c}recipes WHERE id IN ({ph}) ORDER BY id ASC",
            tuple(recipe_ids),
        )
        result = []
        for r in rows:
            entry: dict = {
                "id":        r["id"],
                "title":     r["title"],
                "category":  r["category"],
                "match_pct": None,
            }
            entry["directions"] = r.get("directions")
            if pantry_set:
                names = r.get("ingredient_names") or []
                if names:
                    matched = sum(1 for n in names if n.lower() in pantry_set)
                    entry["match_pct"] = round(matched / len(names), 3)
            result.append(entry)
        return result
    # How many FTS candidates to fetch before Python-scoring for match sort.
    # Large enough to cover several pages with good diversity; small enough
    # that json-parsing + dict-lookup stays sub-second even for big categories.
    _MATCH_POOL_SIZE = 800
    def _browse_by_match(
        self,
        keywords: list[str] | None,
        page: int,
        page_size: int,
        offset: int,
        pantry_set: set[str],
        q_param: str | None,
        c: str,
        sensory_exclude: SensoryExclude | None = None,
        required_ingredient: str | None = None,
    ) -> dict:
        """Browse recipes sorted by pantry match percentage.
        Fetches up to _MATCH_POOL_SIZE FTS candidates, scores each against the
        pantry set in Python (fast dict lookup on a bounded list), then sorts
        and paginates in-memory. This avoids correlated json_each() subqueries
        that are prohibitively slow over 50k+ row result sets.
        The reported total is the full FTS count (from cache), not pool size.
        """
        import json as _json
        pantry_lower = {p.lower() for p in pantry_set}
        # ── required-ingredient FTS filter (must-include) ─────────────────────
        req_fts_term = (
            self._ingredient_fts_term(required_ingredient) if required_ingredient else ""
        )
        # ── Fetch candidate pool from FTS ────────────────────────────────────
        base_cols = (
            f"SELECT r.id, r.title, r.category, r.ingredient_names, r.directions, r.sensory_tags"
            f" FROM {c}recipes r"
        )
        fts_sub = (
            f"r.id IN (SELECT rowid FROM {c}recipe_browser_fts"
            f" WHERE recipe_browser_fts MATCH ?)"
        )
        self.conn.row_factory = sqlite3.Row
        if keywords is None:
            if req_fts_term:
                if q_param:
                    total = self.conn.execute(
                        f"SELECT COUNT(*) FROM {c}recipes WHERE id IN"
                        f" (SELECT rowid FROM {c}recipe_browser_fts WHERE recipe_browser_fts MATCH ?)"
                        f" AND LOWER(title) LIKE LOWER(?)",
                        (req_fts_term, q_param),
                    ).fetchone()[0]
                    rows = self.conn.execute(
                        f"{base_cols} WHERE {fts_sub} AND LOWER(r.title) LIKE LOWER(?)"
                        f" ORDER BY r.id ASC LIMIT ?",
                        (req_fts_term, q_param, self._MATCH_POOL_SIZE),
                    ).fetchall()
                else:
                    total = self.conn.execute(
                        f"SELECT COUNT(*) FROM {c}recipes WHERE id IN"
                        f" (SELECT rowid FROM {c}recipe_browser_fts WHERE recipe_browser_fts MATCH ?)",
                        (req_fts_term,),
                    ).fetchone()[0]
                    rows = self.conn.execute(
                        f"{base_cols} WHERE {fts_sub} ORDER BY r.id ASC LIMIT ?",
                        (req_fts_term, self._MATCH_POOL_SIZE),
                    ).fetchall()
            elif q_param:
                total = self.conn.execute(
                    f"SELECT COUNT(*) FROM {c}recipes WHERE LOWER(title) LIKE LOWER(?)",
                    (q_param,),
                ).fetchone()[0]
                rows = self.conn.execute(
                    f"{base_cols} WHERE LOWER(r.title) LIKE LOWER(?)"
                    f" ORDER BY r.id ASC LIMIT ?",
                    (q_param, self._MATCH_POOL_SIZE),
                ).fetchall()
            else:
                total = self.conn.execute(
                    f"SELECT COUNT(*) FROM {c}recipes"
                ).fetchone()[0]
                rows = self.conn.execute(
                    f"{base_cols} ORDER BY r.id ASC LIMIT ?",
                    (self._MATCH_POOL_SIZE,),
                ).fetchall()
        else:
            keywords_expr = self._browser_fts_query(keywords)
            combined_match = (
                f"({keywords_expr}) AND {req_fts_term}" if req_fts_term else keywords_expr
            )
            if q_param:
                total = self.conn.execute(
                    f"SELECT COUNT(*) FROM {c}recipes r"
                    f" WHERE {fts_sub} AND LOWER(r.title) LIKE LOWER(?)",
                    (combined_match, q_param),
                ).fetchone()[0]
                rows = self.conn.execute(
                    f"{base_cols} WHERE {fts_sub} AND LOWER(r.title) LIKE LOWER(?)"
                    f" ORDER BY r.id ASC LIMIT ?",
                    (combined_match, q_param, self._MATCH_POOL_SIZE),
                ).fetchall()
            else:
                if required_ingredient:
                    total = self.conn.execute(
                        f"SELECT COUNT(*) FROM {c}recipes r WHERE {fts_sub}",
                        (combined_match,),
                    ).fetchone()[0]
                else:
                    total = self._count_recipes_for_keywords(keywords)
                rows = self.conn.execute(
                    f"{base_cols} WHERE {fts_sub} ORDER BY r.id ASC LIMIT ?",
                    (combined_match, self._MATCH_POOL_SIZE),
                ).fetchall()
        # ── Score in Python, sort, paginate ──────────────────────────────────
        scored = []
        for r in rows:
            row = dict(r)
            # Sensory filter applied before scoring to keep hot path clean
            if sensory_exclude and not sensory_exclude.is_empty():
                if not passes_sensory_filter(row.get("sensory_tags"), sensory_exclude):
                    continue
            try:
                names = _json.loads(row["ingredient_names"] or "[]")
            except Exception:
                names = []
            if names:
                matched = sum(1 for n in names if n.lower() in pantry_lower)
                match_pct = round(matched / len(names), 3)
            else:
                match_pct = None
            scored.append({
                "id":        row["id"],
                "title":     row["title"],
                "category":  row["category"],
                "match_pct": match_pct,
                "directions": row.get("directions"),
            })
        scored.sort(key=lambda r: (-(r["match_pct"] or 0), r["id"]))
        page_slice = scored[offset: offset + page_size]
        return {"recipes": page_slice, "total": total, "page": page}
    def log_browser_telemetry(
        self,
        domain: str,
@ -1734,124 +1381,3 @@ class Store:
        cur = self.conn.execute("DELETE FROM shopping_list_items")
        self.conn.commit()
        return cur.rowcount
    # ── Captured products (visual label cache) ────────────────────────────────
    def get_captured_product(self, barcode: str) -> dict | None:
        """Look up a locally-captured product by barcode.
        Returns the row dict (ingredient_names and allergens already decoded as
        lists) or None if the barcode has not been captured yet.
        """
        return self._fetch_one(
            "SELECT * FROM captured_products WHERE barcode = ?", (barcode,)
        )
    def save_captured_product(
        self,
        barcode: str,
        *,
        product_name: str | None = None,
        brand: str | None = None,
        serving_size_g: float | None = None,
        calories: float | None = None,
        fat_g: float | None = None,
        saturated_fat_g: float | None = None,
        carbs_g: float | None = None,
        sugar_g: float | None = None,
        fiber_g: float | None = None,
        protein_g: float | None = None,
        sodium_mg: float | None = None,
        ingredient_names: list[str] | None = None,
        allergens: list[str] | None = None,
        confidence: float | None = None,
        confirmed_by_user: bool = True,
        source: str = "visual_capture",
    ) -> dict:
        """Insert or replace a captured product row, returning the saved dict."""
        return self._insert_returning(
            """INSERT INTO captured_products
               (barcode, product_name, brand, serving_size_g, calories,
                fat_g, saturated_fat_g, carbs_g, sugar_g, fiber_g,
                protein_g, sodium_mg, ingredient_names, allergens,
                confidence, confirmed_by_user, source)
               VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
               ON CONFLICT(barcode) DO UPDATE SET
                 product_name      = excluded.product_name,
                 brand             = excluded.brand,
                 serving_size_g    = excluded.serving_size_g,
                 calories          = excluded.calories,
                 fat_g             = excluded.fat_g,
                 saturated_fat_g   = excluded.saturated_fat_g,
                 carbs_g           = excluded.carbs_g,
                 sugar_g           = excluded.sugar_g,
                 fiber_g           = excluded.fiber_g,
                 protein_g         = excluded.protein_g,
                 sodium_mg         = excluded.sodium_mg,
                 ingredient_names  = excluded.ingredient_names,
                 allergens         = excluded.allergens,
                 confidence        = excluded.confidence,
                 confirmed_by_user = excluded.confirmed_by_user,
                 source            = excluded.source,
                 captured_at       = datetime('now')
               RETURNING *""",
            (
                barcode, product_name, brand, serving_size_g, calories,
                fat_g, saturated_fat_g, carbs_g, sugar_g, fiber_g,
                protein_g, sodium_mg,
                self._dump(ingredient_names or []),
                self._dump(allergens or []),
                confidence, 1 if confirmed_by_user else 0, source,
            ),
        )
    # ── User Recipes (kiwi#9) ──────────────────────────────────────────────────
    def create_user_recipe(
        self,
        title: str,
        ingredients: list[dict],
        steps: list[str],
        subtitle: str | None = None,
        servings: str | None = None,
        cook_time: str | None = None,
        source_note: str | None = None,
        notes: str | None = None,
        tags: list[str] | None = None,
        source: str = "manual",
        pantry_match_pct: int | None = None,
    ) -> dict[str, Any]:
        return self._insert_returning(
            """INSERT INTO user_recipes
               (title, subtitle, servings, cook_time, source_note,
                ingredients, steps, notes, tags, source, pantry_match_pct)
               VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
               RETURNING *""",
            (
                title, subtitle, servings, cook_time, source_note,
                self._dump(ingredients),
                self._dump(steps),
                notes,
                self._dump(tags or []),
                source,
                pantry_match_pct,
            ),
        )
    def get_user_recipe(self, recipe_id: int) -> dict[str, Any] | None:
        return self._fetch_one(
            "SELECT * FROM user_recipes WHERE id = ?",
            (recipe_id,),
        )
    def list_user_recipes(self) -> list[dict[str, Any]]:
        return self._fetch_all(
            "SELECT * FROM user_recipes ORDER BY created_at DESC",
        )
    def delete_user_recipe(self, recipe_id: int) -> bool:
        cur = self.conn.execute(
            "DELETE FROM user_recipes WHERE id = ?", (recipe_id,)
        )
        self.conn.commit()
        return cur.rowcount > 0
--- a/app/main.py
+++ b/app/main.py
@ -1,9 +1,7 @@
 #!/usr/bin/env python
 # app/main.py
 import asyncio
 import logging
 import os
 from contextlib import asynccontextmanager
 from fastapi import FastAPI
@ -18,36 +16,11 @@ from app.services.meal_plan.affiliates import register_kiwi_programs
 logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(name)s: %(message)s")
 logger = logging.getLogger(__name__)
 _BROWSE_REFRESH_INTERVAL_H = 24
 async def _browse_counts_refresh_loop(corpus_path: str) -> None:
    """Refresh browse counts every 24 h while the container is running."""
    from app.db.store import _COUNT_CACHE
    from app.services.recipe.browse_counts_cache import load_into_memory, refresh
    while True:
        await asyncio.sleep(_BROWSE_REFRESH_INTERVAL_H * 3600)
        try:
            logger.info("browse_counts: starting scheduled refresh...")
            computed = await asyncio.to_thread(
                refresh, corpus_path, settings.BROWSE_COUNTS_PATH
            )
            load_into_memory(settings.BROWSE_COUNTS_PATH, _COUNT_CACHE, corpus_path)
            logger.info("browse_counts: scheduled refresh complete (%d sets)", computed)
        except Exception as exc:
            logger.warning("browse_counts: scheduled refresh failed: %s", exc)
@asynccontextmanager
 async def lifespan(app: FastAPI):
    logger.info("Starting Kiwi API...")
    settings.ensure_dirs()
    # Run DB migrations at startup (ensures all tables exist before any request)
    from app.db.store import Store
    _s = Store(settings.DB_PATH)
    _s.close()
    register_kiwi_programs()
    # Start LLM background task scheduler
@ -59,35 +32,6 @@ async def lifespan(app: FastAPI):
    from app.api.endpoints.community import init_community_store
    init_community_store(settings.COMMUNITY_DB_URL)
    # Initialize ActivityPub instance actor (no-op when AP_ENABLED=false)
    if settings.AP_ENABLED and settings.AP_HOST:
        try:
            from app.services.ap.keys import init_actor
            init_actor(host=settings.AP_HOST, key_path=settings.AP_KEY_PATH)
        except Exception as _ap_exc:
            logger.warning("AP init failed (AP features disabled): %s", _ap_exc)
    # Browse counts cache — warm in-memory cache from disk, refresh if stale.
    # Uses the corpus path the store will attach to at request time.
    corpus_path = os.environ.get("RECIPE_DB_PATH", str(settings.DB_PATH))
    try:
        from app.db.store import _COUNT_CACHE
        from app.services.recipe.browse_counts_cache import (
            is_stale, load_into_memory, refresh,
        )
        if is_stale(settings.BROWSE_COUNTS_PATH):
            logger.info("browse_counts: cache stale — refreshing in background...")
            asyncio.create_task(
                asyncio.to_thread(refresh, corpus_path, settings.BROWSE_COUNTS_PATH)
            )
        else:
            load_into_memory(settings.BROWSE_COUNTS_PATH, _COUNT_CACHE, corpus_path)
    except Exception as exc:
        logger.warning("browse_counts: startup init failed (live FTS fallback active): %s", exc)
    # Nightly background refresh loop
    asyncio.create_task(_browse_counts_refresh_loop(corpus_path))
    yield
    # Graceful scheduler shutdown
@ -114,11 +58,6 @@ app.add_middleware(
 app.include_router(api_router, prefix=settings.API_PREFIX)
 # AP endpoints: WebFinger at root (not under /api/v1), AP objects under /ap
 from app.api.endpoints.activitypub import ap_router, webfinger_router
 app.include_router(webfinger_router)
 app.include_router(ap_router)
@app.get("/")
 async def root():
--- a/app/mcp/init.py
+++ b/app/mcp/init.py
--- a/app/mcp/server.py
+++ b/app/mcp/server.py
@ -1,306 +0,0 @@
 """Kiwi MCP Server — read-only corpus DB access for tag/keyword audits.
 Exposes four tools to Claude:
  kiwi_query_corpus   — run a read-only SQL query against the corpus DB
  kiwi_count_fts      — run an FTS5 MATCH expression and return row count
  kiwi_sample_tags    — return tag frequency distribution by prefix
  kiwi_browse_preview — call the browse endpoint and return first-page results
 Run with:
  python -m app.mcp.server
  (from /Library/Development/CircuitForge/kiwi with cf conda env active)
 Configure in Claude Code ~/.claude/settings.json mcpServers:
  "kiwi": {
    "command": "/devl/miniconda3/envs/cf/bin/python",
    "args": ["-m", "app.mcp.server"],
    "cwd": "/Library/Development/CircuitForge/kiwi",
    "env": {
      "KIWI_DB_PATH": "/Library/Development/CircuitForge/kiwi/data/kiwi.db",
      "KIWI_API_URL": "http://localhost:8512"
    }
  }
 """
 from __future__ import annotations
 import asyncio
 import json
 import os
 import sqlite3
 from pathlib import Path
 import httpx
 from mcp.server import Server
 from mcp.server.stdio import stdio_server
 from mcp.types import TextContent, Tool
 _DB_PATH = os.environ.get(
    "KIWI_DB_PATH",
    str(Path(__file__).parents[3] / "data" / "kiwi.db"),
 )
 _API_URL = os.environ.get("KIWI_API_URL", "http://localhost:8512")
 _TIMEOUT = 30.0
 _QUERY_ROW_LIMIT = 200
 server = Server("kiwi")
 def _open_ro() -> sqlite3.Connection:
    """Open the corpus DB in read-only mode."""
    uri = f"file:///{Path(_DB_PATH).as_posix()}?mode=ro"
    conn = sqlite3.connect(uri, uri=True, check_same_thread=False)
    conn.row_factory = sqlite3.Row
    return conn
@server.list_tools()
 async def list_tools() -> list[Tool]:
    return [
        Tool(
            name="kiwi_query_corpus",
            description=(
                "Run a read-only SQL SELECT query against the Kiwi corpus DB (kiwi.db). "
                "Returns up to 200 rows as a JSON array. "
                "Key tables: recipes (id, title, ingredient_names, inferred_tags, source_url), "
                "recipes_fts (FTS5 virtual table for full-text search), "
                "ingredient_profiles (name, elements, texture_profile). "
                "Use for schema exploration, spot-checking tag coverage, and counting results. "
                "Read-only — any write statement will be rejected by SQLite."
            ),
            inputSchema={
                "type": "object",
                "required": ["sql"],
                "properties": {
                    "sql": {
                        "type": "string",
                        "description": (
                            "A SELECT statement. E.g.: "
                            "SELECT title, inferred_tags FROM recipes WHERE inferred_tags LIKE '%vegan%' LIMIT 10"
                        ),
                    },
                },
            },
        ),
        Tool(
            name="kiwi_count_fts",
            description=(
                "Run an FTS5 MATCH expression against the recipes_fts table and return the hit count. "
                "Useful for quickly auditing keyword coverage without a full query. "
                "Always double-quote all terms in MATCH expressions. "
                "E.g. match_expr='\"tofu\" OR \"tempeh\"' returns how many recipes include either."
            ),
            inputSchema={
                "type": "object",
                "required": ["match_expr"],
                "properties": {
                    "match_expr": {
                        "type": "string",
                        "description": (
                            "FTS5 MATCH expression string (without the MATCH keyword). "
                            'E.g. \'"lentil" OR "chickpea"\' or \'"pasta" AND "vegetarian"\''
                        ),
                    },
                },
            },
        ),
        Tool(
            name="kiwi_sample_tags",
            description=(
                "Return tag frequency distribution from the corpus. "
                "Queries inferred_tags column for tags matching the given prefix pattern. "
                "Useful for auditing how well a category keyword set covers the corpus, "
                "or discovering what tags exist under a domain (cuisine:, meal:, dietary:, texture:)."
            ),
            inputSchema={
                "type": "object",
                "properties": {
                    "prefix": {
                        "type": "string",
                        "default": "",
                        "description": (
                            "Tag prefix to filter by. E.g. 'cuisine:' returns all cuisine tags, "
                            "'meal:' returns all meal type tags, '' returns all tags. "
                            "Returns top 50 by frequency."
                        ),
                    },
                    "limit": {
                        "type": "integer",
                        "default": 50,
                        "description": "Max number of tag entries to return (default 50, max 200).",
                    },
                },
            },
        ),
        Tool(
            name="kiwi_browse_preview",
            description=(
                "Call the Kiwi browse endpoint and return first-page results. "
                "Use to verify that a domain/category returns the expected recipes "
                "after a keyword or tag change, without opening the browser. "
                "Returns recipe titles, match counts, and total result count."
            ),
            inputSchema={
                "type": "object",
                "required": ["domain", "category"],
                "properties": {
                    "domain": {
                        "type": "string",
                        "description": (
                            "Browse domain slug. "
                            "Known domains: cuisine, meal_type, dietary, ingredient, occasion, texture."
                        ),
                    },
                    "category": {
                        "type": "string",
                        "description": "Category slug within the domain, e.g. 'italian', 'breakfast', 'vegan'.",
                    },
                    "subcategory": {
                        "type": "string",
                        "default": "",
                        "description": "Optional subcategory slug to narrow further.",
                    },
                    "page_size": {
                        "type": "integer",
                        "default": 10,
                        "description": "Results per page (default 10, max 50).",
                    },
                },
            },
        ),
    ]
@server.call_tool()
 async def call_tool(name: str, arguments: dict) -> list[TextContent]:
    if name == "kiwi_query_corpus":
        return await _query_corpus(arguments)
    if name == "kiwi_count_fts":
        return await _count_fts(arguments)
    if name == "kiwi_sample_tags":
        return await _sample_tags(arguments)
    if name == "kiwi_browse_preview":
        return await _browse_preview(arguments)
    return [TextContent(type="text", text=f"Unknown tool: {name}")]
 async def _query_corpus(args: dict) -> list[TextContent]:
    sql = args.get("sql", "").strip()
    if not sql.upper().startswith("SELECT"):
        return [TextContent(type="text", text="Error: only SELECT statements are allowed.")]
    def _run() -> list[dict]:
        conn = _open_ro()
        try:
            cur = conn.execute(sql)
            rows = cur.fetchmany(_QUERY_ROW_LIMIT)
            return [dict(r) for r in rows]
        finally:
            conn.close()
    try:
        rows = await asyncio.get_event_loop().run_in_executor(None, _run)
        return [TextContent(type="text", text=json.dumps(rows, indent=2, default=str))]
    except Exception as exc:
        return [TextContent(type="text", text=f"Query error: {exc}")]
 async def _count_fts(args: dict) -> list[TextContent]:
    match_expr = args.get("match_expr", "").strip()
    if not match_expr:
        return [TextContent(type="text", text="Error: match_expr is required.")]
    def _run() -> int:
        conn = _open_ro()
        try:
            cur = conn.execute(
                "SELECT COUNT(*) FROM recipes_fts WHERE recipes_fts MATCH ?",
                (match_expr,),
            )
            return cur.fetchone()[0]
        finally:
            conn.close()
    try:
        count = await asyncio.get_event_loop().run_in_executor(None, _run)
        return [TextContent(type="text", text=json.dumps({"match_expr": match_expr, "count": count}))]
    except Exception as exc:
        return [TextContent(type="text", text=f"FTS error: {exc}")]
 async def _sample_tags(args: dict) -> list[TextContent]:
    prefix = args.get("prefix", "")
    limit = min(int(args.get("limit", 50)), _QUERY_ROW_LIMIT)
    def _run() -> list[dict]:
        conn = _open_ro()
        try:
            # Split inferred_tags (comma or space separated) and count each tag
            sql = """
                WITH tag_rows AS (
                    SELECT trim(value) AS tag
                    FROM recipes, json_each('["' || replace(replace(inferred_tags, ', ', '","'), ',', '","') || '"]')
                    WHERE inferred_tags IS NOT NULL AND inferred_tags != ''
                )
                SELECT tag, COUNT(*) AS frequency
                FROM tag_rows
                WHERE tag LIKE ? AND tag != ''
                GROUP BY tag
                ORDER BY frequency DESC
                LIMIT ?
            """
            pattern = f"{prefix}%" if prefix else "%"
            cur = conn.execute(sql, (pattern, limit))
            return [{"tag": r["tag"], "frequency": r["frequency"]} for r in cur.fetchall()]
        finally:
            conn.close()
    try:
        tags = await asyncio.get_event_loop().run_in_executor(None, _run)
        return [TextContent(type="text", text=json.dumps({"prefix": prefix, "tags": tags}, indent=2))]
    except Exception as exc:
        return [TextContent(type="text", text=f"Tag query error: {exc}")]
 async def _browse_preview(args: dict) -> list[TextContent]:
    domain = args.get("domain", "")
    category = args.get("category", "")
    subcategory = args.get("subcategory", "")
    page_size = min(int(args.get("page_size", 10)), 50)
    params: dict = {"page": 1, "page_size": page_size}
    if subcategory:
        params["subcategory"] = subcategory
    async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
        try:
            resp = await client.get(
                f"{_API_URL}/api/v1/recipes/browse/{domain}/{category}",
                params=params,
            )
            resp.raise_for_status()
        except Exception as exc:
            return [TextContent(type="text", text=f"Browse error: {exc}")]
    data = resp.json()
    summary = {
        "domain": domain,
        "category": category,
        "subcategory": subcategory or None,
        "total": data.get("total", 0),
        "page_size": page_size,
        "titles": [r.get("title", "") for r in data.get("recipes", [])],
    }
    return [TextContent(type="text", text=json.dumps(summary, indent=2))]
 async def _main() -> None:
    async with stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            server.create_initialization_options(),
        )
 if __name__ == "__main__":
    asyncio.run(_main())
--- a/app/models/schemas/inventory.py
+++ b/app/models/schemas/inventory.py
@ -122,7 +122,6 @@ class InventoryItemResponse(BaseModel):
    secondary_state: Optional[str] = None
    secondary_uses: Optional[List[str]] = None
    secondary_warning: Optional[str] = None
    secondary_discard_signs: Optional[str] = None
    status: str
    notes: Optional[str]
    disposal_reason: Optional[str] = None
@ -142,7 +141,6 @@ class BarcodeScanResult(BaseModel):
    inventory_item: Optional[InventoryItemResponse]
    added_to_inventory: bool
    needs_manual_entry: bool = False
    needs_visual_capture: bool = False  # Paid tier offer when no product data found
    message: str
--- a/app/models/schemas/label_capture.py
+++ b/app/models/schemas/label_capture.py
@ -1,59 +0,0 @@
 """Pydantic schemas for visual label capture (kiwi#79)."""
 from __future__ import annotations
 from typing import Any, Dict, List, Optional
 from pydantic import BaseModel, Field
 class LabelCaptureResponse(BaseModel):
    """Extraction result returned after the user photographs a nutrition label."""
    barcode: str
    product_name: Optional[str] = None
    brand: Optional[str] = None
    serving_size_g: Optional[float] = None
    calories: Optional[float] = None
    fat_g: Optional[float] = None
    saturated_fat_g: Optional[float] = None
    carbs_g: Optional[float] = None
    sugar_g: Optional[float] = None
    fiber_g: Optional[float] = None
    protein_g: Optional[float] = None
    sodium_mg: Optional[float] = None
    ingredient_names: List[str] = Field(default_factory=list)
    allergens: List[str] = Field(default_factory=list)
    confidence: float = 0.0
    needs_review: bool = True   # True when confidence < REVIEW_THRESHOLD
 class LabelConfirmRequest(BaseModel):
    """User-confirmed extraction to save to the local product cache."""
    barcode: str
    product_name: Optional[str] = None
    brand: Optional[str] = None
    serving_size_g: Optional[float] = None
    calories: Optional[float] = None
    fat_g: Optional[float] = None
    saturated_fat_g: Optional[float] = None
    carbs_g: Optional[float] = None
    sugar_g: Optional[float] = None
    fiber_g: Optional[float] = None
    protein_g: Optional[float] = None
    sodium_mg: Optional[float] = None
    ingredient_names: List[str] = Field(default_factory=list)
    allergens: List[str] = Field(default_factory=list)
    confidence: float = 0.0
    # When True the confirmed product is also added to inventory
    location: str = "pantry"
    quantity: float = 1.0
    auto_add: bool = True
 class LabelConfirmResponse(BaseModel):
    """Result of confirming a captured product."""
    ok: bool
    barcode: str
    product_id: Optional[int] = None
    inventory_item_id: Optional[int] = None
    message: str
--- a/app/models/schemas/recipe.py
+++ b/app/models/schemas/recipe.py
@ -4,36 +4,6 @@ from __future__ import annotations
 from pydantic import BaseModel, Field
 class LeftoversResponse(BaseModel):
    """Cooked-leftover shelf-life estimate returned by POST /recipes/{id}/leftovers."""
    fridge_days: int
    freeze_days: int | None = None   # None = not recommended
    freeze_by_day: int | None = None  # day number from cook date to freeze by
    storage_advice: str
 class StepAnalysis(BaseModel):
    """Active/passive classification for one direction step."""
    is_passive: bool
    detected_minutes: int | None = None
    prep_min: int | None = None   # estimated physical prep time (action detection)
 class TimeEffortProfile(BaseModel):
    """Parsed time and effort profile for a recipe.
    Mirrors app.services.recipe.time_effort.TimeEffortProfile (dataclass).
    Serialised into RecipeSuggestion so the frontend can render the effort
    summary without a second round-trip.
    """
    active_min: int = 0
    passive_min: int = 0
    total_min: int = 0
    effort_label: str = "moderate"   # "quick" | "moderate" | "involved"
    equipment: list[str] = Field(default_factory=list)
    step_analyses: list[StepAnalysis] = Field(default_factory=list)
 class SwapCandidate(BaseModel):
    original_name: str
    substitute_name: str
@ -73,8 +43,6 @@ class RecipeSuggestion(BaseModel):
    source_url: str | None = None
    complexity: str | None = None  # 'easy' | 'moderate' | 'involved'
    estimated_time_min: int | None = None  # derived from step count + method signals
    time_effort: TimeEffortProfile | None = None  # full time/effort profile from parse_time_effort
    rerank_score: float | None = None  # cross-encoder relevance score (paid+ only, None for free tier)
 class GroceryLink(BaseModel):
@ -93,18 +61,6 @@ class RecipeResult(BaseModel):
    orch_fallback: bool = False  # True when orch budget exhausted; fell back to local LLM
 class RecipeJobQueued(BaseModel):
    job_id: str
    status: str = "queued"
 class RecipeJobStatus(BaseModel):
    job_id: str
    status: str
    result: RecipeResult | None = None
    error: str | None = None
 class NutritionFilters(BaseModel):
    """Optional per-serving upper bounds for macro filtering. None = no filter."""
    max_calories: float | None = None
@ -115,10 +71,6 @@ class NutritionFilters(BaseModel):
 class RecipeRequest(BaseModel):
    pantry_items: list[str]
    # Maps product name → secondary state label for items past nominal expiry
    # but still within their secondary use window (e.g. {"Bread": "stale"}).
    # Used by the recipe engine to boost recipes suited to those specific states.
    secondary_pantry_items: dict[str, str] = Field(default_factory=dict)
    level: int = Field(default=1, ge=1, le=4)
    constraints: list[str] = Field(default_factory=list)
    expiry_first: bool = False
@ -132,13 +84,10 @@ class RecipeRequest(BaseModel):
    allergies: list[str] = Field(default_factory=list)
    nutrition_filters: NutritionFilters = Field(default_factory=NutritionFilters)
    excluded_ids: list[int] = Field(default_factory=list)
    exclude_ingredients: list[str] = Field(default_factory=list)
    shopping_mode: bool = False
    pantry_match_only: bool = False  # when True, only return recipes with zero missing ingredients
    complexity_filter: str | None = None  # 'easy' | 'moderate' | 'involved' — None = any
    max_time_min: int | None = None  # filter by estimated cooking time ceiling
    max_total_min: int | None = None  # filter by parsed total time (active + passive)
    max_active_min: int | None = None  # filter by hands-on active time only
    unit_system: str = "metric"  # "metric" | "imperial"
@ -185,45 +134,3 @@ class BuildRequest(BaseModel):
    template_id: str
    role_overrides: dict[str, str] = Field(default_factory=dict)
 class StreamTokenRequest(BaseModel):
    """Request body for POST /recipes/stream-token.
    Pantry items and dietary constraints are fetched from the DB at request
    time — the client does not supply them here.
    """
    level: int = Field(4, ge=3, le=4, description="Recipe level: 3=styled, 4=wildcard")
    wildcard_confirmed: bool = Field(False, description="Required true for level 4")
 class StreamTokenResponse(BaseModel):
    """Response from POST /recipes/stream-token.
    The frontend opens EventSource at stream_url?token=<token> to receive
    SSE chunks directly from the coordinator.
    """
    stream_url: str
    token: str
    expires_in_s: int
 class AskRequest(BaseModel):
    """Request body for POST /recipes/ask."""
    question: str = Field(min_length=1, max_length=500)
    pantry_items: list[str] = Field(default_factory=list)
 class AskRecipeHit(BaseModel):
    """A single recipe result from the Ask endpoint."""
    id: int
    title: str
    match_pct: float | None = None
    category: str | None = None
 class AskResponse(BaseModel):
    """Response from POST /recipes/ask."""
    answer: str | None = None  # LLM-synthesized response (Paid tier only)
    recipes: list[AskRecipeHit]
    tier: str
--- a/app/models/schemas/recipe_scan.py
+++ b/app/models/schemas/recipe_scan.py
@ -1,74 +0,0 @@
 """Pydantic schemas for the recipe scanner (kiwi#9).
 Scan input → photo(s).
 Scan output → ScannedRecipeResponse (for review + editing before save).
 Save input → ScannedRecipeSaveRequest.
 User recipe output → UserRecipeResponse (after save).
 """
 from __future__ import annotations
 from pydantic import BaseModel, Field
 # ── Ingredient in a scanned recipe ────────────────────────────────────────────
 class ScannedIngredientSchema(BaseModel):
    """One ingredient line extracted from a recipe photo."""
    name: str                      # normalized generic name ("ranch dressing")
    qty: str | None = None         # quantity as string, preserving fractions ("1/2", "¼")
    unit: str | None = None        # unit of measure; null for countable items
    raw: str | None = None         # verbatim original line from the image
    in_pantry: bool = False        # True if this ingredient matches something in the pantry
 # ── Scan response (returned immediately, not persisted) ───────────────────────
 class ScannedRecipeResponse(BaseModel):
    """Structured recipe extracted from photo(s). Returned for user review before save."""
    title: str | None = None
    subtitle: str | None = None    # e.g. "with Broccoli & Ranch Dressing"
    servings: str | None = None    # kept as string: "2", "4-6", "serves 8"
    cook_time: str | None = None   # kept as string: "25 min", "1 hour"
    source_note: str | None = None # e.g. "Purple Carrot", "Betty Crocker"
    ingredients: list[ScannedIngredientSchema] = Field(default_factory=list)
    steps: list[str] = Field(default_factory=list)
    notes: str | None = None
    tags: list[str] = Field(default_factory=list)
    pantry_match_pct: int = 0      # 0-100: percentage of ingredients found in pantry
    confidence: str = "medium"     # "high" | "medium" | "low"
    warnings: list[str] = Field(default_factory=list)
 # ── Save request ──────────────────────────────────────────────────────────────
 class ScannedRecipeSaveRequest(BaseModel):
    """User-reviewed (possibly edited) recipe data to persist as a user recipe."""
    title: str
    subtitle: str | None = None
    servings: str | None = None
    cook_time: str | None = None
    source_note: str | None = None
    ingredients: list[ScannedIngredientSchema]
    steps: list[str]
    notes: str | None = None
    tags: list[str] = Field(default_factory=list)
    source: str = "scan"           # "scan" | "manual"
 # ── User recipe (persisted) ───────────────────────────────────────────────────
 class UserRecipeResponse(BaseModel):
    """A user-created or user-scanned recipe stored in user_recipes table."""
    id: int
    title: str
    subtitle: str | None = None
    servings: str | None = None
    cook_time: str | None = None
    source_note: str | None = None
    ingredients: list[ScannedIngredientSchema]
    steps: list[str]
    notes: str | None = None
    tags: list[str] = Field(default_factory=list)
    source: str
    pantry_match_pct: int | None = None
    created_at: str
--- a/app/services/init.py
+++ b/app/services/init.py
@ -3,11 +3,6 @@
 Business logic services for Kiwi.
 """
 from app.services.receipt_service import ReceiptService
 __all__ = ["ReceiptService"]
 def __getattr__(name: str):
    if name == "ReceiptService":
        from app.services.receipt_service import ReceiptService
        return ReceiptService
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
--- a/app/services/ap/init.py
+++ b/app/services/ap/init.py
--- a/app/services/ap/delivery.py
+++ b/app/services/ap/delivery.py
@ -1,115 +0,0 @@
 # app/services/ap/delivery.py
 # MIT License
 from __future__ import annotations
 import logging
 import time
 from datetime import datetime, timezone
 from pathlib import Path
 from circuitforge_core.activitypub import deliver_activity
 from app.services.ap.keys import get_actor
 logger = logging.getLogger(__name__)
 _RETRIES = 3
 _BACKOFF = [1.0, 4.0, 16.0]
 def deliver_to_followers(post_slug: str, activity: dict, db_path: Path) -> None:
    """Deliver an AP activity to all active followers. Called as a background task.
    Retries each inbox up to 3 times with exponential backoff.
    Logs each attempt to ap_deliveries in the local kiwi.db.
    """
    actor = get_actor()
    if actor is None:
        return
    import sqlite3
    conn = sqlite3.connect(str(db_path))
    conn.row_factory = sqlite3.Row
    try:
        followers = conn.execute(
            "SELECT inbox_url, shared_inbox FROM ap_followers WHERE active = 1"
        ).fetchall()
    finally:
        conn.close()
    # Deduplicate by shared_inbox where available
    inboxes: set[str] = set()
    for row in followers:
        inbox = row["shared_inbox"] or row["inbox_url"]
        inboxes.add(inbox)
    for inbox_url in inboxes:
        _deliver_with_retry(post_slug=post_slug, activity=activity, inbox_url=inbox_url, db_path=db_path)
 def _deliver_with_retry(
    post_slug: str,
    activity: dict,
    inbox_url: str,
    db_path: Path,
 ) -> None:
    actor = get_actor()
    if actor is None:
        return
    import sqlite3
    conn = sqlite3.connect(str(db_path))
    try:
        conn.execute(
            "INSERT OR IGNORE INTO ap_deliveries (post_slug, target_inbox, status) VALUES (?,?,?)",
            (post_slug, inbox_url, "pending"),
        )
        conn.commit()
    finally:
        conn.close()
    last_error: str | None = None
    for attempt, delay in enumerate(_BACKOFF[:_RETRIES]):
        try:
            resp = deliver_activity(activity=activity, inbox_url=inbox_url, actor=actor, timeout=10.0)
            if resp.status_code < 300:
                _update_delivery(db_path, post_slug, inbox_url, "delivered", None)
                return
            last_error = f"HTTP {resp.status_code}"
        except Exception as exc:
            last_error = str(exc)[:200]
        if attempt < _RETRIES - 1:
            time.sleep(delay)
    _update_delivery(db_path, post_slug, inbox_url, "failed", last_error)
    logger.warning("AP delivery failed after %d attempts to %s: %s", _RETRIES, inbox_url, last_error)
 def _update_delivery(
    db_path: Path,
    post_slug: str,
    inbox_url: str,
    status: str,
    error: str | None,
 ) -> None:
    import sqlite3
    now = datetime.now(timezone.utc).isoformat()
    conn = sqlite3.connect(str(db_path))
    try:
        if status == "delivered":
            conn.execute(
                """UPDATE ap_deliveries SET status=?, attempts=attempts+1, delivered_at=?
                   WHERE post_slug=? AND target_inbox=?""",
                (status, now, post_slug, inbox_url),
            )
        else:
            conn.execute(
                """UPDATE ap_deliveries SET status=?, attempts=attempts+1, last_error=?
                   WHERE post_slug=? AND target_inbox=?""",
                (status, error, post_slug, inbox_url),
            )
        conn.commit()
    finally:
        conn.close()
--- a/app/services/ap/keys.py
+++ b/app/services/ap/keys.py
@ -1,48 +0,0 @@
 # app/services/ap/keys.py
 # MIT License
 from __future__ import annotations
 import logging
 from pathlib import Path
 from circuitforge_core.activitypub import CFActor, generate_rsa_keypair, load_actor_from_key_file
 logger = logging.getLogger(__name__)
 _actor: CFActor | None = None
 def get_actor() -> CFActor | None:
    """Return the loaded instance actor, or None if AP is not enabled."""
    return _actor
 def init_actor(host: str, key_path: Path) -> CFActor:
    """Load or generate the instance RSA keypair and build the CFActor singleton.
    Called once at startup when AP_ENABLED=true. Generates a new 2048-bit keypair
    if the key file does not yet exist (first boot).
    """
    global _actor
    key_path.parent.mkdir(parents=True, exist_ok=True)
    if not key_path.exists():
        logger.info("AP: no key file found at %s — generating new RSA-2048 keypair", key_path)
        private_pem, _pub = generate_rsa_keypair(bits=2048)
        key_path.write_text(private_pem, encoding="utf-8")
        key_path.chmod(0o600)
    base = f"https://{host}"
    actor_id = f"{base}/ap/actor"
    _actor = load_actor_from_key_file(
        actor_id=actor_id,
        username="kiwi",
        display_name="Kiwi Pantry",
        private_key_path=str(key_path),
        summary="Community pantry and recipe feed from a Kiwi instance.",
    )
    logger.info("AP: instance actor loaded — %s", actor_id)
    return _actor
--- a/app/services/ap/mastodon.py
+++ b/app/services/ap/mastodon.py
@ -1,194 +0,0 @@
 # app/services/ap/mastodon.py
 # MIT License
 from __future__ import annotations
 import logging
 from pathlib import Path
 import httpx
 logger = logging.getLogger(__name__)
 _APP_SCOPES = "write:statuses"
 _APP_NAME = "Kiwi Pantry"
 _APP_WEBSITE = "https://circuitforge.tech/kiwi"
 def register_app(instance_url: str, redirect_uri: str) -> dict:
    """Dynamically register Kiwi as an OAuth app on the user's Mastodon instance.
    Returns the app credentials dict (client_id, client_secret, etc.).
    Raises httpx.HTTPError on failure.
    """
    url = instance_url.rstrip("/") + "/api/v1/apps"
    resp = httpx.post(
        url,
        data={
            "client_name": _APP_NAME,
            "redirect_uris": redirect_uri,
            "scopes": _APP_SCOPES,
            "website": _APP_WEBSITE,
        },
        timeout=10.0,
    )
    resp.raise_for_status()
    return resp.json()
 def build_authorize_url(instance_url: str, client_id: str, redirect_uri: str) -> str:
    """Return the OAuth authorize URL to redirect the user to."""
    return (
        f"{instance_url.rstrip('/')}/oauth/authorize"
        f"?response_type=code"
        f"&client_id={client_id}"
        f"&redirect_uri={redirect_uri}"
        f"&scope={_APP_SCOPES}"
    )
 def exchange_code(
    instance_url: str,
    client_id: str,
    client_secret: str,
    code: str,
    redirect_uri: str,
 ) -> str:
    """Exchange an authorization code for an access token. Returns the token string."""
    url = instance_url.rstrip("/") + "/oauth/token"
    resp = httpx.post(
        url,
        data={
            "grant_type": "authorization_code",
            "client_id": client_id,
            "client_secret": client_secret,
            "redirect_uri": redirect_uri,
            "code": code,
            "scope": _APP_SCOPES,
        },
        timeout=10.0,
    )
    resp.raise_for_status()
    return resp.json()["access_token"]
 def post_status(instance_url: str, access_token: str, content: str) -> dict:
    """Post a status to the user's Mastodon account. Returns the status response dict."""
    url = instance_url.rstrip("/") + "/api/v1/statuses"
    resp = httpx.post(
        url,
        headers={"Authorization": f"Bearer {access_token}"},
        json={"status": content, "visibility": "public"},
        timeout=15.0,
    )
    resp.raise_for_status()
    return resp.json()
 def build_post_content(post: dict) -> str:
    """Format a community post dict as Mastodon-ready plain text."""
    title = post.get("title") or "Untitled"
    recipe = post.get("recipe_name")
    notes = post.get("outcome_notes") or post.get("description")
    tags_raw: list[str] = post.get("dietary_tags") or []
    lines = []
    if recipe and recipe != title:
        lines.append(f"🍽 {title} — {recipe}")
    else:
        lines.append(f"🍽 {title}")
    if notes:
        snippet = notes[:200].strip()
        if len(notes) > 200:
            snippet += "…"
        lines.append(f"\n{snippet}")
    hashtags = ["#Kiwi", "#Cooking"]
    for tag in tags_raw[:3]:
        ht = "#" + "".join(w.capitalize() for w in tag.replace("-", " ").split())
        hashtags.append(ht)
    lines.append("\n" + " ".join(hashtags))
    return "\n".join(lines)
 def store_token(
    db_path: Path,
    directus_user_id: str,
    instance_url: str,
    access_token: str,
    encryption_key: str | None,
 ) -> None:
    """Persist a Mastodon access token in the user's local kiwi.db."""
    token_to_store = _encrypt(access_token, encryption_key)
    import sqlite3
    conn = sqlite3.connect(str(db_path))
    try:
        conn.execute(
            """INSERT INTO mastodon_tokens (directus_user_id, instance_url, access_token)
               VALUES (?, ?, ?)
               ON CONFLICT(directus_user_id) DO UPDATE SET
                 instance_url=excluded.instance_url,
                 access_token=excluded.access_token,
                 updated_at=datetime('now')""",
            (directus_user_id, instance_url.rstrip("/"), token_to_store),
        )
        conn.commit()
    finally:
        conn.close()
 def get_token(
    db_path: Path,
    directus_user_id: str,
    encryption_key: str | None,
 ) -> tuple[str, str] | None:
    """Return (instance_url, plaintext_access_token) or None if not connected."""
    import sqlite3
    conn = sqlite3.connect(str(db_path))
    try:
        row = conn.execute(
            "SELECT instance_url, access_token FROM mastodon_tokens WHERE directus_user_id = ?",
            (directus_user_id,),
        ).fetchone()
    finally:
        conn.close()
    if row is None:
        return None
    return row[0], _decrypt(row[1], encryption_key)
 def delete_token(db_path: Path, directus_user_id: str) -> None:
    """Remove the user's stored Mastodon token."""
    import sqlite3
    conn = sqlite3.connect(str(db_path))
    try:
        conn.execute(
            "DELETE FROM mastodon_tokens WHERE directus_user_id = ?", (directus_user_id,)
        )
        conn.commit()
    finally:
        conn.close()
 def _encrypt(plaintext: str, key: str | None) -> str:
    if key is None:
        return plaintext
    try:
        from cryptography.fernet import Fernet
        return Fernet(key.encode()).encrypt(plaintext.encode()).decode()
    except Exception:
        logger.warning("Mastodon token encryption failed — storing plaintext")
        return plaintext
 def _decrypt(ciphertext: str, key: str | None) -> str:
    if key is None:
        return ciphertext
    try:
        from cryptography.fernet import Fernet
        return Fernet(key.encode()).decrypt(ciphertext.encode()).decode()
    except Exception:
        logger.warning("Mastodon token decryption failed — returning as-is")
        return ciphertext
--- a/app/services/community/dedup.py
+++ b/app/services/community/dedup.py
@ -1,111 +0,0 @@
 # app/services/community/dedup.py
 # MIT License
 from __future__ import annotations
 import json
 import logging
 from pathlib import Path
 logger = logging.getLogger(__name__)
 _SIMILARITY_TIERS = {
    "exact_recipe": "This exact recipe is already in the community feed.",
    "very_similar": "Very similar recipes already exist (70%+ ingredient overlap).",
    "somewhat_similar": "Somewhat similar recipes exist (35-70% ingredient overlap).",
    "different": "No close matches found.",
 }
 def _parse_ingredient_names(raw) -> set[str]:
    """Return a normalised set of ingredient name tokens from various stored formats."""
    if raw is None:
        return set()
    if isinstance(raw, str):
        try:
            raw = json.loads(raw)
        except (ValueError, TypeError):
            return set()
    names: set[str] = set()
    for item in raw:
        if isinstance(item, str):
            names.add(item.lower().strip())
        elif isinstance(item, dict):
            name = item.get("name") or item.get("ingredient") or ""
            if name:
                names.add(name.lower().strip())
    return names
 def jaccard(a: set[str], b: set[str]) -> float:
    if not a and not b:
        return 1.0
    if not a or not b:
        return 0.0
    return len(a & b) / len(a | b)
 def similarity_tier(jaccard_score: float, exact_recipe: bool) -> str:
    if exact_recipe:
        return "exact_recipe"
    if jaccard_score >= 0.70:
        return "very_similar"
    if jaccard_score >= 0.35:
        return "somewhat_similar"
    return "different"
 def fetch_recipe_ingredients(db_path: Path, recipe_id: int | None) -> set[str]:
    """Look up ingredient names for a recipe from the local corpus. Returns empty set on miss."""
    if recipe_id is None:
        return set()
    try:
        from app.db.store import Store
        store = Store(db_path)
        try:
            row = store.get_recipe(recipe_id)
            if row is None:
                return set()
            return _parse_ingredient_names(row.get("ingredient_names"))
        finally:
            store.close()
    except Exception:
        logger.debug("ingredient lookup failed for recipe_id=%s", recipe_id)
        return set()
 def build_similar_post_result(
    post,
    incoming_recipe_id: int | None,
    incoming_ingredients: set[str],
    db_path: Path,
 ) -> dict:
    """Build a similarity result dict for one existing community post."""
    exact = (
        incoming_recipe_id is not None
        and post.recipe_id is not None
        and post.recipe_id == incoming_recipe_id
    )
    j_score = 0.0
    if not exact and incoming_ingredients:
        existing_ingredients = fetch_recipe_ingredients(db_path, post.recipe_id)
        if existing_ingredients:
            j_score = jaccard(incoming_ingredients, existing_ingredients)
    tier = similarity_tier(j_score, exact)
    return {
        "slug": post.slug,
        "title": post.title,
        "recipe_name": post.recipe_name,
        "pseudonym": post.pseudonym,
        "published": (
            post.published.isoformat()
            if hasattr(post.published, "isoformat")
            else str(post.published)
        ),
        "similarity_tier": tier,
        "jaccard_score": round(j_score, 3) if not exact else None,
        "tier_description": _SIMILARITY_TIERS.get(tier, ""),
    }
--- a/app/services/coordinator_proxy.py
+++ b/app/services/coordinator_proxy.py
@ -1,94 +0,0 @@
 """cf-orch coordinator proxy client.
 Calls the coordinator's /proxy/authorize endpoint to obtain a one-time
 stream URL + token for LLM streaming. Always raises CoordinatorError on
 failure — callers decide how to handle it (stream-token endpoint returns
 503 or 403 as appropriate).
 """
 from __future__ import annotations
 import logging
 import os
 from dataclasses import dataclass
 import httpx
 log = logging.getLogger(__name__)
 class CoordinatorError(Exception):
    """Raised when the coordinator returns an error or is unreachable."""
    def __init__(self, message: str, status_code: int = 503):
        super().__init__(message)
        self.status_code = status_code
@dataclass(frozen=True)
 class StreamTokenResult:
    stream_url: str
    token: str
    expires_in_s: int
 def _coordinator_url() -> str:
    return os.environ.get("COORDINATOR_URL", "http://10.1.10.71:7700")
 def _product_key() -> str:
    return os.environ.get("COORDINATOR_KIWI_KEY", "")
 async def coordinator_authorize(
    prompt: str,
    caller: str = "kiwi-recipe",
    ttl_s: int = 300,
 ) -> StreamTokenResult:
    """Call POST /proxy/authorize on the coordinator.
    Returns a StreamTokenResult with the stream URL and one-time token.
    Raises CoordinatorError on any failure (network, auth, capacity).
    """
    url = f"{_coordinator_url()}/proxy/authorize"
    key = _product_key()
    if not key:
        raise CoordinatorError(
            "COORDINATOR_KIWI_KEY env var is not set — streaming unavailable",
            status_code=503,
        )
    payload = {
        "product": "kiwi",
        "product_key": key,
        "caller": caller,
        "prompt": prompt,
        "params": {},
        "ttl_s": ttl_s,
    }
    try:
        async with httpx.AsyncClient(timeout=10.0) as client:
            resp = await client.post(url, json=payload)
    except httpx.RequestError as exc:
        log.warning("coordinator_authorize network error: %s", exc)
        raise CoordinatorError(f"Coordinator unreachable: {exc}", status_code=503)
    if resp.status_code == 401:
        raise CoordinatorError("Invalid product key", status_code=401)
    if resp.status_code == 429:
        raise CoordinatorError("Too many concurrent streams", status_code=429)
    if resp.status_code == 503:
        raise CoordinatorError("No GPU available for streaming", status_code=503)
    if not resp.is_success:
        raise CoordinatorError(
            f"Coordinator error {resp.status_code}: {resp.text[:200]}",
            status_code=503,
        )
    data = resp.json()
    # Use public_stream_url if coordinator provides it (cloud mode), else stream_url
    stream_url = data.get("public_stream_url") or data["stream_url"]
    return StreamTokenResult(
        stream_url=stream_url,
        token=data["token"],
        expires_in_s=data["expires_in_s"],
    )
--- a/app/services/expiration_predictor.py
+++ b/app/services/expiration_predictor.py
@ -157,160 +157,42 @@ class ExpirationPredictor:
    # These are NOT spoilage extensions — they describe a qualitative state
    # change where the ingredient is specifically suited for certain preparations.
    # Sources: USDA FoodKeeper, food science, culinary tradition.
    #
    # Fields:
    #   window_days        — days past nominal expiry still usable in secondary state
    #   label              — short UI label for the state
    #   uses               — recipe contexts suited to this state (shown in UI)
    #   warning            — safety note, calm tone, None if none needed
    #   discard_signs      — qualitative signs the item has gone past the secondary window
    #   constraints_exclude — dietary constraint labels that suppress this entry entirely
    #                         (e.g. alcohol-containing items suppressed for halal/alcohol-free)
    SECONDARY_WINDOW: dict[str, dict] = {
        'bread': {
            'window_days': 5,
            'label': 'stale',
            'uses': ['croutons', 'stuffing', 'bread pudding', 'French toast', 'panzanella'],
            'warning': 'Check for mold before use — discard if any is visible.',
            'discard_signs': 'Visible mold (any colour), or unpleasant smell beyond dry/yeasty.',
            'constraints_exclude': [],
        },
        'bakery': {
            'window_days': 3,
            'label': 'day-old',
-            'uses': ['French toast', 'bread pudding', 'crumbles', 'trifle base', 'cake pops', 'streusel topping', 'bread crumbs'],
+            'uses': ['French toast', 'bread pudding', 'crumbles'],
            'warning': 'Check for mold before use — discard if any is visible.',
            'discard_signs': 'Visible mold, sliminess, or strong sour smell.',
            'constraints_exclude': [],
        },
        'bananas': {
            'window_days': 5,
            'label': 'overripe',
            'uses': ['banana bread', 'smoothies', 'pancakes', 'muffins'],
            'warning': None,
            'discard_signs': 'Leaking liquid, fermented smell, or mold on skin.',
            'constraints_exclude': [],
        },
        'milk': {
            'window_days': 3,
            'label': 'sour',
-            'uses': ['pancakes', 'scones', 'waffles', 'muffins', 'quick breads', 'béchamel', 'baked mac and cheese'],
+            'uses': ['pancakes', 'quick breads', 'baking', 'sauces'],
            'warning': 'Use only in cooked recipes — do not drink.',
            'discard_signs': 'Chunky texture, strong unpleasant smell beyond tangy, or visible separation with grey colour.',
            'constraints_exclude': [],
        },
        'dairy': {
            'window_days': 2,
            'label': 'sour',
-            'uses': ['pancakes', 'scones', 'quick breads', 'muffins', 'waffles'],
+            'uses': ['pancakes', 'quick breads', 'baking'],
            'warning': 'Use only in cooked recipes — do not drink.',
            'discard_signs': 'Strong unpleasant smell, unusual colour, or chunky texture.',
            'constraints_exclude': [],
        },
        'cheese': {
            'window_days': 14,
-            'label': 'rind-ready',
+            'label': 'well-aged',
-            'uses': ['parmesan broth', 'minestrone', 'ribollita', 'risotto', 'polenta', 'bean soups', 'gratins'],
+            'uses': ['broth', 'soups', 'risotto', 'gratins'],
            'warning': None,
            'discard_signs': 'Soft or wet texture on hard cheese, pink or black mold (white/green surface mold on hard cheese can be cut off with 1cm margin).',
            'constraints_exclude': [],
        },
        'rice': {
            'window_days': 2,
            'label': 'day-old',
            'uses': ['fried rice', 'onigiri', 'rice porridge', 'congee', 'arancini', 'stuffed peppers', 'rice fritters'],
            'warning': 'Refrigerate immediately after cooking — do not leave at room temp.',
            'discard_signs': 'Slimy texture, unusual smell, or more than 4 days since cooking.',
            'constraints_exclude': [],
        },
        'tortillas': {
            'window_days': 5,
            'label': 'stale',
            'uses': ['chilaquiles', 'migas', 'tortilla soup', 'casserole'],
            'warning': 'Check for mold, especially if stored in a sealed bag — discard if any is visible.',
            'discard_signs': 'Visible mold (check seams and edges), or strong sour smell.',
            'constraints_exclude': [],
        },
        # ── New entries ──────────────────────────────────────────────────────
        'apples': {
            'window_days': 7,
            'label': 'soft',
            'uses': ['applesauce', 'apple butter', 'baked apples', 'apple crisp', 'smoothies', 'chutney'],
            'warning': None,
            'discard_signs': 'Large bruised areas with fermented smell, visible mold, or liquid leaking from skin.',
            'constraints_exclude': [],
        },
        'leafy_greens': {
            'window_days': 2,
            'label': 'wilting',
            'uses': ['sautéed greens', 'soups', 'smoothies', 'frittata', 'pasta add-in', 'stir fry'],
            'warning': None,
            'discard_signs': 'Slimy texture, strong unpleasant smell, or yellowed and mushy leaves.',
            'constraints_exclude': [],
        },
        'tomatoes': {
            'window_days': 4,
            'label': 'soft',
            'uses': ['roasted tomatoes', 'tomato sauce', 'shakshuka', 'bruschetta', 'soup', 'salsa'],
            'warning': None,
            'discard_signs': 'Broken skin with liquid pooling, mold, or fermented smell.',
            'constraints_exclude': [],
        },
        'cooked_pasta': {
            'window_days': 3,
            'label': 'day-old',
            'uses': ['pasta frittata', 'pasta salad', 'baked pasta', 'soup add-in', 'fried pasta cakes'],
            'warning': 'Refrigerate within 2 hours of cooking.',
            'discard_signs': 'Slimy texture, off smell, or more than 4 days since cooking.',
            'constraints_exclude': [],
        },
        'cooked_potatoes': {
            'window_days': 3,
            'label': 'day-old',
            'uses': ['potato pancakes', 'hash browns', 'potato soup', 'gnocchi', 'twice-baked potatoes', 'croquettes'],
            'warning': 'Refrigerate within 2 hours of cooking.',
            'discard_signs': 'Slimy texture, off smell, or more than 4 days since cooking.',
            'constraints_exclude': [],
        },
        'yogurt': {
            'window_days': 7,
            'label': 'tangy',
            'uses': ['marinades', 'flatbreads', 'smoothies', 'tzatziki', 'baked goods', 'salad dressings'],
            'warning': None,
            'discard_signs': 'Pink or orange discolouration, visible mold, or strongly unpleasant smell (not just tangy).',
            'constraints_exclude': [],
        },
        'cream': {
            'window_days': 2,
            'label': 'sour',
            'uses': ['soups', 'sauces', 'scones', 'quick breads', 'mashed potatoes'],
            'warning': 'Use in cooked recipes only. Discard if the smell is strongly unpleasant rather than tangy.',
            'discard_signs': 'Strong unpleasant smell beyond tangy, unusual colour, or chunky texture.',
            'constraints_exclude': [],
        },
        'wine': {
            'window_days': 4,
            'label': 'open',
            'uses': ['pan sauces', 'braises', 'risotto', 'marinades', 'poaching liquid', 'wine reduction'],
            'warning': None,
            'discard_signs': 'Strong vinegar smell (still usable in braises/marinades), or visible cloudiness with off-smell.',
            'constraints_exclude': ['halal', 'alcohol-free'],
        },
        'cooked_beans': {
            'window_days': 3,
            'label': 'day-old',
            'uses': ['refried beans', 'bean soup', 'bean fritters', 'hummus', 'bean dip', 'grain bowls'],
            'warning': 'Refrigerate within 2 hours of cooking.',
            'discard_signs': 'Slimy texture, off smell, or more than 4 days since cooking.',
            'constraints_exclude': [],
        },
        'cooked_meat': {
            'window_days': 2,
            'label': 'leftover',
            'uses': ['grain bowls', 'tacos', 'soups', 'fried rice', 'sandwiches', 'hash', 'pasta add-in'],
            'warning': 'Refrigerate within 2 hours of cooking.',
            'discard_signs': 'Off smell, slimy texture, or more than 3–4 days since cooking.',
            'constraints_exclude': [],
        },
    }
@ -329,15 +211,10 @@ class ExpirationPredictor:
    ) -> dict | None:
        """Return secondary use info if the item is in its post-expiry secondary window.
-        Returns a dict with label, uses, warning, discard_signs, constraints_exclude,
+        Returns a dict with label, uses, warning, days_past, and window_days when the
-        days_past, and window_days when the item is past its nominal expiry date but
+        item is past its nominal expiry date but still within the secondary use window.
        still within the secondary use window.
        Returns None in all other cases (unknown category, no window defined, not yet
        expired, or past the secondary window).
        Callers should apply constraints_exclude against user dietary constraints
        and suppress the result entirely if any excluded constraint is active.
        See filter_secondary_by_constraints().
        """
        if not category or not expiry_date:
            return None
@ -354,8 +231,6 @@ class ExpirationPredictor:
                    'label': entry['label'],
                    'uses': list(entry['uses']),
                    'warning': entry['warning'],
                    'discard_signs': entry.get('discard_signs'),
                    'constraints_exclude': list(entry.get('constraints_exclude') or []),
                    'days_past': days_past,
                    'window_days': entry['window_days'],
                }
@ -363,23 +238,6 @@ class ExpirationPredictor:
            pass
        return None
    @staticmethod
    def filter_secondary_by_constraints(
        sec: dict | None,
        user_constraints: list[str],
    ) -> dict | None:
        """Suppress secondary state entirely if any excluded constraint is active.
        Call after secondary_state() when user dietary constraints are available.
        Returns sec unchanged when no constraints match, or None when suppressed.
        """
        if sec is None:
            return None
        excluded = sec.get('constraints_exclude') or []
        if any(c.lower() in [e.lower() for e in excluded] for c in user_constraints):
            return None
        return sec
    # Keyword lists are checked in declaration order — most specific first.
    # Rules:
    #  - canned/processed goods BEFORE raw-meat terms (canned chicken != raw chicken)
--- a/app/services/label_capture.py
+++ b/app/services/label_capture.py
@ -1,140 +0,0 @@
 """Visual label capture service for unenriched products (kiwi#79).
 Wraps the cf-core VisionRouter to extract structured nutrition data from a
 photographed nutrition facts panel.  When the VisionRouter is not yet wired
 (NotImplementedError) the service falls back to a mock extraction so the
 barcode scan flow can be exercised end-to-end in development.
 JSON contract returned by the vision model (and mock):
  {
    "product_name": str | null,
    "brand":        str | null,
    "serving_size_g": number | null,
    "calories":       number | null,
    "fat_g":          number | null,
    "saturated_fat_g": number | null,
    "carbs_g":        number | null,
    "sugar_g":        number | null,
    "fiber_g":        number | null,
    "protein_g":      number | null,
    "sodium_mg":      number | null,
    "ingredient_names": [str],
    "allergens":        [str],
    "confidence":       number (0.0–1.0)
  }
 """
 from __future__ import annotations
 import json
 import logging
 import os
 from typing import Any
 log = logging.getLogger(__name__)
 # Confidence below this threshold surfaces amber highlights in the UI.
 REVIEW_THRESHOLD = 0.7
 _MOCK_EXTRACTION: dict[str, Any] = {
    "product_name": "Unknown Product",
    "brand": None,
    "serving_size_g": None,
    "calories": None,
    "fat_g": None,
    "saturated_fat_g": None,
    "carbs_g": None,
    "sugar_g": None,
    "fiber_g": None,
    "protein_g": None,
    "sodium_mg": None,
    "ingredient_names": [],
    "allergens": [],
    "confidence": 0.0,
 }
 _EXTRACTION_PROMPT = """You are reading a nutrition facts label photograph.
 Extract the following fields as a JSON object with no extra text:
 {
  "product_name": <product name or null>,
  "brand": <brand name or null>,
  "serving_size_g": <serving size in grams as a number or null>,
  "calories": <calories per serving as a number or null>,
  "fat_g": <total fat grams or null>,
  "saturated_fat_g": <saturated fat grams or null>,
  "carbs_g": <total carbohydrates grams or null>,
  "sugar_g": <sugars grams or null>,
  "fiber_g": <dietary fiber grams or null>,
  "protein_g": <protein grams or null>,
  "sodium_mg": <sodium milligrams or null>,
  "ingredient_names": [list of individual ingredients as strings],
  "allergens": [list of allergens explicitly stated on label],
  "confidence": <your confidence this extraction is correct, 0.0 to 1.0>
 }
 Use null for any field you cannot read clearly. Do not guess values.
 Respond with JSON only."""
 def extract_label(image_bytes: bytes) -> dict[str, Any]:
    """Run vision model extraction on raw label image bytes.
    Returns a dict matching the nutrition JSON contract above.
    Falls back to a zero-confidence mock if the VisionRouter is not yet
    implemented (stub) or if the model returns unparseable output.
    """
    # Allow unit tests to bypass the vision model entirely.
    if os.environ.get("KIWI_LABEL_CAPTURE_MOCK") == "1":
        log.debug("label_capture: mock mode active")
        return dict(_MOCK_EXTRACTION)
    try:
        from circuitforge_core.vision import caption as vision_caption
        result = vision_caption(image_bytes, prompt=_EXTRACTION_PROMPT)
        raw = result.caption or ""
        return _parse_extraction(raw)
    except Exception as exc:
        log.warning("label_capture: extraction failed (%s) — returning mock extraction", exc)
        return dict(_MOCK_EXTRACTION)
 def _parse_extraction(raw: str) -> dict[str, Any]:
    """Parse the JSON string returned by the vision model.
    Strips markdown code fences if present.  Validates required shape.
    Returns the mock on any parse error.
    """
    text = raw.strip()
    if text.startswith("```"):
        # Strip ```json ... ``` fences
        lines = text.splitlines()
        text = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
    try:
        data = json.loads(text)
    except json.JSONDecodeError as exc:
        log.warning("label_capture: could not parse vision response: %s", exc)
        return dict(_MOCK_EXTRACTION)
    if not isinstance(data, dict):
        log.warning("label_capture: vision response is not a dict")
        return dict(_MOCK_EXTRACTION)
    # Normalise list fields — model may return None instead of []
    for list_key in ("ingredient_names", "allergens"):
        if not isinstance(data.get(list_key), list):
            data[list_key] = []
    # Clamp confidence to [0, 1]
    confidence = data.get("confidence")
    if not isinstance(confidence, (int, float)):
        confidence = 0.0
    data["confidence"] = max(0.0, min(1.0, float(confidence)))
    return data
 def needs_review(extraction: dict[str, Any]) -> bool:
    """Return True when the extraction confidence is below REVIEW_THRESHOLD."""
    return float(extraction.get("confidence", 0.0)) < REVIEW_THRESHOLD
--- a/app/services/leftovers_predictor.py
+++ b/app/services/leftovers_predictor.py
@ -1,233 +0,0 @@
 # app/services/leftovers_predictor.py
 """Cooked-leftovers shelf-life predictor.
 Fast path: deterministic lookup anchored to FDA/USDA safe food handling.
 Fallback: LLM for unclassifiable edge cases (same gate as expiry_llm_matching).
 Design notes:
  - shortest-component-wins for proteins: a fish taco is bounded by the fish.
  - category/keyword signals override ingredient signals for assembled dishes
    (soup, stew, casserole) where the cooking method matters more than the
    dominant protein.
  - no urgency/panic framing — see feedback_kiwi_no_panic.md.
 """
 from __future__ import annotations
 import logging
 import re
 from dataclasses import dataclass, field
 from typing import Any
 logger = logging.getLogger(__name__)
@dataclass
 class LeftoversResult:
    fridge_days: int
    freeze_days: int | None        # None = "not recommended"
    freeze_by_day: int | None      # day number from cook date to freeze by; None = no need
    storage_advice: str
 # ---------------------------------------------------------------------------
 # Protein priority table — shorter shelf life wins when multiple match.
 # Values: (fridge_days, freeze_days).  All fridge values are conservative.
 # Sources: USDA FoodKeeper, FDA Safe Food Handling.
 # ---------------------------------------------------------------------------
 _PROTEIN_SIGNALS: list[tuple[list[str], int, int | None]] = [
    # (keyword_list, fridge_days, freeze_days)
    (["fish", "salmon", "tuna", "cod", "tilapia", "halibut", "trout", "bass",
      "mahi", "snapper", "flounder", "catfish", "swordfish", "sardine", "anchovy"],
     2, 90),
    (["shrimp", "prawn", "scallop", "crab", "lobster", "clam", "mussel",
      "oyster", "squid", "octopus", "seafood"],
     2, 90),
    (["ground beef", "ground turkey", "ground pork", "ground chicken",
      "ground meat", "hamburger", "mince"],
     3, 90),
    (["chicken", "turkey", "poultry", "duck", "hen"],
     3, 90),
    (["pork", "ham", "bacon", "sausage", "chorizo", "bratwurst", "kielbasa",
      "salami", "pepperoni"],
     4, 120),
    (["beef", "steak", "brisket", "roast", "lamb", "veal", "venison"],
     4, 180),
    (["egg", "eggs", "frittata", "quiche", "omelette"],
     3, None),
    (["tofu", "tempeh", "seitan"],
     4, 90),
 ]
 # ---------------------------------------------------------------------------
 # Dish-type signals — override protein signal when a structural match fires.
 # Ordered from most-perishable to least.
 # ---------------------------------------------------------------------------
 _DISH_SIGNALS: list[tuple[list[str], int, int | None, str]] = [
    # (keywords, fridge_days, freeze_days, storage_advice_fragment)
    # Ceviche: acid denatures proteins but does not kill pathogens.
    # FDA/USDA classify it as raw seafood — 2-day fridge max, do not freeze.
    (["ceviche", "tiradito", "leche de tigre"],
     2, None,
     "Acid marination is not the same as heat cooking — treat as raw seafood. "
     "Best eaten the day it's made; 2 days maximum in the fridge."),
    # Fermented / salt-cured dishes — preservation extends shelf life significantly.
    # This matches dish names, not just presence of the ingredient (lardo in a pasta
    # follows normal pasta rules, not this entry).
    (["kimchi", "sauerkraut", "preserved lemon"],
     14, None,
     "Fermented and salt-preserved dishes keep well. Store submerged in their brine."),
    (["confit", "gravlax", "gravad lax", "lardo"],
     7, 60,
     "Store covered in its fat or cure. Keep cold and away from strong-smelling foods."),
    (["soup", "stew", "broth", "chowder", "bisque", "gumbo", "chili"],
     4, 120,
     "Soups and stews keep well in the fridge. Cool to room temperature before covering."),
    (["curry"],
     4, 90,
     "Store curry in an airtight container. The flavours deepen overnight."),
    (["casserole", "bake", "gratin", "lasagna", "lasagne", "moussaka",
      "shepherd's pie", "pot pie"],
     5, 90,
     "Cover tightly. Reheat individual portions rather than the whole dish."),
    (["pasta", "noodle", "spaghetti", "penne", "linguine", "fettuccine",
      "macaroni", "risotto"],
     4, 60,
     "Store pasta and sauce separately if possible to prevent sogginess."),
    (["rice", "fried rice", "pilaf", "biryani"],
     3, 90,
     "Cool rice quickly — spread on a tray if needed. Don't leave at room temperature for more than 1 hour."),
    (["salad"],
     2, None,
     "Keep dressing separate. Once dressed, best eaten the same day."),
    (["stir fry", "stir-fry"],
     3, 60,
     "Reheat in a hot pan or wok rather than a microwave to keep texture."),
    (["sandwich", "wrap", "taco", "burrito"],
     2, None,
     "Assemble fresh when possible. Fillings keep better stored separately."),
    (["pizza"],
     4, 60,
     "Reheat in a dry skillet for a crisp base rather than a microwave."),
    (["muffin", "bread", "biscuit", "scone", "roll"],
     3, 90,
     "Wrap tightly or seal in a bag to prevent drying out."),
    (["cake", "pie", "cookie", "brownie", "dessert", "pudding"],
     5, 90,
     "Store covered at room temperature or in the fridge depending on fillings."),
    (["smoothie", "juice", "shake"],
     1, 7,
     "Best consumed fresh. Stir or shake well before drinking."),
 ]
 # Default when no signals match.
 _DEFAULT_FRIDGE = 4
 _DEFAULT_FREEZE = 90
 _DEFAULT_ADVICE = "Store in an airtight container in the fridge. Reheat until piping hot before eating."
 def _contains_any(text: str, keywords: list[str]) -> bool:
    for kw in keywords:
        if re.search(rf"\b{re.escape(kw)}\b", text, re.IGNORECASE):
            return True
    return False
 def _scan_ingredients(ingredients: list[str]) -> tuple[int, int | None] | None:
    """Return (fridge_days, freeze_days) for the most-perishable protein found."""
    joined = " ".join(str(i) for i in ingredients).lower()
    best: tuple[int, int | None] | None = None
    for keywords, fridge, freeze in _PROTEIN_SIGNALS:
        if _contains_any(joined, keywords):
            if best is None or fridge < best[0]:
                best = (fridge, freeze)
    return best
 def _scan_dish_type(text: str) -> tuple[int, int | None, str] | None:
    """Return (fridge_days, freeze_days, advice) for the first matching dish type."""
    for keywords, fridge, freeze, advice in _DISH_SIGNALS:
        if _contains_any(text, keywords):
            return fridge, freeze, advice
    return None
 def predict_leftovers(
    title: str,
    ingredients: list[str],
    category: str | None = None,
    keywords: list[str] | None = None,
 ) -> LeftoversResult:
    """Predict cooked-leftover shelf life deterministically.
    Falls back gracefully — always returns a result even for unknown recipes.
    """
    # Build a combined text blob for dish-type scanning.
    search_text = " ".join(filter(None, [
        title,
        category or "",
        " ".join(keywords or []),
    ]))
    # Dish-type match takes structural priority over raw ingredient protein signal.
    dish = _scan_dish_type(search_text)
    protein = _scan_ingredients(ingredients)
    if dish:
        fridge_days, freeze_days, base_advice = dish
        # Still apply shortest-protein-wins if protein is more perishable than dish default.
        if protein and protein[0] < fridge_days:
            fridge_days = protein[0]
            if protein[1] is not None and (freeze_days is None or protein[1] < freeze_days):
                freeze_days = protein[1]
        advice = base_advice
    elif protein:
        fridge_days, freeze_days = protein
        advice = _DEFAULT_ADVICE
    else:
        fridge_days = _DEFAULT_FRIDGE
        freeze_days = _DEFAULT_FREEZE
        advice = _DEFAULT_ADVICE
    # freeze_by_day: recommend freezing on day 2 if fridge window is tight (≤3 days).
    freeze_by_day: int | None = None
    if freeze_days is not None and fridge_days <= 3:
        freeze_by_day = 2
    return LeftoversResult(
        fridge_days=fridge_days,
        freeze_days=freeze_days,
        freeze_by_day=freeze_by_day,
        storage_advice=advice,
    )
 def predict_leftovers_from_row(recipe: dict[str, Any]) -> LeftoversResult:
    """Convenience wrapper that accepts a Store row dict directly."""
    import json as _json
    title = recipe.get("title") or ""
    raw_ingredients = recipe.get("ingredient_names") or []
    if isinstance(raw_ingredients, str):
        try:
            raw_ingredients = _json.loads(raw_ingredients)
        except Exception:
            raw_ingredients = [raw_ingredients]
    raw_keywords = recipe.get("keywords") or []
    if isinstance(raw_keywords, str):
        try:
            raw_keywords = _json.loads(raw_keywords)
        except Exception:
            raw_keywords = [raw_keywords]
    return predict_leftovers(
        title=title,
        ingredients=[str(i) for i in raw_ingredients],
        category=recipe.get("category"),
        keywords=[str(k) for k in raw_keywords],
    )
--- a/app/services/magpie_hook.py
+++ b/app/services/magpie_hook.py
@ -1,97 +0,0 @@
 """Magpie data-flywheel hook.
 Fires anonymized recipe-signal events to the Magpie ingest endpoint when a
 user saves or rates a recipe.  This is the Kiwi side of the flywheel — Magpie
 does not have a receiver endpoint yet, so the hook stubs out gracefully: if
 ``MAGPIE_INGEST_URL`` is unset, or the request fails for any reason, it logs
 at DEBUG level and returns without raising.
 """
 from __future__ import annotations
 import logging
 from pathlib import Path
 logger = logging.getLogger(__name__)
 _INGEST_PATH = "/api/v1/ingest/recipe-signal"
 async def fire_recipe_signal(
    db_path: Path,
    recipe_id: int,
    rating: int | None,
    style_tags: list[str],
 ) -> None:
    """Post an anonymized recipe signal to Magpie if the user has opted in.
    Args:
        db_path:    Path to the user's SQLite database.
        recipe_id:  Internal Kiwi recipe ID being rated/saved.
        rating:     Star rating (0–5) or None if not yet rated.
        style_tags: Style tags applied to the saved recipe.
    """
    from app.core.config import settings
    if not settings.MAGPIE_INGEST_URL:
        return
    # Check per-user opt-in via a short-lived Store (own connection, own thread
    # context is fine — this runs in the async event loop as a background task
    # so we open and close the connection immediately).
    from app.db.store import Store
    try:
        store = Store(db_path)
        try:
            opt_in = store.get_setting("magpie_opt_in")
        finally:
            store.close()
    except Exception as exc:  # noqa: BLE001
        logger.debug("magpie_hook: could not read magpie_opt_in setting: %s", exc)
        return
    if opt_in != "true":
        return
    # Fetch the recipe to get its external_id (source URL slug / corpus key).
    try:
        store = Store(db_path)
        try:
            recipe = store.get_recipe(recipe_id)
        finally:
            store.close()
    except Exception as exc:  # noqa: BLE001
        logger.debug("magpie_hook: could not fetch recipe %d: %s", recipe_id, exc)
        return
    if recipe is None:
        logger.debug("magpie_hook: recipe %d not found, skipping", recipe_id)
        return
    external_id: str | None = recipe.get("external_id") if isinstance(recipe, dict) else getattr(recipe, "external_id", None)
    if not external_id:
        # Corpus recipe not yet enriched with a source identifier — skip quietly.
        logger.debug("magpie_hook: recipe %d has no external_id, skipping", recipe_id)
        return
    payload = {
        "product": "kiwi",
        "signal": "recipe_rating",
        "external_id": external_id,
        "rating": rating,
        "style_tags": style_tags,
    }
    url = settings.MAGPIE_INGEST_URL.rstrip("/") + _INGEST_PATH
    try:
        import httpx
        async with httpx.AsyncClient(timeout=3.0) as client:
            response = await client.post(url, json=payload)
            logger.debug(
                "magpie_hook: POST %s → %d", url, response.status_code
            )
    except Exception as exc:  # noqa: BLE001
        # Magpie may not have a receiver yet — log and swallow.
        logger.debug("magpie_hook: ingest request failed (stub): %s", exc)
--- a/app/services/meal_plan/llm_router.py
+++ b/app/services/meal_plan/llm_router.py
@ -2,20 +2,17 @@
 # BSL 1.1 — LLM feature
 """Provide a router-compatible LLM client for meal plan generation tasks.
-Cloud (CF_ORCH_URL set), tier 1 — task-based routing (preferred):
+Cloud (CF_ORCH_URL set):
-  Calls /api/inference/task with product=kiwi, task=meal_plan.
+  Allocates a cf-text service via cf-orch (3B-7B GGUF, ~2GB VRAM).
-  The coordinator resolves the model from assignments.yaml.
+  Returns an _OrchTextRouter that wraps the cf-text HTTP endpoint
-
+  with a .complete(system, user, **kwargs) interface.
 Cloud (CF_ORCH_URL set), tier 2 — direct allocation (fallback):
  Allocates cf-text directly via client.allocate(). Used when the task
  is not yet registered in the coordinator (cf-orch#61 not deployed).
 Local / self-hosted (no CF_ORCH_URL):
  Returns an LLMRouter instance which tries ollama, vllm, or any
  backend configured in ~/.config/circuitforge/llm.yaml.
-All paths expose the same (router, ctx) interface so llm_planner.py
+Both paths expose the same interface so llm_timing.py and llm_planner.py
-needs no knowledge of the backend.
+need no knowledge of the backend.
 """
 from __future__ import annotations
@ -25,7 +22,8 @@ from contextlib import nullcontext
 logger = logging.getLogger(__name__)
-# cf-orch service name and TTL for direct-allocate fallback path.
+# cf-orch service name and VRAM budget for meal plan LLM tasks.
 # These are lighter than recipe_llm (4.0 GB) — cf-text handles them.
 _SERVICE_TYPE = "cf-text"
 _TTL_S = 120.0
 _CALLER = "kiwi-meal-plan"
@ -64,58 +62,16 @@ class _OrchTextRouter:
        return resp.choices[0].message.content or ""
 # Imported at module level so tests can patch the names in this module's namespace.
 # app.services.task_inference.task_allocate — patch target for task routing tests.
 try:
    from app.services.task_inference import TaskNotRegistered, task_allocate
    _HAS_TASK_INFERENCE = True
 except ImportError:
    _HAS_TASK_INFERENCE = False
 # circuitforge_orch.client.CFOrchClient — patch target for direct-allocate fallback tests.
 try:
    from circuitforge_orch.client import CFOrchClient
 except ImportError:
    CFOrchClient = None  # type: ignore[assignment,misc]
 # circuitforge_core.llm.router.LLMRouter — patch target for local-inference tests.
 try:
    from circuitforge_core.llm.router import LLMRouter
 except (ImportError, FileNotFoundError):
    LLMRouter = None  # type: ignore[assignment,misc]
 def get_meal_plan_router():
    """Return an LLM client for meal plan tasks.
-    Returns (router, ctx) where ctx is a context manager the caller holds
+    Tries cf-orch cf-text allocation first (cloud); falls back to LLMRouter
-    open for the duration of the LLM call. Returns (None, nullcontext(None))
+    (local ollama/vllm). Returns None if no backend is available.
    if no backend is available.
    """
    cf_orch_url = os.environ.get("CF_ORCH_URL")
    if cf_orch_url:
        # Tier 1: task-based routing — coordinator owns model selection.
        if _HAS_TASK_INFERENCE:
            try:
                ctx = task_allocate(
                    "kiwi", "meal_plan",
                    service_hint=_SERVICE_TYPE,
                    ttl_s=_TTL_S,
                )
                alloc = ctx.__enter__()
                return _OrchTextRouter(alloc.url), ctx
            except TaskNotRegistered:
                logger.debug(
                    "kiwi.meal_plan not in coordinator assignments — "
                    "falling back to direct cf-text allocation"
                )
            except Exception as exc:
                logger.debug("task allocation failed, trying direct allocate: %s", exc)
        # Tier 2: direct allocation — hardcoded service type.
        if CFOrchClient is not None:
        try:
            from circuitforge_orch.client import CFOrchClient
            client = CFOrchClient(cf_orch_url)
            ctx = client.allocate(
                service=_SERVICE_TYPE,
@ -125,13 +81,12 @@ def get_meal_plan_router():
            alloc = ctx.__enter__()
            if alloc is not None:
                return _OrchTextRouter(alloc.url), ctx
                ctx.__exit__(None, None, None)  # release allocation before falling through
        except Exception as exc:
            logger.debug("cf-orch cf-text allocation failed, falling back to LLMRouter: %s", exc)
-    # Tier 3: local inference — ollama / vllm / openai-compat.
+    # Local fallback: LLMRouter (ollama / vllm / openai-compat)
    if LLMRouter is not None:
    try:
        from circuitforge_core.llm.router import LLMRouter
        return LLMRouter(), nullcontext(None)
    except FileNotFoundError:
        logger.debug("LLMRouter: no llm.yaml and no LLM env vars — meal plan LLM disabled")
@ -139,4 +94,3 @@ def get_meal_plan_router():
    except Exception as exc:
        logger.debug("LLMRouter init failed: %s", exc)
        return None, nullcontext(None)
    return None, nullcontext(None)
--- a/app/services/ocr/docuvision_client.py
+++ b/app/services/ocr/docuvision_client.py
@ -18,51 +18,43 @@ class DocuvisionResult:
 class DocuvisionClient:
    """Thin client for the cf-docuvision service."""
-    def __init__(self, base_url: str, timeout: float = 120.0) -> None:
+    def __init__(self, base_url: str) -> None:
        self._base_url = base_url.rstrip("/")
        self._timeout = timeout
-    def extract_text(self, image_path: str | Path, hint: str = "text") -> DocuvisionResult:
+    def extract_text(self, image_path: str | Path) -> DocuvisionResult:
-        """Send an image to docuvision and return extracted text.
+        """Send an image to docuvision and return extracted text."""
        Args:
            image_path: Path to the image file.
            hint: Docuvision extraction hint — "text" for dense prose (recipes),
                  "table" for tabular data, "form" for form fields, "auto" for
                  automatic detection.
        """
        image_bytes = Path(image_path).read_bytes()
        b64 = base64.b64encode(image_bytes).decode()
-        with httpx.Client(timeout=self._timeout) as client:
+        with httpx.Client(timeout=30.0) as client:
            resp = client.post(
                f"{self._base_url}/extract",
-                json={"image_b64": b64, "hint": hint},
+                json={"image": b64},
            )
            resp.raise_for_status()
            data = resp.json()
        return DocuvisionResult(
-            text=data.get("raw_text", ""),
+            text=data.get("text", ""),
-            confidence=data.get("metadata", {}).get("confidence"),
+            confidence=data.get("confidence"),
            raw=data,
        )
-    async def extract_text_async(self, image_path: str | Path, hint: str = "text") -> DocuvisionResult:
+    async def extract_text_async(self, image_path: str | Path) -> DocuvisionResult:
        """Async version."""
        image_bytes = Path(image_path).read_bytes()
        b64 = base64.b64encode(image_bytes).decode()
-        async with httpx.AsyncClient(timeout=self._timeout) as client:
+        async with httpx.AsyncClient(timeout=30.0) as client:
            resp = await client.post(
                f"{self._base_url}/extract",
-                json={"image_b64": b64, "hint": hint},
+                json={"image": b64},
            )
            resp.raise_for_status()
            data = resp.json()
        return DocuvisionResult(
-            text=data.get("raw_text", ""),
+            text=data.get("text", ""),
-            confidence=data.get("metadata", {}).get("confidence"),
+            confidence=data.get("confidence"),
            raw=data,
        )
--- a/app/services/ocr/vl_model.py
+++ b/app/services/ocr/vl_model.py
@ -32,29 +32,6 @@ def _try_docuvision(image_path: str | Path) -> str | None:
    cf_orch_url = os.environ.get("CF_ORCH_URL")
    if not cf_orch_url:
        return None
    # Tier 1: task-based routing — coordinator owns model selection.
    try:
        from app.services.task_inference import task_allocate, TaskNotRegistered
        from app.services.ocr.docuvision_client import DocuvisionClient
        try:
            with task_allocate(
                "kiwi", "ocr",
                service_hint="cf-docuvision",
                ttl_s=60.0,
            ) as alloc:
                doc_client = DocuvisionClient(alloc.url)
                result = doc_client.extract_text(image_path)
                return result.text if result.text else None
        except TaskNotRegistered:
            logger.debug(
                "kiwi.ocr not in coordinator assignments — "
                "falling back to direct cf-docuvision allocation"
            )
    except Exception as exc:
        logger.debug("task allocation path failed, trying direct allocate: %s", exc)
    # Tier 2: direct allocation — hardcoded service type.
    try:
        from circuitforge_orch.client import CFOrchClient
        from app.services.ocr.docuvision_client import DocuvisionClient
@ -72,7 +49,7 @@ def _try_docuvision(image_path: str | Path) -> str | None:
            result = doc_client.extract_text(image_path)
            return result.text if result.text else None
    except Exception as exc:
-        logger.debug("cf-docuvision fast-path failed, falling back to local VLM: %s", exc)
+        logger.debug("cf-docuvision fast-path failed, falling back: %s", exc)
        return None
--- a/app/services/recipe/browse_counts_cache.py
+++ b/app/services/recipe/browse_counts_cache.py
@ -1,256 +0,0 @@
 """
 Browse counts cache — pre-computes and persists recipe counts for all
 browse domain keyword sets so category/subcategory page loads never
 hit the 3.8 GB FTS index at request time.
 Counts change only when the corpus changes (after a pipeline run).
 The cache is a small SQLite file separate from both the read-only
 corpus DB and per-user kiwi.db files, so the container can write it.
 Refresh triggers:
  1. Startup     — if cache is missing or older than STALE_DAYS
  2. Nightly     — asyncio background task started in main.py lifespan
  3. Pipeline    — infer_recipe_tags.py calls refresh() at end of run
 The in-memory _COUNT_CACHE in store.py is pre-warmed from this file
 on startup, so FTS queries are never needed for known keyword sets.
 """
 from __future__ import annotations
 import logging
 import sqlite3
 from datetime import datetime, timezone
 from pathlib import Path
 logger = logging.getLogger(__name__)
 STALE_DAYS = 7
 # ---------------------------------------------------------------------------
 # Internal helpers
 # ---------------------------------------------------------------------------
 def _kw_key(keywords: list[str]) -> str:
    """Stable string key for a keyword list — sorted and pipe-joined."""
    return "|".join(sorted(keywords))
 def _fts_match_expr(keywords: list[str]) -> str:
    phrases = ['"' + kw.replace('"', '""') + '"' for kw in keywords]
    return " OR ".join(phrases)
 def _ensure_schema(conn: sqlite3.Connection) -> None:
    conn.execute("""
        CREATE TABLE IF NOT EXISTS browse_counts (
            keywords_key  TEXT PRIMARY KEY,
            count         INTEGER NOT NULL,
            computed_at   TEXT NOT NULL
        )
    """)
    conn.execute("""
        CREATE TABLE IF NOT EXISTS browse_counts_meta (
            key   TEXT PRIMARY KEY,
            value TEXT NOT NULL
        )
    """)
    conn.commit()
 # ---------------------------------------------------------------------------
 # Public API
 # ---------------------------------------------------------------------------
 def is_stale(cache_path: Path, max_age_days: int = STALE_DAYS) -> bool:
    """Return True if the cache is missing, empty, or older than max_age_days."""
    if not cache_path.exists():
        return True
    try:
        conn = sqlite3.connect(cache_path)
        row = conn.execute(
            "SELECT value FROM browse_counts_meta WHERE key = 'refreshed_at'"
        ).fetchone()
        conn.close()
        if row is None:
            return True
        age = (datetime.now(timezone.utc) - datetime.fromisoformat(row[0])).days
        return age >= max_age_days
    except Exception:
        return True
 def load_into_memory(cache_path: Path, count_cache: dict, corpus_path: str) -> int:
    """
    Load all rows from the cache file into the in-memory count_cache dict.
    Uses corpus_path (the current RECIPE_DB_PATH env value) as the cache key,
    not what was stored in the file — the file may have been built against a
    different mount path (e.g. pipeline ran on host, container sees a different
    path). Counts are corpus-content-derived and path-independent.
    Returns the number of entries loaded.
    """
    if not cache_path.exists():
        return 0
    try:
        conn = sqlite3.connect(cache_path)
        rows = conn.execute("SELECT keywords_key, count FROM browse_counts").fetchall()
        conn.close()
        loaded = 0
        for kw_key, count in rows:
            keywords = kw_key.split("|") if kw_key else []
            cache_key = (corpus_path, *sorted(keywords))
            count_cache[cache_key] = count
            loaded += 1
        logger.info("browse_counts: warmed %d entries from %s", loaded, cache_path)
        return loaded
    except Exception as exc:
        logger.warning("browse_counts: load failed: %s", exc)
        return 0
 def refresh(corpus_path: str, cache_path: Path) -> int:
    """
    Run FTS5 queries for every keyword set in browser_domains.DOMAINS
    and write results to cache_path.
    Safe to call from both the host pipeline script and the in-container
    nightly task. The corpus_path must be reachable and readable from
    the calling process.
    Returns the number of keyword sets computed.
    """
    from app.services.recipe.browser_domains import DOMAINS  # local import — avoid circular
    cache_path.parent.mkdir(parents=True, exist_ok=True)
    cache_conn = sqlite3.connect(cache_path)
    _ensure_schema(cache_conn)
    # Collect every unique keyword list across all domains/categories/subcategories.
    # DOMAINS structure: {domain: {label: str, categories: {cat_name: {keywords, subcategories}}}}
    seen: dict[str, list[str]] = {}
    for domain_data in DOMAINS.values():
        for cat_data in domain_data.get("categories", {}).values():
            if not isinstance(cat_data, dict):
                continue
            top_kws = cat_data.get("keywords", [])
            if top_kws:
                seen[_kw_key(top_kws)] = top_kws
            for subcat_kws in cat_data.get("subcategories", {}).values():
                if subcat_kws:
                    seen[_kw_key(subcat_kws)] = subcat_kws
    try:
        corpus_conn = sqlite3.connect(f"file:{corpus_path}?mode=ro", uri=True)
    except Exception as exc:
        logger.error("browse_counts: cannot open corpus %s: %s", corpus_path, exc)
        cache_conn.close()
        return 0
    now = datetime.now(timezone.utc).isoformat()
    computed = 0
    try:
        for kw_key, kws in seen.items():
            try:
                row = corpus_conn.execute(
                    "SELECT count(*) FROM recipe_browser_fts WHERE recipe_browser_fts MATCH ?",
                    (_fts_match_expr(kws),),
                ).fetchone()
                count = row[0] if row else 0
                cache_conn.execute(
                    "INSERT OR REPLACE INTO browse_counts (keywords_key, count, computed_at)"
                    " VALUES (?, ?, ?)",
                    (kw_key, count, now),
                )
                computed += 1
            except Exception as exc:
                logger.warning("browse_counts: query failed key=%r: %s", kw_key[:60], exc)
        # Merge accepted community tags into counts.
        # For each (domain, category, subcategory) that has accepted community
        # tags, add the count of distinct tagged recipe_ids to the FTS count.
        # The two overlap rarely (community tags exist precisely because FTS
        # missed those recipes), so simple addition is accurate enough.
        try:
            _merge_community_tag_counts(cache_conn, DOMAINS, now)
        except Exception as exc:
            logger.warning("browse_counts: community merge skipped: %s", exc)
        cache_conn.execute(
            "INSERT OR REPLACE INTO browse_counts_meta (key, value) VALUES ('refreshed_at', ?)",
            (now,),
        )
        cache_conn.execute(
            "INSERT OR REPLACE INTO browse_counts_meta (key, value) VALUES ('corpus_path', ?)",
            (corpus_path,),
        )
        cache_conn.commit()
        logger.info("browse_counts: wrote %d counts → %s", computed, cache_path)
    finally:
        corpus_conn.close()
        cache_conn.close()
    return computed
 def _merge_community_tag_counts(
    cache_conn: sqlite3.Connection,
    domains: dict,
    now: str,
    threshold: int = 2,
 ) -> None:
    """Add accepted community tag counts on top of FTS counts in the cache.
    Queries the community PostgreSQL store (if available) for accepted tags
    grouped by (domain, category, subcategory), maps each back to its keyword
    set key, then increments the cached count.
    Silently skips if community features are unavailable.
    """
    try:
        from app.api.endpoints.community import _get_community_store
        store = _get_community_store()
        if store is None:
            return
    except Exception:
        return
    for domain_id, domain_data in domains.items():
        for cat_name, cat_data in domain_data.get("categories", {}).items():
            if not isinstance(cat_data, dict):
                continue
            # Check subcategories
            for subcat_name, subcat_kws in cat_data.get("subcategories", {}).items():
                if not subcat_kws:
                    continue
                ids = store.get_accepted_recipe_ids_for_subcategory(
                    domain=domain_id,
                    category=cat_name,
                    subcategory=subcat_name,
                    threshold=threshold,
                )
                if not ids:
                    continue
                kw_key = _kw_key(subcat_kws)
                cache_conn.execute(
                    "UPDATE browse_counts SET count = count + ? WHERE keywords_key = ?",
                    (len(ids), kw_key),
                )
            # Check category-level tags (subcategory IS NULL)
            top_kws = cat_data.get("keywords", [])
            if top_kws:
                ids = store.get_accepted_recipe_ids_for_subcategory(
                    domain=domain_id,
                    category=cat_name,
                    subcategory=None,
                    threshold=threshold,
                )
                if ids:
                    kw_key = _kw_key(top_kws)
                    cache_conn.execute(
                        "UPDATE browse_counts SET count = count + ? WHERE keywords_key = ?",
                        (len(ids), kw_key),
                    )
    logger.info("browse_counts: community tag counts merged")
--- a/app/services/recipe/browser_domains.py
+++ b/app/services/recipe/browser_domains.py
@ -5,12 +5,6 @@ Each domain provides a two-level category hierarchy for browsing the recipe corp
 Keyword matching is case-insensitive against the recipes.category column and the
 recipes.keywords JSON array. A recipe may appear in multiple categories (correct).
 Category values are either:
  - list[str]  — flat keyword list (no subcategories)
  - dict       — {"keywords": list[str], "subcategories": {name: list[str]}}
                  keywords covers the whole category (used for "All X" browse);
                  subcategories each have their own narrower keyword list.
 These are starter mappings based on the food.com dataset structure. Run:
    SELECT category, count(*) FROM recipes
@ -25,601 +19,51 @@ DOMAINS: dict[str, dict] = {
    "cuisine": {
        "label": "Cuisine",
        "categories": {
-            "Italian": {
+            "Italian":        ["italian", "pasta", "pizza", "risotto", "lasagna", "carbonara"],
-                "keywords": ["cuisine:Italian", "italian", "pasta", "pizza", "risotto", "lasagna", "carbonara"],
+            "Mexican":        ["mexican", "tex-mex", "taco", "enchilada", "burrito", "salsa", "guacamole"],
-                "subcategories": {
+            "Asian":          ["asian", "chinese", "japanese", "thai", "korean", "vietnamese", "stir fry", "stir-fry", "ramen", "sushi"],
-                    "Sicilian":   ["sicilian", "sicily", "arancini", "caponata",
+            "American":       ["american", "southern", "bbq", "barbecue", "comfort food", "cajun", "creole"],
-                                   "involtini", "cannoli"],
+            "Mediterranean":  ["mediterranean", "greek", "middle eastern", "turkish", "moroccan", "lebanese"],
-                    "Neapolitan": ["neapolitan", "naples", "pizza napoletana",
+            "Indian":         ["indian", "curry", "lentil", "dal", "tikka", "masala", "biryani"],
-                                   "sfogliatelle", "ragù"],
+            "European":       ["french", "german", "spanish", "british", "irish", "scandinavian"],
-                    "Tuscan":     ["tuscan", "tuscany", "ribollita", "bistecca",
+            "Latin American": ["latin american", "peruvian", "argentinian", "colombian", "cuban", "caribbean"],
                                   "pappardelle", "crostini"],
                    "Roman":      ["roman", "rome", "cacio e pepe", "carbonara",
                                   "amatriciana", "gricia", "supplì"],
                    "Venetian":   ["venetian", "venice", "risotto", "bigoli",
                                   "baccalà", "sarde in saor"],
                    "Ligurian":   ["ligurian", "liguria", "pesto", "focaccia",
                                   "trofie", "farinata"],
                },
            },
            "Mexican": {
                "keywords": ["cuisine:Mexican", "mexican", "taco", "enchilada", "burrito",
                             "salsa", "guacamole", "mole", "tamale"],
                "subcategories": {
                    "Oaxacan":      ["oaxacan", "oaxaca", "mole negro", "tlayuda",
                                     "chapulines", "mezcal", "tasajo", "memelas"],
                    "Yucatecan":    ["yucatecan", "yucatan", "cochinita pibil", "poc chuc",
                                     "sopa de lima", "panuchos", "papadzules"],
                    "Veracruz":     ["veracruz", "veracruzana", "huachinango",
                                     "picadas", "enfrijoladas", "caldo de mariscos"],
                    "Street Food":  ["taco", "elote", "tlacoyos", "torta", "tamale",
                                     "quesadilla", "tostada", "sope", "gordita"],
                    "Mole":         ["mole", "mole negro", "mole rojo", "mole verde",
                                     "mole poblano", "mole amarillo", "pipián"],
                    "Baja / Cal-Mex": ["baja", "baja california", "cal-mex", "baja fish taco",
                                       "fish taco", "carne asada fries", "california burrito",
                                       "birria", "birria tacos", "quesabirria",
                                       "lobster puerto nuevo", "tijuana", "ensenada",
                                       "agua fresca", "caesar salad tijuana"],
                    "Mexico City":  ["mexico city", "chilaquiles", "tlayuda cdmx",
                                     "tacos de canasta", "torta ahogada", "pozole",
                                     "chiles en nogada"],
                },
            },
            "Asian": {
                "keywords": ["cuisine:Chinese", "cuisine:Japanese", "cuisine:Korean",
                             "cuisine:Thai", "cuisine:Vietnamese",
                             "asian", "chinese", "japanese", "thai", "korean", "vietnamese",
                             "stir fry", "stir-fry", "ramen", "sushi", "malaysian",
                             "taiwanese", "singaporean", "burmese", "cambodian",
                             "laotian", "mongolian", "hong kong"],
                "subcategories": {
                    "Korean":       ["korean", "kimchi", "bibimbap", "bulgogi", "japchae",
                                     "doenjang", "gochujang", "tteokbokki", "sundubu",
                                     "galbi", "jjigae", "kbbq", "korean fried chicken"],
                    "Japanese":     ["japanese", "sushi", "ramen", "tempura", "miso",
                                     "teriyaki", "udon", "soba", "bento", "yakitori",
                                     "tonkatsu", "onigiri", "okonomiyaki", "takoyaki",
                                     "kaiseki", "izakaya"],
                    "Chinese":      ["chinese", "dim sum", "fried rice", "dumplings", "wonton",
                                     "spring roll", "szechuan", "sichuan", "cantonese",
                                     "chow mein", "mapo tofu", "lo mein", "hot pot",
                                     "peking duck", "char siu", "congee"],
                    "Thai":         ["thai", "pad thai", "green curry", "red curry",
                                     "coconut milk", "lemongrass", "satay", "tom yum",
                                     "larb", "khao man gai", "massaman", "pad see ew"],
                    "Vietnamese":   ["vietnamese", "pho", "banh mi", "spring rolls",
                                     "vermicelli", "nuoc cham", "bun bo hue",
                                     "banh xeo", "com tam", "bun cha"],
                    "Filipino":     ["filipino", "adobo", "sinigang", "pancit", "lumpia",
                                     "kare-kare", "lechon", "sisig", "halo-halo",
                                     "dinuguan", "tinola", "bistek"],
                    "Indonesian":   ["indonesian", "rendang", "nasi goreng", "gado-gado",
                                     "tempeh", "sambal", "soto", "opor ayam",
                                     "bakso", "mie goreng", "nasi uduk"],
                    "Malaysian":    ["malaysian", "laksa", "nasi lemak", "char kway teow",
                                     "satay malaysia", "roti canai", "bak kut teh",
                                     "cendol", "mee goreng mamak", "curry laksa"],
                    "Taiwanese":    ["taiwanese", "beef noodle soup", "lu rou fan",
                                     "oyster vermicelli", "scallion pancake taiwan",
                                     "pork chop rice", "three cup chicken",
                                     "bubble tea", "stinky tofu", "ba wan"],
                    "Singaporean":  ["singaporean", "chicken rice", "chili crab",
                                     "singaporean laksa", "bak chor mee", "rojak",
                                     "kaya toast", "nasi padang", "satay singapore"],
                    "Burmese":      ["burmese", "myanmar", "mohinga", "laphet thoke",
                                     "tea leaf salad", "ohn no khao swe",
                                     "mont di", "nangyi thoke"],
                    "Hong Kong":    ["hong kong", "hk style", "pineapple bun",
                                     "wonton noodle soup", "hk milk tea", "egg tart",
                                     "typhoon shelter crab", "char siu bao", "jook",
                                     "congee hk", "silk stocking tea", "dan tat",
                                     "siu mai hk", "cheung fun"],
                    "Cambodian":    ["cambodian", "khmer", "amok", "lok lak",
                                     "kuy teav", "bai sach chrouk", "nom banh chok",
                                     "samlor korko", "beef loc lac"],
                    "Laotian":      ["laotian", "lao", "larb", "tam mak hoong",
                                     "or lam", "khao niaw", "ping kai",
                                     "naem khao", "khao piak sen", "mok pa"],
                    "Mongolian":    ["mongolian", "buuz", "khuushuur", "tsuivan",
                                     "boodog", "airag", "khorkhog", "bansh",
                                     "guriltai shol", "suutei tsai"],
                    "South Asian Fusion": ["south asian fusion", "indo-chinese",
                                           "hakka chinese", "chilli chicken",
                                           "manchurian", "schezwan"],
                },
            },
            "Indian": {
                "keywords": ["cuisine:Indian", "indian", "curry", "lentil", "dal", "tikka", "masala",
                             "biryani", "naan", "chutney", "pakistani", "sri lankan",
                             "bangladeshi", "nepali"],
                "subcategories": {
                    "North Indian":  ["north indian", "punjabi", "mughal", "tikka masala",
                                      "naan", "tandoori", "butter chicken", "palak paneer",
                                      "chole", "rajma", "aloo gobi"],
                    "South Indian":  ["south indian", "tamil", "kerala", "dosa", "idli",
                                      "sambar", "rasam", "coconut chutney", "appam",
                                      "fish curry kerala", "puttu", "payasam"],
                    "Bengali":       ["bengali", "mustard fish", "hilsa", "shorshe ilish",
                                      "mishti doi", "rasgulla", "kosha mangsho"],
                    "Gujarati":      ["gujarati", "dhokla", "thepla", "undhiyu",
                                      "khandvi", "fafda", "gujarati dal"],
                    "Pakistani":     ["pakistani", "nihari", "haleem", "seekh kebab",
                                      "karahi", "biryani karachi", "chapli kebab",
                                      "halwa puri", "paya"],
                    "Sri Lankan":    ["sri lankan", "kottu roti", "hoppers", "pol sambol",
                                      "sri lankan curry", "lamprais", "string hoppers",
                                      "wambatu moju"],
                    "Bangladeshi":   ["bangladeshi", "bangladesh", "dhaka biryani",
                                      "shutki", "pitha", "hilsa curry", "kacchi biryani",
                                      "bhuna khichuri", "doi maach", "rezala"],
                    "Nepali":        ["nepali", "dal bhat", "momos", "sekuwa",
                                      "sel roti", "gundruk", "thukpa"],
                },
            },
            "Mediterranean": {
                "keywords": ["cuisine:Mediterranean", "cuisine:Greek", "cuisine:Middle Eastern",
                             "mediterranean", "greek", "middle eastern", "turkish",
                             "lebanese", "jewish", "palestinian", "yemeni", "egyptian",
                             "syrian", "iraqi", "jordanian"],
                "subcategories": {
                    "Greek":          ["greek", "feta", "tzatziki", "moussaka", "spanakopita",
                                       "souvlaki", "dolmades", "spanakopita", "tiropita",
                                       "galaktoboureko"],
                    "Turkish":        ["turkish", "kebab", "borek", "meze", "baklava",
                                       "lahmacun", "menemen", "pide", "iskender",
                                       "kisir", "simit"],
                    "Syrian":         ["syrian", "fattet hummus", "kibbeh syria",
                                       "muhammara", "maklouba syria", "sfeeha",
                                       "halawet el jibn"],
                    "Lebanese":       ["lebanese", "middle eastern", "hummus", "falafel",
                                       "tabbouleh", "kibbeh", "fattoush", "manakish",
                                       "kafta", "sfiha"],
                    "Jewish":         ["jewish", "israeli", "ashkenazi", "sephardic",
                                       "shakshuka", "sabich", "za'atar", "tahini",
                                       "zhug", "zhoug", "s'khug", "z'houg",
                                       "hawaiij", "hawaij", "hawayej",
                                       "matzo", "latke", "rugelach", "babka", "challah",
                                       "cholent", "gefilte fish", "brisket", "kugel",
                                       "new york jewish", "new york deli", "pastrami",
                                       "knish", "lox", "bagel and lox", "jewish deli"],
                    "Palestinian":    ["palestinian", "musakhan", "maqluba", "knafeh",
                                       "maftoul", "freekeh", "sumac chicken"],
                    "Yemeni":         ["yemeni", "saltah", "lahoh", "bint al-sahn",
                                       "zhug", "zhoug", "hulba", "fahsa",
                                       "hawaiij", "hawaij", "hawayej"],
                    "Egyptian":       ["egyptian", "koshari", "molokhia", "mahshi",
                                       "ful medames", "ta'ameya", "feteer meshaltet"],
                },
            },
            "American": {
                "keywords": ["cuisine:American", "cuisine:Southern", "cuisine:Cajun",
                             "american", "southern", "comfort food", "cajun", "creole",
                             "hawaiian", "tex-mex", "soul food"],
                "subcategories": {
                    "Southern":     ["southern", "soul food", "fried chicken",
                                     "collard greens", "cornbread", "biscuits and gravy",
                                     "mac and cheese", "sweet potato pie", "okra"],
                    "Cajun/Creole": ["cajun", "creole", "new orleans", "gumbo",
                                     "jambalaya", "etouffee", "dirty rice", "po'boy",
                                     "muffuletta", "red beans and rice"],
                    "Tex-Mex":      ["tex-mex", "southwestern", "chili", "fajita",
                                     "queso", "breakfast taco", "chile con carne"],
                    "New England":  ["new england", "chowder", "lobster", "clam",
                                     "maple", "yankee", "boston baked beans",
                                     "johnnycake", "fish and chips"],
                    "Pacific Northwest": ["pacific northwest", "pnw", "dungeness crab",
                                          "salmon", "cedar plank", "razor clam",
                                          "geoduck", "chanterelle", "marionberry"],
                    "Hawaiian":     ["hawaiian", "hawaii", "plate lunch", "loco moco",
                                     "poke", "spam musubi", "kalua pig", "lau lau",
                                     "haupia", "poi", "manapua", "garlic shrimp",
                                     "saimin", "huli huli", "malasada"],
                },
            },
            "BBQ & Smoke": {
                # Top-level keywords: cuisine:BBQ inferred tag + broad corpus terms.
                "keywords": ["cuisine:BBQ", "bbq", "barbecue", "barbeque", "smoked", "smoky",
                             "smoke", "pit", "smoke ring", "low and slow",
                             "brisket", "pulled pork", "ribs", "spare ribs",
                             "baby back", "baby back ribs", "dry rub", "wet rub",
                             "cookout", "smoker", "smoked meat", "smoked chicken",
                             "smoked pork", "smoked beef", "smoked turkey",
                             "pit smoked", "wood smoked", "slow smoked",
                             "charcoal", "chargrilled", "burnt ends"],
                "subcategories": {
                    "Texas BBQ":       ["texas bbq", "central texas bbq", "brisket",
                                        "beef brisket", "beef ribs", "smoked brisket",
                                        "post oak", "salt and pepper rub",
                                        "east texas bbq", "lockhart", "franklin style"],
                    "Carolina BBQ":    ["carolina bbq", "north carolina bbq", "whole hog",
                                        "vinegar sauce", "vinegar bbq", "lexington style",
                                        "eastern nc", "south carolina bbq", "mustard sauce",
                                        "carolina pulled pork"],
                    "Kansas City BBQ": ["kansas city bbq", "kc bbq", "burnt ends",
                                        "sweet bbq sauce", "tomato molasses sauce",
                                        "baby back ribs", "kansas city ribs"],
                    "Memphis BBQ":     ["memphis bbq", "dry rub ribs", "wet ribs",
                                        "memphis style", "dry rub pork", "memphis ribs"],
                    "Alabama BBQ":     ["alabama bbq", "white sauce", "alabama white sauce",
                                        "smoked chicken", "white bbq sauce"],
                    "Kentucky BBQ":    ["kentucky bbq", "mutton bbq", "owensboro bbq",
                                        "black dip", "western kentucky barbecue", "mutton"],
                    "St. Louis BBQ":   ["st louis bbq", "st louis ribs", "st. louis ribs",
                                        "st louis cut ribs", "spare ribs st louis"],
                    "Backyard Grill":  ["backyard bbq", "cookout", "grilled burgers",
                                        "charcoal grill", "kettle grill", "tailgate",
                                        "grill out", "backyard grilling"],
                },
            },
            "European": {
                "keywords": ["cuisine:French", "cuisine:German", "cuisine:Spanish",
                             "french", "german", "spanish", "british", "irish", "scottish",
                             "welsh", "scandinavian", "nordic", "eastern european"],
                "subcategories": {
                    "French":        ["french", "provencal", "beurre", "crepe",
                                      "ratatouille", "cassoulet", "bouillabaisse"],
                    "Spanish":       ["spanish", "paella", "tapas", "gazpacho",
                                      "tortilla espanola", "chorizo"],
                    "German":        ["german", "bratwurst", "sauerkraut", "schnitzel",
                                      "pretzel", "strudel"],
                    "British":       ["british", "english", "pub food", "cornish",
                                      "shepherd's pie", "bangers", "toad in the hole",
                                      "coronation chicken", "london", "londoner",
                                      "cornish pasty", "ploughman's"],
                    "Irish":         ["irish", "ireland", "colcannon", "coddle",
                                      "irish stew", "soda bread", "boxty", "champ"],
                    "Scottish":      ["scottish", "scotland", "haggis", "cullen skink",
                                      "cranachan", "scotch broth", "glaswegian",
                                      "neeps and tatties", "tablet"],
                    "Scandinavian":  ["scandinavian", "nordic", "swedish", "norwegian",
                                      "danish", "finnish", "gravlax", "swedish meatballs",
                                      "lefse", "smörgåsbord", "fika", "crispbread",
                                      "cardamom bun", "herring", "æbleskiver",
                                      "lingonberry", "lutefisk", "janssons frestelse",
                                      "knäckebröd", "kladdkaka"],
                    "Eastern European": ["eastern european", "polish", "russian", "ukrainian",
                                         "czech", "hungarian", "pierogi", "borscht",
                                         "goulash", "kielbasa", "varenyky", "pelmeni"],
                },
            },
            "Latin American": {
                "keywords": ["cuisine:Latin American", "cuisine:Caribbean",
                             "latin american", "peruvian", "argentinian", "colombian",
                             "cuban", "caribbean", "brazilian", "venezuelan", "chilean"],
                "subcategories": {
                    "Peruvian":    ["peruvian", "ceviche", "lomo saltado", "anticucho",
                                    "aji amarillo", "causa", "leche de tigre",
                                    "arroz con leche peru", "pollo a la brasa"],
                    "Brazilian":   ["brazilian", "churrasco", "feijoada", "pao de queijo",
                                    "brigadeiro", "coxinha", "moqueca", "vatapa",
                                    "caipirinha", "acai bowl"],
                    "Colombian":   ["colombian", "bandeja paisa", "arepas", "empanadas",
                                    "sancocho", "ajiaco", "buñuelos", "changua"],
                    "Argentinian": ["argentinian", "asado", "chimichurri", "empanadas argentina",
                                    "milanesa", "locro", "dulce de leche", "medialunas"],
                    "Venezuelan":  ["venezuelan", "pabellón criollo", "arepas venezuela",
                                    "hallacas", "cachapas", "tequeños", "caraotas"],
                    "Chilean":     ["chilean", "cazuela", "pastel de choclo", "curanto",
                                    "sopaipillas", "charquicán", "completo"],
                    "Cuban":       ["cuban", "ropa vieja", "moros y cristianos",
                                    "picadillo", "lechon cubano", "vaca frita",
                                    "tostones", "platanos maduros"],
                    "Jamaican":    ["jamaican", "jerk chicken", "jerk pork", "ackee saltfish",
                                    "curry goat", "rice and peas", "escovitch",
                                    "jamaican patty", "callaloo jamaica", "festival"],
                    "Puerto Rican": ["puerto rican", "mofongo", "pernil", "arroz con gandules",
                                     "sofrito", "pasteles", "tostones pr", "tembleque",
                                     "coquito", "asopao"],
                    "Dominican":   ["dominican", "mangu", "sancocho dominicano",
                                    "pollo guisado", "habichuelas guisadas",
                                    "tostones dominicanos", "morir soñando"],
                    "Haitian":     ["haitian", "griot", "pikliz", "riz et pois",
                                    "joumou", "akra", "pain patate", "labouyi"],
                    "Trinidad":    ["trinidadian", "doubles", "roti trinidad", "pelau",
                                    "callaloo trinidad", "bake and shark",
                                    "curry duck", "oil down"],
                },
            },
            "Central American": {
                "keywords": ["central american", "salvadoran", "guatemalan",
                             "honduran", "nicaraguan", "costa rican", "panamanian"],
                "subcategories": {
                    "Salvadoran":   ["salvadoran", "el salvador", "pupusas", "curtido",
                                     "sopa de pata", "nuégados", "atol shuco"],
                    "Guatemalan":   ["guatemalan", "pepián", "jocon", "kak'ik",
                                     "hilachas", "rellenitos", "fiambre"],
                    "Costa Rican":  ["costa rican", "gallo pinto", "casado",
                                     "olla de carne", "arroz con leche cr",
                                     "tres leches cr"],
                    "Honduran":     ["honduran", "baleadas", "sopa de caracol",
                                     "tapado", "machuca", "catrachitas"],
                    "Nicaraguan":   ["nicaraguan", "nacatamal", "vigorón", "indio viejo",
                                     "gallo pinto nicaragua", "güirilas"],
                },
            },
            "African": {
                "keywords": ["african", "west african", "east african", "ethiopian",
                             "nigerian", "ghanaian", "kenyan", "south african",
                             "senegalese", "tunisian"],
                "subcategories": {
                    "West African":        ["west african", "nigerian", "ghanaian",
                                            "jollof rice", "egusi soup", "fufu", "suya",
                                            "groundnut stew", "kelewele", "kontomire",
                                            "waakye", "ofam", "bitterleaf soup"],
                    "Senegalese":          ["senegalese", "senegal", "thieboudienne",
                                            "yassa", "mafe", "thiou", "ceebu jen",
                                            "domoda"],
                    "Ethiopian & Eritrean": ["ethiopian", "eritrean", "injera", "doro wat",
                                             "kitfo", "tibs", "shiro", "misir wat",
                                             "gomen", "ful ethiopian", "tegamino"],
                    "East African":        ["east african", "kenyan", "tanzanian", "ugandan",
                                            "nyama choma", "ugali", "sukuma wiki",
                                            "pilau kenya", "mandazi", "matoke",
                                            "githeri", "irio"],
                    "North African":       ["north african", "tunisian", "algerian", "libyan",
                                            "brik", "lablabi", "merguez", "shakshuka tunisian",
                                            "harissa tunisian", "couscous algerian"],
                    "South African":       ["south african", "braai", "bobotie", "boerewors",
                                            "bunny chow", "pap", "chakalaka", "biltong",
                                            "malva pudding", "koeksister", "potjiekos"],
                    "Moroccan":            ["moroccan", "tagine", "couscous morocco",
                                            "harissa", "chermoula", "preserved lemon",
                                            "pastilla", "mechoui", "bastilla"],
                },
            },
            "Pacific & Oceania": {
                "keywords": ["pacific", "oceania", "polynesian", "melanesian",
                             "micronesian", "maori", "fijian", "samoan", "tongan",
                             "hawaiian", "australian", "new zealand"],
                "subcategories": {
                    "Māori / New Zealand": ["maori", "new zealand", "hangi", "rewena bread",
                                            "boil-up", "paua", "kumara", "pavlova nz",
                                            "whitebait fritter", "kina", "hokey pokey"],
                    "Australian":          ["australian", "meat pie", "lamington",
                                            "anzac biscuits", "damper", "barramundi",
                                            "vegemite", "pavlova australia", "tim tam",
                                            "sausage sizzle", "chiko roll", "fairy bread"],
                    "Fijian":              ["fijian", "fiji", "kokoda", "lovo",
                                            "rourou", "palusami fiji", "duruka",
                                            "vakalolo"],
                    "Samoan":              ["samoan", "samoa", "palusami", "oka",
                                            "fa'ausi", "chop suey samoa", "sapasui",
                                            "koko alaisa", "supo esi"],
                    "Tongan":              ["tongan", "tonga", "lu pulu", "'ota 'ika",
                                            "fekkai", "faikakai topai", "kapisi pulu"],
                    "Papua New Guinean":   ["papua new guinea", "png", "mumu",
                                            "sago", "aibika", "kaukau",
                                            "taro png", "coconut crab"],
                    "Hawaiian":            ["hawaiian", "hawaii", "poke", "loco moco",
                                            "plate lunch", "kalua pig", "haupia",
                                            "spam musubi", "poi", "malasada"],
                },
            },
            "Central Asian & Caucasus": {
                "keywords": ["central asian", "caucasus", "georgian", "armenian", "uzbek",
                             "afghan", "persian", "iranian", "azerbaijani", "kazakh"],
                "subcategories": {
                    "Persian / Iranian": ["persian", "iranian", "ghormeh sabzi", "fesenjan",
                                          "tahdig", "joojeh kabab", "ash reshteh",
                                          "zereshk polo", "khoresh", "mast o khiar",
                                          "kashk-e-bademjan", "mirza ghasemi",
                                          "baghali polo"],
                    "Georgian":          ["georgian", "georgia", "khachapuri", "khinkali",
                                          "churchkhela", "ajapsandali", "satsivi",
                                          "pkhali", "lobiani", "badrijani nigvzit"],
                    "Armenian":          ["armenian", "dolma armenia", "lahmajoun",
                                          "manti armenia", "ghapama", "basturma",
                                          "harissa armenia", "nazook", "tolma"],
                    "Azerbaijani":       ["azerbaijani", "azerbaijan", "plov azerbaijan",
                                          "dolma azeri", "dushbara", "levengi",
                                          "shah plov", "gutab"],
                    "Uzbek":             ["uzbek", "uzbekistan", "plov", "samsa",
                                          "lagman", "shashlik", "manti uzbek",
                                          "non bread", "dimlama", "sumalak"],
                    "Afghan":            ["afghan", "afghanistan", "kabuli pulao", "mantu",
                                          "bolani", "qorma", "ashak", "shorwa",
                                          "aushak", "borani banjan"],
                    "Kazakh":            ["kazakh", "beshbarmak", "kuyrdak", "baursak",
                                          "kurt", "shubat", "kazy"],
                },
            },
        },
    },
    "meal_type": {
        "label": "Meal Type",
        "categories": {
-            # Keywords use two complementary sources:
+            "Breakfast":   ["breakfast", "brunch", "eggs", "pancakes", "waffles", "oatmeal", "muffin"],
-            #   1. inferred_tag phrases ("meal:X", "main:X") — indexed in recipe_browser_fts.inferred_tags.
+            "Lunch":       ["lunch", "sandwich", "wrap", "salad", "soup", "light meal"],
-            #      FTS5 tokenises "meal:Breakfast" → ["meal","breakfast"], so the quoted phrase
+            "Dinner":      ["dinner", "main dish", "entree", "main course", "supper"],
-            #      "meal:Breakfast" matches exactly that consecutive token pair.
+            "Snack":       ["snack", "appetizer", "finger food", "dip", "bite", "starter"],
-            #   2. Corpus keyword/category text — only covers the ~1,200 keyword-tagged recipes.
+            "Dessert":     ["dessert", "cake", "cookie", "pie", "sweet", "pudding", "ice cream", "brownie"],
-            #      Kept as a fallback; not the primary signal.
+            "Beverage":    ["drink", "smoothie", "cocktail", "beverage", "juice", "shake"],
-            "Breakfast": {
+            "Side Dish":   ["side dish", "side", "accompaniment", "garnish"],
                "keywords": ["meal:Breakfast", "breakfast", "brunch", "pancakes",
                             "waffles", "oatmeal", "muffin"],
                "subcategories": {
                    "Eggs":              ["meal:Breakfast", "egg", "omelette", "frittata",
                                          "quiche", "scrambled", "benedict", "shakshuka"],
                    "Pancakes & Waffles": ["pancake", "waffle", "crepe", "french toast"],
                    "Baked Goods":       ["muffin", "scone", "biscuit", "quick bread",
                                          "coffee cake", "danish"],
                    "Oats & Grains":     ["oatmeal", "granola", "porridge", "muesli",
                                          "overnight oats"],
                },
            },
            "Lunch": {
                # meal:Lunch tag covers explicitly-tagged recipes.
                # Coverage is limited — most lunch-style recipes have no distinct meal-type tag.
                "keywords": ["meal:Lunch", "lunch", "sandwich", "wrap", "salad",
                             "soup", "light meal"],
                "subcategories": {
                    "Sandwiches": ["sandwich", "sub", "hoagie", "panini", "club",
                                   "grilled cheese", "blt"],
                    "Salads":     ["salad", "grain bowl", "chopped", "caesar",
                                   "cobb"],
                    "Soups":      ["soup", "bisque", "chowder", "gazpacho",
                                   "minestrone", "lentil soup"],
                    "Wraps":      ["wrap", "burrito bowl", "pita", "lettuce wrap",
                                   "quesadilla"],
                },
            },
            "Dinner": {
                # Primary: main:X inferred tags (800k+ recipes).
                # "meal:Dinner" does not exist in the inferred-tag vocabulary — main-protein
                # tags are the best available proxy for main-course dinner recipes.
                "keywords": ["main:Chicken", "main:Beef", "main:Pork", "main:Fish",
                             "main:Pasta", "dinner", "main dish", "entree",
                             "main course", "supper"],
                "subcategories": {
                    "Chicken":    ["main:Chicken"],
                    "Beef":       ["main:Beef"],
                    "Pork":       ["main:Pork"],
                    "Fish & Seafood": ["main:Fish"],
                    "Pasta":      ["main:Pasta"],
                    "Casseroles": ["casserole", "bake", "gratin", "pot pie"],
                    "Stews":      ["stew", "braise", "slow cooker", "pot roast",
                                   "daube"],
                    "Grilled":    ["grilled", "grill", "barbecue", "kebab", "skewer"],
                },
            },
            "Snack": {
                "keywords": ["meal:Snack", "snack", "appetizer", "finger food",
                             "dip", "bite", "starter"],
                "subcategories": {
                    "Dips & Spreads": ["dip", "spread", "hummus", "guacamole",
                                       "salsa", "pate"],
                    "Finger Foods":   ["finger food", "bite", "skewer", "slider",
                                       "wing", "nugget"],
                    "Chips & Crackers": ["chip", "cracker", "crisp", "popcorn",
                                          "pretzel"],
                },
            },
            "Dessert": {
                # "sweet" removed — it matches flavor:Sweet inferred tags, causing false positives.
                "keywords": ["meal:Dessert", "dessert", "cake", "cookie", "pie",
                             "pudding", "ice cream", "brownie"],
                "subcategories": {
                    "Cakes":         ["cake", "cupcake", "layer cake", "bundt",
                                      "cheesecake", "torte"],
                    "Cookies & Bars": ["cookie", "brownie", "blondie", "bar",
                                       "biscotti", "shortbread"],
                    "Pies & Tarts":  ["pie", "tart", "galette", "cobbler", "crisp",
                                       "crumble"],
                    "Frozen":        ["ice cream", "gelato", "sorbet", "frozen dessert",
                                      "popsicle", "granita"],
                    "Puddings":      ["pudding", "custard", "mousse", "panna cotta",
                                      "flan", "creme brulee"],
                    "Candy":         ["candy", "fudge", "truffle", "brittle",
                                      "caramel", "toffee"],
                },
            },
            "Beverage": ["meal:Beverage", "drink", "smoothie", "cocktail", "beverage",
                         "juice", "shake", "lemonade"],
            "Side Dish": {
                # meal:Side Dish not in inferred-tag vocabulary.
                # main:Vegetables and main:Grains are the best proxies — will overlap
                # with some vegetarian mains, which is acceptable.
                "keywords": ["main:Vegetables", "main:Grains", "side dish", "side",
                             "pilaf", "accompaniment"],
                "subcategories": {
                    "Vegetables": ["main:Vegetables"],
                    "Grains & Rice": ["main:Grains", "rice", "pilaf", "quinoa"],
                    "Bread":      ["meal:Bread", "bread", "roll", "biscuit"],
                },
            },
        },
    },
    "dietary": {
        "label": "Dietary",
        # Primary: dietary:X inferred tags (indexed in recipe_browser_fts.inferred_tags).
        # Secondary: text tokens kept as fallback for keyword-tagged recipes.
        # IMPORTANT: Use ONLY structured dietary:X phrases here.
        # Bare text keywords like "vegan", "low-carb" also match can_be:Vegan,
        # can_be:Low-Carb etc. — those are "achievable with substitutions", not
        # "recipe already is". The structured phrase "dietary:Vegan" (consecutive
        # FTS tokens "dietary"+"vegan") does NOT match can_be:Vegan.
        "categories": {
-            "Vegetarian":   ["dietary:Vegetarian"],
+            "Vegetarian":   ["vegetarian"],
-            "Vegan":        ["dietary:Vegan"],
+            "Vegan":        ["vegan", "plant-based", "plant based"],
-            "Gluten-Free":  ["dietary:Gluten-Free"],
+            "Gluten-Free":  ["gluten-free", "gluten free", "celiac"],
-            "Low-Carb":     ["dietary:Low-Carb"],
+            "Low-Carb":     ["low-carb", "low carb", "keto", "ketogenic"],
-            "High-Protein": ["dietary:High-Protein"],
+            "High-Protein": ["high protein", "high-protein"],
-            "Low-Fat":      ["dietary:Low-Fat"],
+            "Low-Fat":      ["low-fat", "low fat", "light"],
-            "Dairy-Free":   ["dietary:Dairy-Free"],
+            "Dairy-Free":   ["dairy-free", "dairy free", "lactose"],
            "Low-Sodium":   ["dietary:Low-Sodium"],
            "Paleo":        ["dietary:Paleo"],
        },
    },
    "main_ingredient": {
        "label": "Main Ingredient",
        "categories": {
-            # keywords use exact inferred_tag strings (main:X) — indexed into recipe_browser_fts.
+            # These values match the inferred_tags written by tag_inferrer._MAIN_INGREDIENT_SIGNALS
-            "Chicken": {
+            # and indexed into recipe_browser_fts — use exact tag strings.
-                "keywords": ["main:Chicken"],
+            "Chicken":    ["main:Chicken"],
-                "subcategories": {
+            "Beef":       ["main:Beef"],
-                    "Baked":    ["baked chicken", "roast chicken", "chicken casserole",
+            "Pork":       ["main:Pork"],
-                                 "chicken bake"],
+            "Fish":       ["main:Fish"],
                    "Grilled":  ["grilled chicken", "chicken kebab", "bbq chicken",
                                 "chicken skewer"],
                    "Fried":    ["fried chicken", "chicken cutlet", "chicken schnitzel",
                                 "crispy chicken"],
                    "Stewed":   ["chicken stew", "chicken soup", "coq au vin",
                                 "chicken curry", "chicken braise"],
                },
            },
            "Beef": {
                "keywords": ["main:Beef"],
                "subcategories": {
                    "Ground Beef": ["ground beef", "hamburger", "meatball", "meatloaf",
                                    "bolognese", "burger"],
                    "Steak":       ["steak", "sirloin", "ribeye", "flank steak",
                                    "filet mignon", "t-bone"],
                    "Roasts":      ["beef roast", "pot roast", "brisket", "prime rib",
                                    "chuck roast"],
                    "Stews":       ["beef stew", "beef braise", "beef bourguignon",
                                    "short ribs"],
                },
            },
            "Pork": {
                "keywords": ["main:Pork"],
                "subcategories": {
                    "Chops":        ["pork chop", "pork loin", "pork cutlet"],
                    "Pulled/Slow":  ["pulled pork", "pork shoulder", "pork butt",
                                     "carnitas", "slow cooker pork"],
                    "Sausage":      ["sausage", "bratwurst", "chorizo", "andouille",
                                     "Italian sausage"],
                    "Ribs":         ["pork ribs", "baby back ribs", "spare ribs",
                                     "pork belly"],
                },
            },
            "Fish": {
                "keywords": ["main:Fish"],
                "subcategories": {
                    "Salmon":    ["salmon", "smoked salmon", "gravlax"],
                    "Tuna":      ["tuna", "albacore", "ahi"],
                    "White Fish": ["cod", "tilapia", "halibut", "sole", "snapper",
                                   "flounder", "bass"],
                    "Shellfish": ["shrimp", "prawn", "crab", "lobster", "scallop",
                                  "mussel", "clam", "oyster"],
                },
            },
            "Pasta":      ["main:Pasta"],
-            "Vegetables": {
+            "Vegetables": ["main:Vegetables"],
                "keywords": ["main:Vegetables"],
                "subcategories": {
                    "Root Veg":    ["potato", "sweet potato", "carrot", "beet",
                                    "parsnip", "turnip"],
                    "Leafy":       ["spinach", "kale", "chard", "arugula",
                                    "collard greens", "lettuce"],
                    "Brassicas":   ["broccoli", "cauliflower", "brussels sprouts",
                                    "cabbage", "bok choy"],
                    "Nightshades": ["tomato", "eggplant", "bell pepper", "zucchini",
                                    "squash"],
                    "Mushrooms":   ["mushroom", "portobello", "shiitake", "oyster mushroom",
                                    "chanterelle"],
                },
            },
            "Eggs":       ["main:Eggs"],
            "Legumes":    ["main:Legumes"],
            "Grains":     ["main:Grains"],
@ -629,53 +73,16 @@ DOMAINS: dict[str, dict] = {
 }
 def _get_category_def(domain: str, category: str) -> list[str] | dict | None:
    """Return the raw category definition, or None if not found."""
    return DOMAINS.get(domain, {}).get("categories", {}).get(category)
 def get_domain_labels() -> list[dict]:
    """Return [{id, label}] for all available domains."""
    return [{"id": k, "label": v["label"]} for k, v in DOMAINS.items()]
 def get_keywords_for_category(domain: str, category: str) -> list[str]:
-    """Return the keyword list for the category (top-level, covers all subcategories).
+    """Return the keyword list for a domain/category pair, or [] if not found."""
-
+    domain_data = DOMAINS.get(domain, {})
-    For flat categories returns the list directly.
+    categories = domain_data.get("categories", {})
-    For nested categories returns the 'keywords' key.
+    return categories.get(category, [])
    Returns [] if category or domain not found.
    """
    cat_def = _get_category_def(domain, category)
    if cat_def is None:
        return []
    if isinstance(cat_def, list):
        return cat_def
    return cat_def.get("keywords", [])
 def category_has_subcategories(domain: str, category: str) -> bool:
    """Return True when a category has a subcategory level."""
    cat_def = _get_category_def(domain, category)
    if not isinstance(cat_def, dict):
        return False
    return bool(cat_def.get("subcategories"))
 def get_subcategory_names(domain: str, category: str) -> list[str]:
    """Return subcategory names for a category, or [] if none exist."""
    cat_def = _get_category_def(domain, category)
    if not isinstance(cat_def, dict):
        return []
    return list(cat_def.get("subcategories", {}).keys())
 def get_keywords_for_subcategory(domain: str, category: str, subcategory: str) -> list[str]:
    """Return keyword list for a specific subcategory, or [] if not found."""
    cat_def = _get_category_def(domain, category)
    if not isinstance(cat_def, dict):
        return []
    return cat_def.get("subcategories", {}).get(subcategory, [])
 def get_category_names(domain: str) -> list[str]:
--- a/app/services/recipe/element_classifier.py
+++ b/app/services/recipe/element_classifier.py
@ -93,18 +93,7 @@ class ElementClassifier:
        return self._heuristic_profile(name)
    def classify_batch(self, names: list[str]) -> list[IngredientProfile]:
-        """Classify multiple names in one DB round-trip, falling back to heuristics."""
+        return [self.classify(n) for n in names]
        if not names:
            return []
        normalised = [n.lower().strip() for n in names]
        c = self._store._cp
        placeholders = ",".join("?" * len(normalised))
        rows = self._store._fetch_all(
            f"SELECT * FROM {c}ingredient_profiles WHERE name IN ({placeholders})",
            tuple(normalised),
        )
        by_name = {r["name"]: self._row_to_profile(r) for r in rows}
        return [by_name.get(n) or self._heuristic_profile(n) for n in normalised]
    def identify_gaps(self, profiles: list[IngredientProfile]) -> list[str]:
        """Return element names that have no coverage in the given profile list."""
--- a/app/services/recipe/grocery_links.py
+++ b/app/services/recipe/grocery_links.py
@ -13,7 +13,6 @@ Walmart is kept inline until cf-core adds Impact network support:
 Links are always generated (plain URLs are useful even without affiliate IDs).
 Walmart links only appear when WALMART_AFFILIATE_ID is set.
 Instacart and Walmart are US/CA-only; other locales get Amazon only.
 """
 from __future__ import annotations
@ -24,26 +23,18 @@ from urllib.parse import quote_plus
 from circuitforge_core.affiliates import wrap_url
 from app.models.schemas.recipe import GroceryLink
 from app.services.recipe.locale_config import get_locale
 logger = logging.getLogger(__name__)
-def _amazon_link(ingredient: str, locale: str) -> GroceryLink:
+def _amazon_fresh_link(ingredient: str) -> GroceryLink:
    cfg = get_locale(locale)
    q = quote_plus(ingredient)
-    domain = cfg["amazon_domain"]
+    base = f"https://www.amazon.com/s?k={q}&i=amazonfresh"
-    dept = cfg["amazon_grocery_dept"]
+    return GroceryLink(ingredient=ingredient, retailer="Amazon Fresh", url=wrap_url(base, "amazon"))
    base = f"https://www.{domain}/s?k={q}&{dept}"
    retailer = "Amazon" if locale != "us" else "Amazon Fresh"
    return GroceryLink(ingredient=ingredient, retailer=retailer, url=wrap_url(base, "amazon"))
-def _instacart_link(ingredient: str, locale: str) -> GroceryLink:
+def _instacart_link(ingredient: str) -> GroceryLink:
    q = quote_plus(ingredient)
    if locale == "ca":
        base = f"https://www.instacart.ca/store/s?k={q}"
    else:
    base = f"https://www.instacart.com/store/s?k={q}"
    return GroceryLink(ingredient=ingredient, retailer="Instacart", url=wrap_url(base, "instacart"))
@ -59,28 +50,26 @@ def _walmart_link(ingredient: str, affiliate_id: str) -> GroceryLink:
 class GroceryLinkBuilder:
-    def __init__(self, tier: str = "free", has_byok: bool = False, locale: str = "us") -> None:
+    def __init__(self, tier: str = "free", has_byok: bool = False) -> None:
        self._tier = tier
        self._locale = locale
        self._locale_cfg = get_locale(locale)
        self._walmart_id = os.environ.get("WALMART_AFFILIATE_ID", "").strip()
    def build_links(self, ingredient: str) -> list[GroceryLink]:
        """Build grocery deeplinks for a single ingredient.
-        Amazon link is always included, routed to the user's locale domain.
+        Amazon Fresh and Instacart links are always included; wrap_url handles
-        Instacart and Walmart are only shown where they operate (US/CA).
+        affiliate ID injection (or returns a plain URL if none is configured).
-        wrap_url handles affiliate ID injection for supported programs.
+        Walmart requires WALMART_AFFILIATE_ID to be set (Impact network uses a
        path-based redirect that doesn't degrade cleanly to a plain URL).
        """
        if not ingredient.strip():
            return []
-        links: list[GroceryLink] = [_amazon_link(ingredient, self._locale)]
+        links: list[GroceryLink] = [
-
+            _amazon_fresh_link(ingredient),
-        if self._locale_cfg["instacart"]:
+            _instacart_link(ingredient),
-            links.append(_instacart_link(ingredient, self._locale))
+        ]
-
+        if self._walmart_id:
        if self._locale_cfg["walmart"] and self._walmart_id:
            links.append(_walmart_link(ingredient, self._walmart_id))
        return links
--- a/app/services/recipe/llm_recipe.py
+++ b/app/services/recipe/llm_recipe.py
@ -1,14 +1,13 @@
 """LLM-driven recipe generator for Levels 3 and 4."""
 from __future__ import annotations
 import asyncio
 import logging
 import os
 import re
 from contextlib import nullcontext
-from typing import TYPE_CHECKING, AsyncGenerator
+from typing import TYPE_CHECKING
-from openai import AsyncOpenAI, OpenAI
+from openai import OpenAI
 if TYPE_CHECKING:
    from app.db.store import Store
@ -69,9 +68,6 @@ class LLMRecipeGenerator:
        if allergy_list:
            lines.append(f"IMPORTANT — must NOT contain: {', '.join(allergy_list)}")
        if req.exclude_ingredients:
            lines.append(f"IMPORTANT — user does not want these today: {', '.join(req.exclude_ingredients)}. Do not include them.")
        lines.append("")
        lines.append(f"Covered culinary elements: {', '.join(covered_elements) or 'none'}")
@ -128,9 +124,6 @@ class LLMRecipeGenerator:
        if allergy_list:
            lines.append(f"Must NOT contain: {', '.join(allergy_list)}")
        if req.exclude_ingredients:
            lines.append(f"Do not use today: {', '.join(req.exclude_ingredients)}")
        unit_line = (
            "Use metric units (grams, ml, Celsius) for all quantities and temperatures."
            if req.unit_system == "metric"
@ -151,7 +144,6 @@ class LLMRecipeGenerator:
        return "\n".join(lines)
    _SERVICE_TYPE = "cf-text"
    _MODEL_CANDIDATES = ["granite-4.1-8b", "deepseek-r1-1.5b"]
    _TTL_S = 300.0
    _CALLER = "kiwi-recipe"
@ -169,10 +161,8 @@ class LLMRecipeGenerator:
                client = CFOrchClient(cf_orch_url)
                return client.allocate(
                    service=self._SERVICE_TYPE,
                    model_candidates=self._MODEL_CANDIDATES,
                    ttl_s=self._TTL_S,
                    caller=self._CALLER,
                    pipeline=os.environ.get("CF_APP_NAME") or None,
                )
            except Exception as exc:
                logger.debug("CFOrchClient init failed, falling back to direct URL: %s", exc)
@ -183,12 +173,7 @@ class LLMRecipeGenerator:
        With CF_ORCH_URL set: acquires a vLLM allocation via CFOrchClient and
        calls the OpenAI-compatible API directly against the allocated service URL.
-        Falls back to LLMRouter when:
+        Allocation failure falls through to LLMRouter rather than silently returning "".
        - Allocation succeeded but the service is cold (warm=False) — avoids
          making the user wait for model load; LLMRouter uses Ollama which is
          already running.
        - Allocation succeeded but the connection to the service URL fails — the
          agent may have registered the service but failed to start it.
        Without CF_ORCH_URL: uses LLMRouter directly.
        """
        ctx = self._get_llm_context()
@ -196,33 +181,11 @@ class LLMRecipeGenerator:
        try:
            alloc = ctx.__enter__()
        except Exception as exc:
            msg = str(exc)
            # 429 = coordinator at capacity (all nodes at max_concurrent limit).
            # Don't fall back to LLMRouter — it's also overloaded and the slow
            # fallback causes nginx 504s. Return "" fast so the caller degrades
            # gracefully (empty recipe result) rather than timing out.
            if "429" in msg or "max_concurrent" in msg.lower():
                logger.info("cf-orch at capacity — returning empty result (graceful degradation)")
                if ctx is not None:
                    try:
                        ctx.__exit__(None, None, None)
                    except Exception:
                        pass
                return ""
            logger.debug("cf-orch allocation failed, falling back to LLMRouter: %s", exc)
            ctx = None  # __enter__ raised — do not call __exit__
        try:
            if alloc is not None:
                # Skip cold services — model not yet loaded means the user would
                # wait 60–120 s for model load before any response. Use LLMRouter
                # (Ollama) instead, which is already warm on the host.
                if not alloc.warm:
                    logger.info(
                        "cf-orch vllm allocated but cold (warm=False) — releasing and falling back to LLMRouter"
                    )
                    raise RuntimeError("vllm cold")
                base_url = alloc.url.rstrip("/") + "/v1"
                client = OpenAI(base_url=base_url, api_key="any")
                model = alloc.model or "__auto__"
@ -238,20 +201,6 @@ class LLMRecipeGenerator:
                return LLMRouter().complete(prompt)
        except Exception as exc:
            logger.error("LLM call failed: %s", exc)
            # When cf-orch gave us an allocation but the service is unreachable
            # (cold skip, connection refused, or other error), fall back to
            # LLMRouter rather than silently returning empty.
            # Skip "vllm" in the fallback order — that backend also routes through
            # cf-orch, which would trigger a second (wasted) cold allocation.
            if alloc is not None:
                logger.info("Falling back to LLMRouter after vllm failure")
                try:
                    from circuitforge_core.llm.router import LLMRouter
                    router = LLMRouter()
                    _order = [b for b in (router.config.get("fallback_order") or []) if b != "vllm"]
                    return router.complete(prompt, fallback_order=_order or None)
                except Exception as fallback_exc:
                    logger.error("LLMRouter fallback also failed: %s", fallback_exc)
            return ""
        finally:
            if ctx is not None:
@ -388,91 +337,3 @@ class LLMRecipeGenerator:
            suggestions=[suggestion],
            element_gaps=gaps,
        )
    async def stream_generate(
        self,
        req: RecipeRequest,
        profiles: list,
        gaps: list[str],
    ) -> AsyncGenerator[str, None]:
        """Stream LLM tokens for L3/L4. Yields raw text chunks as they arrive.
        Tries cf-orch warm vllm first; falls back to Ollama via AsyncOpenAI.
        When neither is reachable, falls back to blocking _call_llm and yields
        the complete response as a single chunk so the caller always gets output.
        """
        if req.level == 4:
            prompt = self.build_level4_prompt(req)
        else:
            prompt = self.build_level3_prompt(req, profiles, gaps)
        # Phase 1: try cf-orch warm vllm (sync allocation, wrapped in thread)
        alloc_info = await asyncio.to_thread(self._try_alloc_for_stream)
        if alloc_info is not None:
            alloc, ctx = alloc_info
            try:
                async for token in self._stream_openai_compat(
                    alloc.url.rstrip("/") + "/v1", "any", alloc.model or "__auto__", prompt
                ):
                    yield token
                return
            except Exception as exc:
                logger.debug("cf-orch stream failed, falling back to Ollama: %s", exc)
            finally:
                await asyncio.to_thread(lambda: _safe_exit(ctx))
        # Phase 2: Ollama streaming via OpenAI-compat API
        from circuitforge_core.llm.router import LLMRouter
        router = LLMRouter()
        ollama = router.config.get("backends", {}).get("ollama")
        if ollama and ollama.get("enabled", True):
            base_url = ollama["base_url"]
            model = ollama.get("model", "llama3")
            try:
                async for token in self._stream_openai_compat(base_url, "any", model, prompt):
                    yield token
                return
            except Exception as exc:
                logger.warning("Ollama streaming failed, falling back to blocking: %s", exc)
        # Phase 3: blocking fallback — yields full response at once
        result = await asyncio.to_thread(self._call_llm, prompt)
        if result:
            yield result
    def _try_alloc_for_stream(self):
        """Attempt cf-orch allocation synchronously; return (alloc, ctx) or None."""
        ctx = self._get_llm_context()
        try:
            alloc = ctx.__enter__()
            if alloc is not None and alloc.warm:
                return alloc, ctx
            # Not warm — release and signal fallback
            _safe_exit(ctx)
        except Exception as exc:
            logger.debug("cf-orch alloc for stream failed: %s", exc)
        return None
    @staticmethod
    async def _stream_openai_compat(
        base_url: str, api_key: str, model: str, prompt: str
    ) -> AsyncGenerator[str, None]:
        client = AsyncOpenAI(base_url=base_url, api_key=api_key)
        if model == "__auto__":
            models = await client.models.list()
            model = models.data[0].id
        stream = await client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            stream=True,
        )
        async for chunk in stream:
            if chunk.choices and chunk.choices[0].delta.content:
                yield chunk.choices[0].delta.content
 def _safe_exit(ctx) -> None:
    try:
        ctx.__exit__(None, None, None)
    except Exception:
        pass
--- a/app/services/recipe/locale_config.py
+++ b/app/services/recipe/locale_config.py
@ -1,160 +0,0 @@
 """
 Shopping locale configuration.
 Maps a locale key to Amazon domain, currency metadata, and retailer availability.
 Instacart and Walmart are US/CA-only; all other locales get Amazon only.
 Amazon Fresh (&i=amazonfresh) is US-only — international domains use the general
 grocery department (&rh=n:16310101) where available, plain search elsewhere.
 """
 from __future__ import annotations
 from typing import TypedDict
 class LocaleConfig(TypedDict):
    amazon_domain: str
    amazon_grocery_dept: str   # URL fragment for grocery department on this locale's site
    currency_code: str
    currency_symbol: str
    instacart: bool
    walmart: bool
 LOCALES: dict[str, LocaleConfig] = {
    "us": {
        "amazon_domain":       "amazon.com",
        "amazon_grocery_dept": "i=amazonfresh",
        "currency_code":       "USD",
        "currency_symbol":     "$",
        "instacart":           True,
        "walmart":             True,
    },
    "ca": {
        "amazon_domain":       "amazon.ca",
        "amazon_grocery_dept": "rh=n:6967215011",   # Grocery dept on .ca  # gitleaks:allow
        "currency_code":       "CAD",
        "currency_symbol":     "CA$",
        "instacart":           True,
        "walmart":             False,
    },
    "gb": {
        "amazon_domain":       "amazon.co.uk",
        "amazon_grocery_dept": "rh=n:340831031",    # Grocery dept on .co.uk
        "currency_code":       "GBP",
        "currency_symbol":     "£",
        "instacart":           False,
        "walmart":             False,
    },
    "au": {
        "amazon_domain":       "amazon.com.au",
        "amazon_grocery_dept": "rh=n:5765081051",   # Pantry/grocery on .com.au  # gitleaks:allow
        "currency_code":       "AUD",
        "currency_symbol":     "A$",
        "instacart":           False,
        "walmart":             False,
    },
    "nz": {
        # NZ has no Amazon storefront — route to .com.au as nearest option
        "amazon_domain":       "amazon.com.au",
        "amazon_grocery_dept": "rh=n:5765081051",  # gitleaks:allow
        "currency_code":       "NZD",
        "currency_symbol":     "NZ$",
        "instacart":           False,
        "walmart":             False,
    },
    "de": {
        "amazon_domain":       "amazon.de",
        "amazon_grocery_dept": "rh=n:340843031",    # Lebensmittel & Getränke
        "currency_code":       "EUR",
        "currency_symbol":     "€",
        "instacart":           False,
        "walmart":             False,
    },
    "fr": {
        "amazon_domain":       "amazon.fr",
        "amazon_grocery_dept": "rh=n:197858031",
        "currency_code":       "EUR",
        "currency_symbol":     "€",
        "instacart":           False,
        "walmart":             False,
    },
    "it": {
        "amazon_domain":       "amazon.it",
        "amazon_grocery_dept": "rh=n:525616031",
        "currency_code":       "EUR",
        "currency_symbol":     "€",
        "instacart":           False,
        "walmart":             False,
    },
    "es": {
        "amazon_domain":       "amazon.es",
        "amazon_grocery_dept": "rh=n:599364031",
        "currency_code":       "EUR",
        "currency_symbol":     "€",
        "instacart":           False,
        "walmart":             False,
    },
    "nl": {
        "amazon_domain":       "amazon.nl",
        "amazon_grocery_dept": "rh=n:16584827031",
        "currency_code":       "EUR",
        "currency_symbol":     "€",
        "instacart":           False,
        "walmart":             False,
    },
    "se": {
        "amazon_domain":       "amazon.se",
        "amazon_grocery_dept": "rh=n:20741393031",
        "currency_code":       "SEK",
        "currency_symbol":     "kr",
        "instacart":           False,
        "walmart":             False,
    },
    "jp": {
        "amazon_domain":       "amazon.co.jp",
        "amazon_grocery_dept": "rh=n:2246283051",  # gitleaks:allow
        "currency_code":       "JPY",
        "currency_symbol":     "¥",
        "instacart":           False,
        "walmart":             False,
    },
    "in": {
        "amazon_domain":       "amazon.in",
        "amazon_grocery_dept": "rh=n:2454178031",  # gitleaks:allow
        "currency_code":       "INR",
        "currency_symbol":     "₹",
        "instacart":           False,
        "walmart":             False,
    },
    "mx": {
        "amazon_domain":       "amazon.com.mx",
        "amazon_grocery_dept": "rh=n:10737659011",
        "currency_code":       "MXN",
        "currency_symbol":     "MX$",
        "instacart":           False,
        "walmart":             False,
    },
    "br": {
        "amazon_domain":       "amazon.com.br",
        "amazon_grocery_dept": "rh=n:17878420011",
        "currency_code":       "BRL",
        "currency_symbol":     "R$",
        "instacart":           False,
        "walmart":             False,
    },
    "sg": {
        "amazon_domain":       "amazon.sg",
        "amazon_grocery_dept": "rh=n:6981647051",  # gitleaks:allow
        "currency_code":       "SGD",
        "currency_symbol":     "S$",
        "instacart":           False,
        "walmart":             False,
    },
 }
 DEFAULT_LOCALE = "us"
 def get_locale(key: str) -> LocaleConfig:
    """Return locale config for *key*, falling back to US if unknown."""
    return LOCALES.get(key, LOCALES[DEFAULT_LOCALE])
--- a/app/services/recipe/recipe_engine.py
+++ b/app/services/recipe/recipe_engine.py
@ -20,13 +20,10 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
    from app.db.store import Store
-from app.models.schemas.recipe import GroceryLink, NutritionPanel, RecipeRequest, RecipeResult, RecipeSuggestion, StepAnalysis, TimeEffortProfile, SwapCandidate
+from app.models.schemas.recipe import GroceryLink, NutritionPanel, RecipeRequest, RecipeResult, RecipeSuggestion, SwapCandidate
 from app.services.recipe.element_classifier import ElementClassifier
 from app.services.recipe.grocery_links import GroceryLinkBuilder
 from app.services.recipe.substitution_engine import SubstitutionEngine
 from app.services.recipe.sensory import SensoryExclude, build_sensory_exclude, passes_sensory_filter
 from app.services.recipe.time_effort import parse_time_effort
 from app.services.recipe.reranker import rerank_suggestions
 _LEFTOVER_DAILY_MAX_FREE = 5
@ -36,38 +33,6 @@ _SWAP_STOPWORDS = frozenset({
    "to", "from", "at", "by", "as", "on",
 })
 # Marketing / prep / packaging words stripped when tokenising product-label names
 # into individual ingredient tokens.  Parallel to Store._FTS_TOKEN_STOPWORDS —
 # both lists should agree.  Kept here to avoid a circular import at runtime.
 _PRODUCT_TOKEN_STOPWORDS = frozenset({
    # Basic English stopwords
    "a", "an", "the", "of", "in", "for", "with", "and", "or", "to",
    "from", "at", "by", "as", "on", "into",
    # Brand / marketing words that appear in product names
    "lean", "cuisine", "healthy", "choice", "stouffer", "original",
    "classic", "deluxe", "homestyle", "family", "style", "grade",
    "premium", "select", "natural", "organic", "fresh", "lite",
    "ready", "quick", "easy", "instant", "microwave", "frozen",
    "brand", "size", "large", "small", "medium", "extra",
    # Plant-based / alt-meat brand names
    "daring", "gardein", "morningstar", "lightlife", "tofurky",
    "quorn", "omni", "nuggs", "simulate",
    # Preparation states
    "cut", "diced", "sliced", "chopped", "minced", "shredded",
    "cooked", "raw", "whole", "boneless", "skinless", "trimmed",
    "pre", "prepared", "marinated", "seasoned", "breaded", "battered",
    "grilled", "roasted", "smoked", "canned", "dried", "dehydrated",
    "pieces", "piece", "strips", "strip", "chunks", "chunk",
    "fillets", "fillet", "cutlets", "cutlet", "tenders", "nuggets",
    # Units / packaging
    "oz", "lb", "lbs", "pkg", "pack", "box", "can", "bag", "jar",
    # Adjectives that aren't ingredients
    "firm", "soft", "silken", "hard", "crispy", "crunchy", "smooth",
    "mild", "spicy", "hot", "sweet", "savory", "unsalted", "salted",
    "low", "high", "reduced", "free", "fat", "sodium", "sugar", "calorie",
    "dairy", "gluten", "vegan", "plant", "based", "free",
 })
 # Maps product-label substrings to recipe-corpus canonical terms.
 # Kept in sync with Store._FTS_SYNONYMS — both must agree on canonical names.
 # Used to expand pantry_set so single-word recipe ingredients can match
@ -190,56 +155,6 @@ _PANTRY_LABEL_SYNONYMS: dict[str, str] = {
 }
 # When a pantry item is in a secondary state (e.g. bread → "stale"), expand
 # the pantry set with terms that recipe ingredients commonly use to describe
 # that state.  This lets "stale bread" in a recipe ingredient match a pantry
 # entry that is simply called "Bread" but is past its nominal use-by date.
 # Each key is (category_in_SECONDARY_WINDOW, label_returned_by_secondary_state).
 # Values are additional strings added to the pantry set for FTS coverage.
 _SECONDARY_STATE_SYNONYMS: dict[tuple[str, str], list[str]] = {
    # ── Existing entries (corrected) ─────────────────────────────────────────
    ("bread", "stale"):         ["stale bread", "day-old bread", "old bread", "dried bread"],
    ("bakery", "day-old"):      ["day-old bread", "stale bread", "stale pastry",
                                  "day-old croissant", "stale croissant", "day-old muffin",
                                  "stale cake", "old pastry", "day-old baguette"],
    ("bananas", "overripe"):    ["overripe bananas", "very ripe bananas", "spotty bananas",
                                  "brown bananas", "black bananas", "mushy bananas",
                                  "mashed banana", "ripe bananas"],
    ("milk", "sour"):           ["sour milk", "slightly sour milk", "buttermilk",
                                  "soured milk", "off milk", "milk gone sour"],
    ("dairy", "sour"):          ["sour milk", "slightly sour milk", "soured milk"],
    ("cheese", "rind-ready"):   ["parmesan rind", "cheese rind", "aged cheese",
                                  "hard cheese rind", "parmigiano rind", "grana padano rind",
                                  "pecorino rind", "dry cheese"],
    ("rice", "day-old"):        ["day-old rice", "leftover rice", "cold rice", "cooked rice",
                                  "old rice"],
    ("tortillas", "stale"):     ["stale tortillas", "dried tortillas", "day-old tortillas"],
    # ── New entries ──────────────────────────────────────────────────────────
    ("apples", "soft"):         ["soft apples", "mealy apples", "overripe apples",
                                  "bruised apples", "mushy apple"],
    ("leafy_greens", "wilting"):["wilted spinach", "wilted greens", "limp lettuce",
                                  "wilted kale", "tired greens"],
    ("tomatoes", "soft"):       ["overripe tomatoes", "very ripe tomatoes", "ripe tomatoes",
                                  "soft tomatoes", "bruised tomatoes"],
    ("cooked_pasta", "day-old"):["leftover pasta", "cooked pasta", "day-old pasta",
                                  "cold pasta", "pre-cooked pasta"],
    ("cooked_potatoes", "day-old"): ["leftover potatoes", "cooked potatoes", "day-old potatoes",
                                      "mashed potatoes", "baked potatoes"],
    ("yogurt", "tangy"):        ["sour yogurt", "tangy yogurt", "past-date yogurt",
                                  "older yogurt", "well-cultured yogurt"],
    ("cream", "sour"):          ["slightly soured cream", "cultured cream",
                                  "heavy cream gone sour", "soured cream"],
    ("wine", "open"):           ["open wine", "leftover wine", "day-old wine",
                                  "cooking wine", "red wine", "white wine"],
    ("cooked_beans", "day-old"):["leftover beans", "cooked beans", "day-old beans",
                                  "cold beans", "pre-cooked beans",
                                  "cooked chickpeas", "cooked lentils"],
    ("cooked_meat", "leftover"):["leftover chicken", "shredded chicken", "leftover beef",
                                  "cooked chicken", "pulled chicken", "leftover pork",
                                  "cooked meat", "rotisserie chicken"],
 }
 # Matches leading quantity/unit prefixes in recipe ingredient strings,
 # e.g. "2 cups flour" → "flour", "1/2 c. ketchup" → "ketchup",
 #      "3 oz. butter" → "butter"
@ -369,24 +284,14 @@ def _prep_note_for(ingredient: str) -> str | None:
    return template.format(ingredient=ingredient_name)
-def _expand_pantry_set(
+def _expand_pantry_set(pantry_items: list[str]) -> set[str]:
    pantry_items: list[str],
    secondary_pantry_items: dict[str, str] | None = None,
 ) -> set[str]:
    """Return pantry_set expanded with canonical recipe-corpus synonyms.
    For each pantry item, checks _PANTRY_LABEL_SYNONYMS for substring matches
    and adds the canonical form.  This lets single-word recipe ingredients
    ("hamburger", "chicken") match product-label pantry entries
    ("burger patties", "rotisserie chicken").
    If secondary_pantry_items is provided (product_name → state label), items
    in a secondary state also receive state-specific synonym expansion so that
    recipe ingredients like "stale bread" or "day-old rice" are matched.
    """
    from app.services.expiration_predictor import ExpirationPredictor
    _predictor = ExpirationPredictor()
    expanded: set[str] = set()
    for item in pantry_items:
        lower = item.lower().strip()
@ -394,22 +299,6 @@ def _expand_pantry_set(
        for pattern, canonical in _PANTRY_LABEL_SYNONYMS.items():
            if pattern in lower:
                expanded.add(canonical)
        # Extract individual ingredient tokens from multi-word product names.
        # "Organic Extra Firm Tofu" → adds "tofu"; "Brown Basmati Rice" → adds "rice".
        # This catches plain ingredients that _PANTRY_LABEL_SYNONYMS doesn't translate.
        for token in lower.split():
            if len(token) >= 4 and token not in _PRODUCT_TOKEN_STOPWORDS:
                expanded.add(token)
        # Secondary state expansion — adds terms like "stale bread", "day-old rice"
        if secondary_pantry_items and item in secondary_pantry_items:
            state_label = secondary_pantry_items[item]
            category = _predictor.get_category_from_product(item)
            if category:
                synonyms = _SECONDARY_STATE_SYNONYMS.get((category, state_label), [])
                expanded.update(synonyms)
    return expanded
@ -686,21 +575,6 @@ def _estimate_time_min(directions: list[str], complexity: str) -> int:
    return max(10, 20 + steps * 4)  # moderate
 def _within_time(directions: list[str], max_total_min: int) -> bool:
    """Return True if parsed total time (active + passive) is within max_total_min.
    Graceful degradation:
    - Empty directions -> True (no data, don't hide)
    - total_min == 0 (no time signals found) -> True (unparseable, don't hide)
    """
    if not directions:
        return True
    profile = parse_time_effort(directions)
    if profile.total_min == 0:
        return True
    return profile.total_min <= max_total_min
 def _classify_method_complexity(
    directions: list[str],
    available_equipment: list[str] | None = None,
@ -760,8 +634,7 @@ class RecipeEngine:
        profiles = self._classifier.classify_batch(req.pantry_items)
        gaps = self._classifier.identify_gaps(profiles)
-        pantry_set = _expand_pantry_set(req.pantry_items, req.secondary_pantry_items or None)
+        pantry_set = _expand_pantry_set(req.pantry_items)
        exclude_set = _expand_pantry_set(req.exclude_ingredients) if req.exclude_ingredients else set()
        if req.level >= 3:
            from app.services.recipe.llm_recipe import LLMRecipeGenerator
@ -775,13 +648,9 @@ class RecipeEngine:
        #   - match ratio: require ≥60% ingredient coverage to avoid low-signal results
        _l1 = req.level == 1 and not req.shopping_mode
        nf = req.nutrition_filters
        # L1 uses a larger candidate pool — the ratio gate below will prune
        # aggressively anyway, so we need more raw candidates to end up with
        # enough results for a packaged-food / plant-based pantry.
        _fts_limit = 60 if _l1 else 20
        rows = self._store.search_recipes_by_ingredients(
            req.pantry_items,
-            limit=_fts_limit,
+            limit=20,
            category=req.category or None,
            max_calories=nf.max_calories,
            max_sugar_g=nf.max_sugar_g,
@ -792,21 +661,14 @@ class RecipeEngine:
        )
        # L1 strict defaults: cap missing ingredients and require a minimum ratio.
        # 0.35 allows ~1/3 ingredient coverage — low enough for packaged/plant-based
        # pantries that rarely match raw-ingredient corpus recipes 1:1, but still
        # filters out recipes where only one common staple matched.
        _L1_MAX_MISSING_DEFAULT = 2
-        _L1_MIN_MATCH_RATIO = 0.35
+        _L1_MIN_MATCH_RATIO = 0.6
        effective_max_missing = req.max_missing
        if _l1 and effective_max_missing is None:
            effective_max_missing = _L1_MAX_MISSING_DEFAULT
        # Load sensory preferences -- applied as silent post-score filter
        _sensory_prefs_json = self._store.get_setting("sensory_preferences")
        _sensory_exclude = build_sensory_exclude(_sensory_prefs_json)
        suggestions = []
-        hard_day_tier_map: dict[int, int] = {}  # recipe_id -> tier when hard_day_mode
+        hard_day_tier_map: dict[int, int] = {}  # recipe_id → tier when hard_day_mode
        for row in rows:
            ingredient_names: list[str] = row.get("ingredient_names") or []
@ -816,15 +678,6 @@ class RecipeEngine:
                except Exception:
                    ingredient_names = []
            # Skip recipes that require any ingredient the user has excluded.
            if exclude_set and any(_ingredient_in_pantry(n, exclude_set) for n in ingredient_names):
                continue
            # Sensory filter -- silent exclusion of recipes exceeding user tolerance
            if not _sensory_exclude.is_empty():
                if not passes_sensory_filter(row.get("sensory_tags"), _sensory_exclude):
                    continue
            # Compute missing ingredients, detecting pantry coverage first.
            # When covered, collect any prep-state annotations (e.g. "melted butter"
            # → note "Melt the butter before starting.") to surface separately.
@ -880,14 +733,9 @@ class RecipeEngine:
                except Exception:
                    directions = [directions]
-            # Compute complexity + parse time effort once — reused for filters and response.
+            # Compute complexity for every suggestion (used for badge + filter).
            row_complexity = _classify_method_complexity(directions, available_equipment)
            row_time_min = _estimate_time_min(directions, row_complexity)
            row_time_effort = parse_time_effort(
                directions,
                ingredients=row.get("ingredients") or [],
                ingredient_names=row.get("ingredient_names") or [],
            )
            # Filter and tier-rank by hard_day_mode
            if req.hard_day_mode:
@ -907,25 +755,6 @@ class RecipeEngine:
            if req.max_time_min is not None and row_time_min > req.max_time_min:
                continue
            # Total time filter (kiwi#52).
            # Prefer parsed time extracted from direction text (explicit "15 minutes" mentions).
            # When directions contain no parseable time signals, fall back to the
            # step-count estimate so the filter still has teeth on the corpus majority.
            if req.max_total_min is not None:
                if row_time_effort.total_min > 0:
                    if row_time_effort.total_min > req.max_total_min:
                        continue
                elif row_time_min > req.max_total_min:
                    continue
            # Active (hands-on) time filter — independent of total time.
            # Lets users request "≤30 min hands-on, any total" to include slow braises.
            # Skips recipes where active_min == 0 (no time signals parsed) to avoid
            # hiding valid results when the parser couldn't extract timing.
            if req.max_active_min is not None and row_time_effort.active_min > 0:
                if row_time_effort.active_min > req.max_active_min:
                    continue
            # Level 2: also add dietary constraint swaps from substitution_pairs
            if req.level == 2 and req.constraints:
                for ing in ingredient_names:
@ -963,21 +792,6 @@ class RecipeEngine:
                v is not None
                for v in (nutrition.calories, nutrition.sugar_g, nutrition.carbs_g)
            )
            te = TimeEffortProfile(
                active_min=row_time_effort.active_min,
                passive_min=row_time_effort.passive_min,
                total_min=row_time_effort.total_min,
                effort_label=row_time_effort.effort_label,
                equipment=list(row_time_effort.equipment),
                step_analyses=[
                    StepAnalysis(
                        is_passive=sa.is_passive,
                        detected_minutes=sa.detected_minutes,
                        prep_min=sa.prep_min,
                    )
                    for sa in row_time_effort.step_analyses
                ],
            )
            suggestions.append(RecipeSuggestion(
                id=row["id"],
                title=row["title"],
@ -986,31 +800,19 @@ class RecipeEngine:
                swap_candidates=swap_candidates,
                matched_ingredients=matched,
                missing_ingredients=missing,
                directions=directions,
                prep_notes=sorted(prep_note_set),
                level=req.level,
                nutrition=nutrition if has_nutrition else None,
                source_url=_build_source_url(row),
                complexity=row_complexity,
                estimated_time_min=row_time_min,
                time_effort=te,
            ))
-        # Sort corpus results.
+        # Sort corpus results — assembly templates are now served from a dedicated tab.
-        # Paid+ tier: cross-encoder reranker orders by full pantry + dietary fit.
+        # Hard day mode: primary sort by tier (0=premade, 1=simple, 2=moderate),
-        # Free tier (or reranker failure): overlap sort with hard_day_mode tier grouping.
+        # then by match_count within each tier.
-        reranked = rerank_suggestions(req, suggestions)
+        # Normal mode: sort by match_count descending.
        if reranked is not None:
            # Reranker provided relevance order. In hard_day_mode, still respect
            # tier grouping as primary sort; reranker order applies within each tier.
        if req.hard_day_mode and hard_day_tier_map:
                suggestions = sorted(
                    reranked,
                    key=lambda s: hard_day_tier_map.get(s.id, 1),
                )
            else:
                suggestions = reranked
        elif req.hard_day_mode and hard_day_tier_map:
            suggestions = sorted(
                suggestions,
                key=lambda s: (hard_day_tier_map.get(s.id, 1), -s.match_count),
--- a/app/services/recipe/recipe_scanner.py
+++ b/app/services/recipe/recipe_scanner.py
@ -1,524 +0,0 @@
 """Recipe scanner service (kiwi#9).
 Extracts structured recipe data from one or more photos of recipe cards,
 cookbook pages, or handwritten notes.
 Pipeline:
  photo(s) -> EXIF correction -> VLM extraction -> JSON parse -> pantry cross-ref
 Vision backend priority (mirrors receipt OCR pattern):
  1. cf-orch vision service (if CF_ORCH_URL set)
  2. Local Qwen2.5-VL (if GPU available)
  3. Anthropic API (BYOK -- if ANTHROPIC_API_KEY set)
 BSL 1.1 -- requires Paid tier or BYOK.
 """
 from __future__ import annotations
 import base64
 import io
 import json
 import logging
 import os
 import re
 from collections.abc import Callable
 from dataclasses import dataclass
 from pathlib import Path
 logger = logging.getLogger(__name__)
 # Maximum number of photos per scan call (to limit VLM context / VRAM)
 MAX_IMAGES = 4
 # VLM prompt -- adapted from tests/fixtures/recipe_scan/extract_test.py
 _EXTRACTION_PROMPT = """
 You are extracting a recipe from a photograph of a recipe card, cookbook page, or handwritten note.
 If two or more images are provided, treat them as a single recipe across multiple pages
 (e.g. ingredients on page 1, directions on page 2).
 Return a single JSON object with these fields:
 - title: recipe name (string)
 - subtitle: any secondary title or serving suggestion e.g. "with Broccoli & Ranch Dressing" (string or null)
 - servings: serving size if shown, as a string e.g. "2", "4-6" (string or null)
 - cook_time: total cook time if shown, e.g. "15 min", "1 hour" (string or null)
 - source_note: any attribution text like "From Betty Crocker" or "Purple Carrot" (string or null)
 - ingredients: array of ingredient objects, each with:
  - name: normalized generic ingredient name, lowercase, no quantities, no brand names
    (e.g. "Follow Your Heart Vegan Ranch" becomes "ranch dressing")
  - qty: quantity as a string, preserving fractions e.g. "1/2", a quarter symbol (string or null)
  - unit: unit of measure, null for countable items (e.g. "3 eggs" has unit: null)
  - raw: the original ingredient line verbatim, exactly as it appears
 - steps: ordered array of instruction strings, one distinct step per element
 - notes: any tips, substitutions, storage instructions, or variations (string or null)
 - confidence: "high" if text is clear and complete, "medium" if some parts are uncertain,
  "low" if mostly handwritten or significantly degraded
 - warnings: array of strings describing anything the user should double-check
  (e.g. "Directions appear to continue on another page not shown")
 Return only valid JSON. No markdown fences. No explanation outside the JSON.
 If the image does not appear to be a recipe at all, return: {"error": "not_a_recipe"}
 """.strip()
 # ── Data types ─────────────────────────────────────────────────────────────────
@dataclass
 class ScannedIngredient:
    name: str
    qty: str | None = None
    unit: str | None = None
    raw: str | None = None
    in_pantry: bool = False
@dataclass
 class ScannedRecipeResult:
    title: str | None
    subtitle: str | None
    servings: str | None
    cook_time: str | None
    source_note: str | None
    ingredients: list[ScannedIngredient]
    steps: list[str]
    notes: str | None
    tags: list[str]
    pantry_match_pct: int
    confidence: str
    warnings: list[str]
 # ── Image helpers ──────────────────────────────────────────────────────────────
 def _load_image_b64(path: Path) -> str:
    """Load image, apply EXIF rotation, return base64-encoded JPEG bytes."""
    from PIL import Image, ImageOps
    with open(path, "rb") as f:
        raw = f.read()
    img = Image.open(io.BytesIO(raw))
    img = ImageOps.exif_transpose(img).convert("RGB")
    buf = io.BytesIO()
    img.save(buf, format="JPEG", quality=90)
    return base64.b64encode(buf.getvalue()).decode()
 # ── Vision backend ─────────────────────────────────────────────────────────────
 def _call_via_anthropic(image_paths: list[Path], prompt: str) -> str:
    """Send image(s) + prompt to Anthropic API. Raises RuntimeError if unavailable."""
    try:
        import anthropic
    except ImportError as exc:
        raise RuntimeError("anthropic package not installed") from exc
    api_key = os.environ.get("ANTHROPIC_API_KEY")
    if not api_key:
        raise RuntimeError("ANTHROPIC_API_KEY not set")
    client = anthropic.Anthropic(api_key=api_key)
    content: list[dict] = []
    for i, path in enumerate(image_paths):
        if i > 0:
            content.append({"type": "text", "text": f"(Page {i + 1} of the same recipe:)"})
        content.append({
            "type": "image",
            "source": {
                "type": "base64",
                "media_type": "image/jpeg",
                "data": _load_image_b64(path),
            },
        })
    content.append({"type": "text", "text": prompt})
    msg = client.messages.create(
        # Haiku is cost-efficient for well-structured extraction prompts
        model="claude-haiku-4-5-20251001",
        max_tokens=2048,
        messages=[{"role": "user", "content": content}],
    )
    return msg.content[0].text.strip()
 def _call_via_local_vlm(image_paths: list[Path], prompt: str) -> str:
    """Send image(s) + prompt to local Qwen2.5-VL. Raises RuntimeError if unavailable."""
    try:
        import torch
    except ImportError as exc:
        raise RuntimeError("torch not installed") from exc
    if not torch.cuda.is_available():
        raise RuntimeError("No CUDA device -- local VLM unavailable")
    # Lazy import so the module loads fast when GPU is absent
    from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
    from PIL import Image, ImageOps
    model_name = "Qwen/Qwen2.5-VL-7B-Instruct"
    logger.info("Loading local VLM for recipe scan: %s", model_name)
    model = Qwen2VLForConditionalGeneration.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto",
        low_cpu_mem_usage=True,
    )
    processor = AutoProcessor.from_pretrained(model_name)
    model.train(False)  # inference mode
    images = []
    for path in image_paths:
        with open(path, "rb") as f:
            raw = f.read()
        img = Image.open(io.BytesIO(raw))
        img = ImageOps.exif_transpose(img).convert("RGB")
        images.append(img)
    inputs = processor(images=images, text=prompt, return_tensors="pt")
    inputs = {k: v.to("cuda", torch.float16) if isinstance(v, torch.Tensor) else v
              for k, v in inputs.items()}
    with torch.no_grad():
        output_ids = model.generate(
            **inputs,
            max_new_tokens=2048,
            do_sample=False,
            temperature=0.0,
        )
    output = processor.decode(output_ids[0], skip_special_tokens=True)
    output = output.replace(prompt, "").strip()
    # Free VRAM
    del model
    torch.cuda.empty_cache()
    return output
 def _build_ocr_extraction_prompt(ocr_text: str) -> str:
    """Build a text-LLM prompt for structuring OCR output into recipe JSON.
    Swaps the image-centric preamble of _EXTRACTION_PROMPT for an OCR-centric
    one, then appends the combined OCR text as input. The JSON schema section
    is shared verbatim to keep the two paths in sync.
    """
    schema_idx = _EXTRACTION_PROMPT.find("Return a single JSON object")
    schema_part = _EXTRACTION_PROMPT[schema_idx:] if schema_idx != -1 else _EXTRACTION_PROMPT
    return (
        "You are extracting a recipe from OCR text taken from a recipe card, "
        "cookbook page, or handwritten note.\n\n"
        "The text below was obtained via optical character recognition and may "
        "contain minor scanning artifacts or formatting irregularities.\n\n"
        f"{schema_part}\n\nOCR Text:\n{ocr_text}"
    )
 def _call_via_cf_text_vlm(alloc_url: str, image_paths: list[Path], prompt: str) -> str:
    """Call the cf-text OpenAI-compat API with images via the llama.cpp multimodal backend."""
    import httpx
    content: list[dict] = []
    for i, path in enumerate(image_paths):
        if i > 0:
            content.append({"type": "text", "text": f"(Page {i + 1} of the same recipe:)"})
        b64 = _load_image_b64(path)
        content.append({
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{b64}"},
        })
    content.append({"type": "text", "text": prompt})
    resp = httpx.post(
        f"{alloc_url.rstrip('/')}/v1/chat/completions",
        json={
            "model": "local",
            "messages": [{"role": "user", "content": content}],
            "max_tokens": 2048,
            "temperature": 0.0,
        },
        timeout=180.0,
    )
    resp.raise_for_status()
    return resp.json()["choices"][0]["message"]["content"].strip()
 def _call_vision_backend(
    image_paths: list[Path],
    prompt: str,
    progress_cb: "Callable[[str, str], None] | None" = None,
 ) -> str:
    """Dispatch to the best available vision backend.
    Priority: cf-orch (Qwen2-VL GGUF via cf-text) -> local Qwen2.5-VL -> Anthropic API.
    Raises RuntimeError with a clear message when no backend is available.
    Args:
        image_paths: Images to process.
        prompt: Extraction prompt (used by local VLM / Anthropic paths).
        progress_cb: Optional callback(status, message) for SSE progress events.
                     Called synchronously from the thread — caller bridges to async.
    """
    def _progress(status: str, message: str) -> None:
        if progress_cb:
            progress_cb(status, message)
    errors: list[str] = []
    # 1. Try cf-orch task allocation → cf-docuvision (Qwen2-VL GGUF via llama.cpp).
    #    Two-step: docuvision OCRs the image(s), then LLMRouter structures the text into JSON.
    cf_orch_url = os.environ.get("CF_ORCH_URL")
    if cf_orch_url:
        try:
            from app.services.task_inference import TaskNotRegistered, task_allocate
            from app.services.ocr.docuvision_client import DocuvisionClient
            from circuitforge_core.llm.router import LLMRouter
            try:
                _progress("allocating", "Starting vision service...")
                with task_allocate("kiwi", "recipe_scan", service_hint="cf-docuvision", ttl_s=120.0) as alloc:
                    _progress("scanning", "Extracting recipe text from photo...")
                    doc_client = DocuvisionClient(alloc.url)
                    ocr_parts: list[str] = []
                    for i, path in enumerate(image_paths):
                        result = doc_client.extract_text(path, hint="text")
                        prefix = f"(Page {i + 1} of the same recipe)\n" if len(image_paths) > 1 else ""
                        ocr_parts.append(f"{prefix}{result.text}")
                    combined_ocr = "\n\n".join(ocr_parts)
                    if not combined_ocr.strip():
                        raise ValueError("Docuvision returned no text — image may not be a recipe")
                    _progress("structuring", "Parsing recipe structure...")
                    text = LLMRouter().complete(
                        _build_ocr_extraction_prompt(combined_ocr),
                        system="You are a recipe data extractor. Return ONLY valid JSON. No markdown, no explanation, no code fences.",
                    )
                    if text:
                        return text
            except TaskNotRegistered:
                logger.debug("kiwi.recipe_scan not yet registered in cf-orch assignments")
        except Exception as exc:
            logger.debug("cf-orch vision failed for recipe scan: %s", exc)
            errors.append(f"cf-orch: {exc}")
    # 2. Try local Qwen2.5-VL
    try:
        return _call_via_local_vlm(image_paths, prompt)
    except Exception as exc:
        logger.debug("Local VLM unavailable for recipe scan: %s", exc)
        errors.append(f"local VLM: {exc}")
    # 3. Try Anthropic API (BYOK)
    try:
        return _call_via_anthropic(image_paths, prompt)
    except Exception as exc:
        logger.debug("Anthropic API failed for recipe scan: %s", exc)
        errors.append(f"Anthropic: {exc}")
    raise RuntimeError(
        "No vision backend configured for recipe scanning. "
        "Options: cf-orch (CF_ORCH_URL), local GPU, or ANTHROPIC_API_KEY (BYOK). "
        f"Errors: {'; '.join(errors)}"
    )
 # ── Parsing helpers ────────────────────────────────────────────────────────────
 def _normalize_ingredient_name(name: str) -> str:
    """Lowercase + strip whitespace. Preserves multi-word names as-is."""
    return name.lower().strip()
 def _extract_json_object(text: str) -> str | None:
    """Return the first balanced JSON object from text, or None if not found.
    Uses brace-counting rather than a greedy regex so trailing prose and
    nested objects are handled correctly.
    """
    start = text.find("{")
    if start == -1:
        return None
    depth = 0
    in_string = False
    escape_next = False
    for i, ch in enumerate(text[start:], start):
        if escape_next:
            escape_next = False
            continue
        if ch == "\\" and in_string:
            escape_next = True
            continue
        if ch == '"':
            in_string = not in_string
            continue
        if in_string:
            continue
        if ch == "{":
            depth += 1
        elif ch == "}":
            depth -= 1
            if depth == 0:
                return text[start : i + 1]
    return None
 def _parse_scanner_json(raw_text: str) -> dict:
    """Extract and return the JSON dict from VLM output.
    Handles:
    - Pure JSON
    - JSON in ```json ... ``` markdown fences
    - Qwen3-style <think>...</think> or <thinking>...</thinking> preambles
    - JSON preceded or followed by prose
    Raises ValueError on not_a_recipe or unparseable output.
    """
    text = raw_text.strip()
    # Strip thinking-token blocks emitted by reasoning models (Qwen3, DeepSeek-R1, etc.)
    text = re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL | re.IGNORECASE).strip()
    text = re.sub(r"<thinking>.*?</thinking>", "", text, flags=re.DOTALL | re.IGNORECASE).strip()
    # Strip markdown fences if present
    if "```" in text:
        # Find the content between the first ``` pair
        fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
        if fence_match:
            text = fence_match.group(1).strip()
    # Try direct parse
    try:
        data = json.loads(text)
    except json.JSONDecodeError:
        # Fall back to brace-balanced extraction from anywhere in the output
        candidate = _extract_json_object(text)
        if not candidate:
            logger.warning("Could not parse JSON from LLM output (first 400 chars): %r", text[:400])
            raise ValueError(f"Could not parse JSON from VLM output: {text[:200]!r}")
        try:
            data = json.loads(candidate)
        except json.JSONDecodeError as exc:
            logger.warning("Brace-extracted JSON still invalid: %r", candidate[:400])
            raise ValueError(f"Could not parse JSON from VLM output: {exc}") from exc
    if isinstance(data, dict) and data.get("error") == "not_a_recipe":
        raise ValueError("not_a_recipe: image does not appear to contain a recipe")
    return data
 # ── Pantry cross-reference ─────────────────────────────────────────────────────
 def _cross_reference_pantry(
    ingredients: list[ScannedIngredient],
    pantry_names: list[str],
 ) -> tuple[list[ScannedIngredient], int]:
    """Mark ingredients found in the pantry and return updated list + match percent.
    Matching is bidirectional by token:
    - "broccoli florets" matches pantry item "broccoli" (pantry token in ingredient)
    - "pumpkin seeds" matches pantry "pumpkin seeds" (exact)
    Returns (updated_ingredients, pantry_match_pct).
    """
    if not ingredients:
        return ingredients, 0
    normalized_pantry = [_normalize_ingredient_name(p) for p in pantry_names]
    updated: list[ScannedIngredient] = []
    matched = 0
    for ingr in ingredients:
        norm_ingr = _normalize_ingredient_name(ingr.name)
        in_pantry = any(
            (p_tok in norm_ingr or norm_ingr in p_tok)
            for p in normalized_pantry
            for p_tok in p.split()
            if len(p_tok) >= 4  # skip short stop-words like "of", "and", "the"
        )
        updated.append(ScannedIngredient(
            name=ingr.name,
            qty=ingr.qty,
            unit=ingr.unit,
            raw=ingr.raw,
            in_pantry=in_pantry,
        ))
        if in_pantry:
            matched += 1
    pct = round(matched / len(ingredients) * 100)
    return updated, pct
 # ── Main scanner class ─────────────────────────────────────────────────────────
 class RecipeScanner:
    """Stateless recipe scanner. One instance can be reused across requests."""
    def scan(
        self,
        image_paths: list[Path],
        pantry_names: list[str] | None = None,
        progress_cb: Callable[[str, str], None] | None = None,
    ) -> ScannedRecipeResult:
        """Extract a structured recipe from one or more photos.
        Args:
            image_paths: 1-4 image files (phone photos, scans).
            pantry_names: Flat list of product names from user's inventory.
                          Pass [] or None to skip pantry cross-reference.
        Returns:
            ScannedRecipeResult with all fields populated.
        Raises:
            ValueError: Image is not a recipe, or JSON could not be parsed.
            RuntimeError: No vision backend is configured.
        """
        if not image_paths:
            raise ValueError("At least one image is required")
        if len(image_paths) > MAX_IMAGES:
            raise ValueError(f"Maximum {MAX_IMAGES} images per scan (got {len(image_paths)})")
        # Call vision backend
        raw_text = _call_vision_backend(image_paths, _EXTRACTION_PROMPT, progress_cb=progress_cb)
        # Parse JSON from VLM output
        data = _parse_scanner_json(raw_text)
        # Build ingredient list
        raw_ingredients = data.get("ingredients") or []
        ingredients: list[ScannedIngredient] = [
            ScannedIngredient(
                name=str(item.get("name") or "").strip() or "unknown",
                qty=str(item["qty"]) if item.get("qty") is not None else None,
                unit=str(item["unit"]) if item.get("unit") is not None else None,
                raw=str(item["raw"]) if item.get("raw") is not None else None,
            )
            for item in raw_ingredients
            if isinstance(item, dict)
        ]
        # Pantry cross-reference
        ingredients, pct = _cross_reference_pantry(
            ingredients,
            pantry_names or [],
        )
        return ScannedRecipeResult(
            title=data.get("title") or None,
            subtitle=data.get("subtitle") or None,
            servings=str(data["servings"]) if data.get("servings") is not None else None,
            cook_time=str(data["cook_time"]) if data.get("cook_time") is not None else None,
            source_note=data.get("source_note") or None,
            ingredients=ingredients,
            steps=[str(s) for s in (data.get("steps") or []) if s],
            notes=data.get("notes") or None,
            tags=list(data.get("tags") or []),
            pantry_match_pct=pct,
            confidence=data.get("confidence") or "medium",
            warnings=list(data.get("warnings") or []),
        )
--- a/app/services/recipe/reranker.py
+++ b/app/services/recipe/reranker.py
@ -1,175 +0,0 @@
 """
 Reranker integration for recipe suggestions.
 Wraps circuitforge_core.reranker to score recipe candidates against a
 natural-language query built from the user's pantry, constraints, and
 preferences. Paid+ tier only; free tier returns None (caller keeps
 existing sort). All exceptions are caught and logged — the reranker
 must never break recipe suggestions.
 Environment:
    CF_RERANKER_MOCK=1  — force mock backend (tests, no model required)
 """
 from __future__ import annotations
 import logging
 from dataclasses import dataclass, field
 from app.models.schemas.recipe import RecipeRequest, RecipeSuggestion
 log = logging.getLogger(__name__)
 # Tiers that get reranker access.
 _RERANKER_TIERS: frozenset[str] = frozenset({"paid", "premium", "local"})
 # Minimum candidates worth reranking — below this the cross-encoder
 # overhead is not justified and the overlap sort is fine.
 _MIN_CANDIDATES: int = 3
@dataclass(frozen=True)
 class RerankerInput:
    """Intermediate representation passed to the reranker."""
    query: str
    candidates: list[str]
    suggestion_ids: list[int]  # parallel to candidates, for re-mapping
 # ── Query builder ─────────────────────────────────────────────────────────────
 def build_query(req: RecipeRequest) -> str:
    """Build a natural-language query string from the recipe request.
    Encodes the user's full context so the cross-encoder can score
    relevance, dietary fit, and expiry urgency in a single pass.
    Only non-empty segments are included.
    """
    parts: list[str] = []
    if req.pantry_items:
        parts.append(f"Recipe using: {', '.join(req.pantry_items)}")
    if req.exclude_ingredients:
        parts.append(f"Avoid: {', '.join(req.exclude_ingredients)}")
    if req.allergies:
        parts.append(f"Allergies: {', '.join(req.allergies)}")
    if req.constraints:
        parts.append(f"Dietary: {', '.join(req.constraints)}")
    if req.category:
        parts.append(f"Category: {req.category}")
    if req.style_id:
        parts.append(f"Style: {req.style_id}")
    if req.complexity_filter:
        parts.append(f"Prefer: {req.complexity_filter}")
    if req.hard_day_mode:
        parts.append("Prefer: easy, minimal effort")
    # Secondary pantry items carry a state label (e.g. "stale", "overripe")
    # that helps the reranker favour recipes suited to those specific states.
    if req.secondary_pantry_items:
        expiry_parts = [f"{name} ({state})" for name, state in req.secondary_pantry_items.items()]
        parts.append(f"Use soon: {', '.join(expiry_parts)}")
    elif req.expiry_first:
        parts.append("Prefer: recipes that use expiring items first")
    return ". ".join(parts) + "." if parts else "Recipe."
 # ── Candidate builder ─────────────────────────────────────────────────────────
 def build_candidate_string(suggestion: RecipeSuggestion) -> str:
    """Build a candidate string for a single recipe suggestion.
    Format: "{title}. Ingredients: {comma-joined ingredients}"
    Matched ingredients appear before missing ones.
    Directions excluded to stay within BGE's 512-token window.
    """
    ingredients = suggestion.matched_ingredients + suggestion.missing_ingredients
    if not ingredients:
        return suggestion.title
    return f"{suggestion.title}. Ingredients: {', '.join(ingredients)}"
 # ── Input assembler ───────────────────────────────────────────────────────────
 def build_reranker_input(
    req: RecipeRequest,
    suggestions: list[RecipeSuggestion],
 ) -> RerankerInput:
    """Assemble query and candidate strings for the reranker."""
    query = build_query(req)
    candidates: list[str] = []
    ids: list[int] = []
    for s in suggestions:
        candidates.append(build_candidate_string(s))
        ids.append(s.id)
    return RerankerInput(query=query, candidates=candidates, suggestion_ids=ids)
 # ── cf-core seam (isolated for monkeypatching in tests) ──────────────────────
 def _do_rerank(query: str, candidates: list[str], top_n: int = 0):
    """Thin wrapper around cf-core rerank(). Extracted so tests can patch it."""
    from circuitforge_core.reranker import rerank
    return rerank(query, candidates, top_n=top_n)
 # ── Public entry point ────────────────────────────────────────────────────────
 def rerank_suggestions(
    req: RecipeRequest,
    suggestions: list[RecipeSuggestion],
 ) -> list[RecipeSuggestion] | None:
    """Rerank suggestions using the cf-core cross-encoder.
    Returns a reordered list with rerank_score populated, or None when:
    - Tier is not paid+ (free tier keeps overlap sort)
    - Fewer than _MIN_CANDIDATES suggestions (not worth the overhead)
    - Any exception is raised (graceful fallback to existing sort)
    The caller should treat None as "keep existing sort order".
    Original suggestions are never mutated.
    """
    if req.tier not in _RERANKER_TIERS:
        return None
    if len(suggestions) < _MIN_CANDIDATES:
        return None
    try:
        rinput = build_reranker_input(req, suggestions)
        results = _do_rerank(rinput.query, rinput.candidates, top_n=0)
        # Map reranked results back to RecipeSuggestion objects using the
        # candidate string as key (build_candidate_string is deterministic).
        candidate_map: dict[str, RecipeSuggestion] = {
            build_candidate_string(s): s for s in suggestions
        }
        reranked: list[RecipeSuggestion] = []
        for rr in results:
            suggestion = candidate_map.get(rr.candidate)
            if suggestion is not None:
                reranked.append(suggestion.model_copy(
                    update={"rerank_score": round(float(rr.score), 4)}
                ))
        if len(reranked) < len(suggestions):
            log.warning(
                "Reranker lost %d/%d suggestions during mapping, falling back",
                len(suggestions) - len(reranked),
                len(suggestions),
            )
            return None
        return reranked
    except Exception:
        log.exception("Reranker failed, falling back to overlap sort")
        return None
--- a/app/services/recipe/sensory.py
+++ b/app/services/recipe/sensory.py
@ -1,133 +0,0 @@
 """
 Sensory filter dataclass and helpers.
 SensoryExclude bridges user preferences (from user_settings) to the
 store browse methods and recipe engine suggest flow.
 Recipes with sensory_tags = '{}' (untagged) pass ALL filters --
 graceful degradation when tag_sensory_profiles.py has not run.
 """
 from __future__ import annotations
 import json
 from dataclasses import dataclass, field
 _SMELL_LEVELS: tuple[str, ...] = ("mild", "aromatic", "pungent", "fermented")
 _NOISE_LEVELS: tuple[str, ...] = ("quiet", "moderate", "loud", "very_loud")
@dataclass(frozen=True)
 class SensoryExclude:
    """Derived filter criteria from user sensory preferences.
    textures:    texture tags to exclude (empty tuple = no texture filter)
    smell_above: if set, exclude recipes whose smell level is strictly above
                 this level in the smell spectrum
    noise_above: if set, exclude recipes whose noise level is strictly above
                 this level in the noise spectrum
    """
    textures: tuple[str, ...] = field(default_factory=tuple)
    smell_above: str | None = None
    noise_above: str | None = None
    @classmethod
    def empty(cls) -> "SensoryExclude":
        """No filtering -- pass-through for users with no preferences set."""
        return cls()
    def is_empty(self) -> bool:
        """True when no filtering will be applied."""
        return not self.textures and self.smell_above is None and self.noise_above is None
 def build_sensory_exclude(prefs_json: str | None) -> SensoryExclude:
    """Parse user_settings value for 'sensory_preferences' into a SensoryExclude.
    Expected JSON shape:
      {
        "avoid_textures": ["mushy", "slimy"],
        "max_smell":      "pungent",
        "max_noise":      "loud"
      }
    Returns SensoryExclude.empty() on missing, null, or malformed input.
    """
    if not prefs_json:
        return SensoryExclude.empty()
    try:
        prefs = json.loads(prefs_json)
    except (json.JSONDecodeError, TypeError):
        return SensoryExclude.empty()
    if not isinstance(prefs, dict):
        return SensoryExclude.empty()
    avoid_textures = tuple(
        t for t in (prefs.get("avoid_textures") or [])
        if isinstance(t, str)
    )
    max_smell: str | None = prefs.get("max_smell") or None
    max_noise: str | None = prefs.get("max_noise") or None
    if max_smell and max_smell not in _SMELL_LEVELS:
        max_smell = None
    if max_noise and max_noise not in _NOISE_LEVELS:
        max_noise = None
    return SensoryExclude(
        textures=avoid_textures,
        smell_above=max_smell,
        noise_above=max_noise,
    )
 def passes_sensory_filter(
    sensory_tags_raw: str | dict | None,
    exclude: SensoryExclude,
 ) -> bool:
    """Return True if the recipe passes the sensory exclude criteria.
    sensory_tags_raw: the sensory_tags column value (JSON string or already-parsed dict).
    exclude:          derived filter criteria.
    Untagged recipes (empty dict or '{}') always pass -- graceful degradation.
    Empty SensoryExclude always passes -- no preferences set.
    """
    if exclude.is_empty():
        return True
    if sensory_tags_raw is None:
        return True
    if isinstance(sensory_tags_raw, str):
        try:
            tags: dict = json.loads(sensory_tags_raw)
        except (json.JSONDecodeError, TypeError):
            return True
    else:
        tags = sensory_tags_raw
    if not tags:
        return True
    if exclude.textures:
        recipe_textures: list[str] = tags.get("textures") or []
        for t in recipe_textures:
            if t in exclude.textures:
                return False
    if exclude.smell_above is not None:
        recipe_smell: str | None = tags.get("smell")
        if recipe_smell and recipe_smell in _SMELL_LEVELS:
            max_idx = _SMELL_LEVELS.index(exclude.smell_above)
            recipe_idx = _SMELL_LEVELS.index(recipe_smell)
            if recipe_idx > max_idx:
                return False
    if exclude.noise_above is not None:
        recipe_noise: str | None = tags.get("noise")
        if recipe_noise and recipe_noise in _NOISE_LEVELS:
            max_idx = _NOISE_LEVELS.index(exclude.noise_above)
            recipe_idx = _NOISE_LEVELS.index(recipe_noise)
            if recipe_idx > max_idx:
                return False
    return True
--- a/app/services/recipe/style_classifier.py
+++ b/app/services/recipe/style_classifier.py
@ -1,139 +0,0 @@
 # app/services/recipe/style_classifier.py
 # BSL 1.1 — LLM feature
 """LLM style-tag classifier for saved recipes.
 Reads recipe title, ingredients, and directions and suggests 3–5 style tags
 from the curated vocabulary shared with SaveRecipeModal.vue.
 Cloud (CF_ORCH_URL set): allocates a cf-text service via cf-orch (2 GB VRAM).
 Local: falls back to LLMRouter (ollama / vllm / openai-compat).
 """
 from __future__ import annotations
 import json
 import logging
 import os
 import re
 from contextlib import nullcontext
 from typing import Any
 logger = logging.getLogger(__name__)
 _SERVICE_TYPE = "cf-text"
 _TTL_S = 60.0
 _CALLER = "kiwi-style-classify"
 # Canonical vocabulary — must stay in sync with SUGGESTED_TAGS in SaveRecipeModal.vue.
 STYLE_TAG_VOCAB: frozenset[str] = frozenset({
    "comforting", "light", "spicy", "umami", "sweet", "savory", "rich",
    "crispy", "creamy", "hearty", "quick", "hands-off", "meal-prep-friendly",
    "fancy", "one-pot",
 })
 _SYSTEM_PROMPT = """\
 You are a culinary tagger. Given a recipe, suggest 3 to 5 style tags that best \
 describe its character. You MUST only use tags from this list:
 comforting, light, spicy, umami, sweet, savory, rich, crispy, creamy, hearty, \
 quick, hands-off, meal-prep-friendly, fancy, one-pot
 Return ONLY a JSON array of strings, no explanation. Example:
 ["comforting", "hearty", "one-pot"]
 """
 def _build_router():
    """Return (router, context_manager) for style classify tasks.
    Tries cf-orch cf-text allocation first; falls back to LLMRouter.
    Returns (None, nullcontext) if no backend is available.
    """
    cf_orch_url = os.environ.get("CF_ORCH_URL")
    if cf_orch_url:
        try:
            from app.services.meal_plan.llm_router import _OrchTextRouter  # reuse adapter
            from circuitforge_orch.client import CFOrchClient
            client = CFOrchClient(cf_orch_url)
            ctx = client.allocate(service=_SERVICE_TYPE, ttl_s=_TTL_S, caller=_CALLER)
            alloc = ctx.__enter__()
            if alloc is not None:
                return _OrchTextRouter(alloc.url), ctx
        except Exception as exc:
            logger.debug("cf-orch allocation failed for style classify, falling back: %s", exc)
    try:
        from circuitforge_core.llm.router import LLMRouter
        return LLMRouter(), nullcontext(None)
    except FileNotFoundError:
        logger.debug("LLMRouter: no llm.yaml — style classifier LLM disabled")
        return None, nullcontext(None)
    except Exception as exc:
        logger.debug("LLMRouter init failed: %s", exc)
        return None, nullcontext(None)
 def _parse_tags(raw: str) -> list[str]:
    """Extract valid vocab tags from raw LLM output.
    Tries JSON parse first; falls back to extracting any vocab word present
    in the response text so minor formatting deviations still work.
    """
    # Strip markdown fences
    raw = re.sub(r"```[a-z]*", "", raw).strip()
    try:
        parsed = json.loads(raw)
        if isinstance(parsed, list):
            return [t for t in parsed if isinstance(t, str) and t in STYLE_TAG_VOCAB][:5]
    except (json.JSONDecodeError, ValueError):
        pass
    # Fallback: scan for vocab words
    found = [t for t in STYLE_TAG_VOCAB if re.search(rf"\b{re.escape(t)}\b", raw, re.IGNORECASE)]
    return sorted(found, key=lambda t: raw.lower().index(t.lower()))[:5]
 def classify_style(recipe: dict[str, Any]) -> list[str]:
    """Return 3–5 suggested style tags for *recipe*.
    *recipe* is a Store row dict with at least ``title``, ``ingredient_names``
    (list[str]), and ``directions`` (list[str] or str).
    Returns an empty list if no LLM backend is available.
    """
    router, ctx = _build_router()
    if router is None:
        return []
    title = recipe.get("title") or "Unknown"
    ingredients = recipe.get("ingredient_names") or []
    if isinstance(ingredients, str):
        try:
            ingredients = json.loads(ingredients)
        except Exception:
            ingredients = [ingredients]
    directions = recipe.get("directions") or []
    if isinstance(directions, str):
        try:
            directions = json.loads(directions)
        except Exception:
            directions = [directions]
    user_prompt = (
        f"Recipe: {title}\n"
        f"Ingredients: {', '.join(str(i) for i in ingredients[:20])}\n"
        f"Steps: {' '.join(str(d) for d in directions[:8])[:600]}"
    )
    try:
        with ctx:
            raw = router.complete(
                system=_SYSTEM_PROMPT,
                user=user_prompt,
                max_tokens=64,
                temperature=0.3,
            )
        return _parse_tags(raw)
    except Exception as exc:
        logger.warning("Style classifier LLM call failed: %s", exc)
        return []
--- a/app/services/recipe/tag_inferrer.py
+++ b/app/services/recipe/tag_inferrer.py
@ -22,8 +22,6 @@ queries find recipes the food.com corpus tags alone would miss.
 """
 from __future__ import annotations
 import re
 # ---------------------------------------------------------------------------
 # Text-signal tables
@ -70,15 +68,6 @@ _CUISINE_SIGNALS: list[tuple[str, list[str]]] = [
    ("cuisine:Cajun",          ["cajun", "creole", "gumbo", "jambalaya", "andouille", "etouffee"]),
    ("cuisine:African",        ["injera", "berbere", "jollof", "suya", "egusi", "fufu", "tagine"]),
    ("cuisine:Caribbean",      ["jerk", "scotch bonnet", "callaloo", "ackee"]),
    # BBQ detection: match on title terms and key ingredients; these rarely appear
    # in food.com's own keyword/category taxonomy so we derive the tag from content.
    ("cuisine:BBQ",            ["brisket", "pulled pork", "spare ribs", "baby back ribs",
                                 "baby back", "burnt ends", "pit smoked", "smoke ring",
                                 "low and slow", "hickory", "mesquite", "liquid smoke",
                                 "bbq brisket", "smoked brisket", "barbecue brisket",
                                 "carolina bbq", "texas bbq", "kansas city bbq",
                                 "memphis bbq", "smoked ribs", "smoked pulled pork",
                                 "dry rub ribs", "wet rub ribs", "beer can chicken smoked"]),
 ]
 _DIETARY_SIGNALS: list[tuple[str, list[str]]] = [
@ -123,50 +112,6 @@ _TIME_SIGNALS: list[tuple[str, list[str]]] = [
    ("time:Slow Cook",    ["slow cooker", "crockpot", "< 4 hours", "braise"]),
 ]
 # ---------------------------------------------------------------------------
 # Meal type signals — matched against TITLE ONLY (not ingredient text).
 # Ingredient names frequently contain words like "cake flour" or "sandwich
 # bread" which would produce false meal-type tags if matched against the full
 # title+ingredient string.
 # ---------------------------------------------------------------------------
 _MEAL_SIGNALS: list[tuple[str, list[str]]] = [
    ("meal:Breakfast", [
        "breakfast", "pancake", "waffle", "french toast", "scrambled egg",
        "frittata", "hash brown", "hash browns", "breakfast burrito",
        "breakfast sandwich", "breakfast casserole", "overnight oat",
        "granola", "oatmeal", "muffin", "morning glory", "eggs benedict",
        "shakshuka", "crepe", "scone",
    ]),
    ("meal:Dessert", [
        "dessert", "cake", "cookie", "brownie", "cheesecake", "pudding",
        "fudge", "ice cream", "sorbet", "cupcake", "mousse", "candy",
        "truffle", "gelato", "donut", "doughnut", "cobbler", "crisp",
        "crumble", "tiramisu", "eclair", "sundae", "milkshake", "parfait",
        "biscotti", "macaron", "panna cotta", "baklava", "churro", "tart",
        "torte", "strudel", "compote", "semifreddo",
    ]),
    ("meal:Snack", [
        "snack", "appetizer", "dip", "chips", "popcorn", "trail mix",
        "energy ball", "deviled egg", "cheese ball", "nachos",
        "pretzel bites", "protein ball", "granola bar",
    ]),
    ("meal:Beverage", [
        "smoothie", "cocktail", "mocktail", "lemonade", "limeade",
        "margarita", "sangria", "punch", "milkshake", "milk shake",
        "juice", "spritzer", "iced tea", "hot chocolate", "chai latte",
        "mulled wine", "eggnog", "slushie", "frappe", "horchata",
        "agua fresca", "shrub", "switchel",
    ]),
    ("meal:Lunch", [
        "lunch", "sandwich", "panini", "grilled cheese", "wrap",
        "lunchbox", "lunch box",
    ]),
    ("meal:Bread", [
        "bread", "sourdough", "focaccia", "flatbread", "dinner roll",
        "loaf", "baguette", "ciabatta", "brioche", "challah", "pita",
    ]),
 ]
 _MAIN_INGREDIENT_SIGNALS: list[tuple[str, list[str]]] = [
    ("main:Chicken",    ["chicken", "poultry", "turkey"]),
    ("main:Beef",       ["beef", "ground beef", "steak", "brisket", "pot roast"]),
@ -242,29 +187,6 @@ def _match_signals(text: str, table: list[tuple[str, list[str]]]) -> list[str]:
    return [tag for tag, pats in table if any(p in text for p in pats)]
 def _match_title_signals(title: str, table: list[tuple[str, list[str]]]) -> list[str]:
    """Match signals against title text only, using word-boundary + optional plural.
    Pattern: `\\bWORD(?:s|es)?\\b`
    This handles:
    - Plurals: "cookie" matches "cookies", "sandwich" matches "sandwiches"
    - Substring rejection: "cake" does NOT match "pancake" (no word boundary
      before 'c' in pan|cake), "tart" does NOT match "tartare" (after "tart"
      the 'a' is a word char, not a boundary)
    - Avoids false positives from ingredient text ("cake flour", "sandwich bread")
      by only matching the recipe title, not the full title+ingredient string.
    """
    t = title.lower()
    return [
        tag for tag, pats in table
        if any(
            re.search(r"\b" + re.escape(p.strip()) + r"(?:s|es)?\b", t)
            for p in pats
        )
    ]
 def infer_tags(
    title: str,
    ingredient_names: list[str],
@ -327,9 +249,6 @@ def infer_tags(
    tags.update(_match_signals(text, _FLAVOR_SIGNALS))
    tags.update(_match_signals(text, _MAIN_INGREDIENT_SIGNALS))
    # Meal type: title-only to avoid "cake flour" → meal:Dessert false positives
    tags.update(_match_title_signals(title, _MEAL_SIGNALS))
    # 3. Time signals from corpus keywords + text
    corpus_text = " ".join(kw.lower() for kw in corpus_keywords)
    tags.update(_match_signals(corpus_text, _TIME_SIGNALS))
--- a/app/services/recipe/time_effort.py
+++ b/app/services/recipe/time_effort.py
@ -1,602 +0,0 @@
 """
 Runtime parser for active/passive time split, prep effort, and equipment detection.
 Operates over a list of direction strings plus an optional ingredient list.
 No I/O — pure Python functions. Sub-millisecond for up to 20 recipes.
 Time estimation strategy (in priority order):
 1. Explicit time mention in step text  ("simmer for 20 minutes")
 2. Passive keyword + per-technique default  ("bake until golden" → 30 min)
 3. Prep action + ingredient quantity scaling  ("dice 2 lbs potatoes" → ~5 min)
 4. Fallback active default  (assembly/misc steps → 2 min each)
 Quantity scaling uses n^0.75 (sub-linear, matching human batch-work curves).
 Pass `ingredients` + `ingredient_names` to enable cross-referenced scaling.
 Without them, prep actions use base times only (no scaling).
 """
 from __future__ import annotations
 import math
 import re
 from dataclasses import dataclass, field
 from typing import Final
 # ── Passive step keywords ─────────────────────────────────────────────────
 _PASSIVE_PATTERNS: Final[list[str]] = [
    "simmer", "bake", "roast", "broil", "refrigerate", "marinate",
    "chill", "cool", "freeze", "rest", "stand", "set", "soak",
    "steep", "proof", "rise", "let", "wait", "overnight", "braise",
    r"slow\s+cook", r"pressure\s+cook",
 ]
 _PASSIVE_RE: re.Pattern[str] = re.compile(
    r"\b(?:" + "|".join(_PASSIVE_PATTERNS) + r")\b",
    re.IGNORECASE,
 )
 # Per-technique passive defaults (minutes) — used when no explicit time found.
 # Calibrated to conservative midpoints from USDA FoodKeeper + culinary practice.
 _PASSIVE_DEFAULTS: Final[list[tuple[re.Pattern[str], int]]] = [
    # Multi-word first (longer match wins)
    (re.compile(r"\bslow\s+cook\b", re.IGNORECASE), 300),      # 5 hr crockpot default
    (re.compile(r"\bpressure\s+cook\b", re.IGNORECASE), 15),
    (re.compile(r"\bovernight\b", re.IGNORECASE), 480),         # 8 hr
    # Single-word
    (re.compile(r"\bbraise\b", re.IGNORECASE), 90),
    (re.compile(r"\bmarinate\b", re.IGNORECASE), 60),
    (re.compile(r"\brefrigerate\b", re.IGNORECASE), 120),
    (re.compile(r"\bproof\b|\brise\b", re.IGNORECASE), 60),
    (re.compile(r"\bsoak\b", re.IGNORECASE), 30),
    (re.compile(r"\bfreeze\b", re.IGNORECASE), 120),
    (re.compile(r"\bchill\b", re.IGNORECASE), 60),
    (re.compile(r"\broast\b", re.IGNORECASE), 40),
    (re.compile(r"\bbake\b", re.IGNORECASE), 30),
    (re.compile(r"\bbroil\b", re.IGNORECASE), 8),
    (re.compile(r"\bsimmer\b", re.IGNORECASE), 20),
    (re.compile(r"\bset\b", re.IGNORECASE), 30),                # gelatin / custard set
    (re.compile(r"\bsteep\b", re.IGNORECASE), 5),
    (re.compile(r"\brest\b|\bstand\b", re.IGNORECASE), 10),
    (re.compile(r"\bcool\b", re.IGNORECASE), 15),
    (re.compile(r"\bwait\b|\blet\b", re.IGNORECASE), 5),
 ]
 # ── Explicit time extraction ──────────────────────────────────────────────
 _TIME_RE: re.Pattern[str] = re.compile(
    r"(\d+)\s*(?:[-\u2013]|-to-)\s*(\d+)\s*(hour|hr|minute|min|second|sec)s?"
    r"|"
    r"(\d+)\s*(hour|hr|minute|min|second|sec)s?",
    re.IGNORECASE,
 )
 _MAX_MINUTES_PER_STEP: Final[int] = 480  # 8-hour sanity cap
 # ── Prep action detection ─────────────────────────────────────────────────
 # Base times (minutes) per prep action, calibrated to ~3 items / 0.5 lb reference.
 # These are starting points — flagged for calibration against real recipe timing data.
 _PREP_ACTION_BASES: Final[dict[str, float]] = {
    # Peeling / stripping
    "peel": 1.5,
    "pare": 1.5,
    "hull": 1.5,
    "pit": 2.0,     # cherries, avocados
    "core": 1.0,
    "stem": 1.0,
    "trim": 1.0,
    # Cutting
    "chop": 2.0,
    "cut": 1.5,
    "dice": 2.5,    # more precise than chop
    "mince": 2.0,
    "slice": 1.5,
    "julienne": 4.0,
    "cube": 2.0,
    "quarter": 1.0,
    "halve": 0.5,
    "shred": 2.0,
    # Grating / zesting
    "grate": 3.0,
    "zest": 2.0,
    # Crushing
    "crush": 0.5,
    "smash": 0.5,
    "crack": 0.5,
    # Mixing / assembly (lower base — less physical effort)
    "knead": 8.0,       # bread dough: consistent regardless of quantity
    "whisk": 1.5,
    "beat": 2.0,
    "cream": 3.0,       # butter + sugar until fluffy
    "fold": 1.5,
    "stir": 0.5,
    "combine": 0.5,
    "mix": 1.0,
    "season": 0.5,
 }
 # Compiled regex — longer patterns first to avoid partial matches.
 _PREP_RE: re.Pattern[str] = re.compile(
    r"\b(?:" + "|".join(
        re.escape(k) for k in sorted(_PREP_ACTION_BASES, key=len, reverse=True)
    ) + r")\b",
    re.IGNORECASE,
 )
 # Default active time per step when no explicit time and no prep action detected.
 _ACTIVE_STEP_DEFAULT_MIN: Final[float] = 2.0
 # ── Prep-needing ingredient classification ────────────────────────────────
 #
 # Only ingredients in this set get quantity-scaled prep time.
 # Liquids, spices, canned goods, and dry staples are excluded — they require
 # no physical prep beyond measuring.
 _PREP_NEEDING: Final[frozenset[str]] = frozenset({
    # Alliums
    "onion", "shallot", "leek", "scallion", "green onion", "chive", "garlic",
    # Root / stem vegetables
    "ginger", "carrot", "celery", "potato", "sweet potato", "yam",
    "beet", "turnip", "parsnip", "radish", "fennel", "celeriac",
    # Squash / gourd family
    "zucchini", "squash", "pumpkin", "cucumber",
    # Peppers
    "pepper", "bell pepper", "jalapeño", "jalapeno", "chili", "chile",
    # Brassicas
    "broccoli", "cauliflower", "cabbage", "kale", "chard", "spinach",
    "brussels sprout",
    # Other vegetables
    "tomato", "eggplant", "aubergine", "corn", "artichoke", "asparagus",
    "green bean", "snow pea", "snap pea", "mushroom", "lettuce",
    # Fruits
    "apple", "pear", "peach", "nectarine", "plum", "apricot",
    "mango", "papaya", "pineapple", "melon", "watermelon", "cantaloupe",
    "avocado", "banana",
    "strawberry", "raspberry", "blackberry", "blueberry", "cherry",
    "citrus", "lemon", "lime", "orange", "grapefruit",
    # Protein (trimming / portioning)
    "chicken", "turkey", "duck",
    "beef", "pork", "lamb", "veal",
    "fish", "salmon", "tuna", "cod", "tilapia", "halibut", "shrimp",
    "scallop", "crab", "lobster",
    # Dairy requiring active prep
    "cheese",
    # Nuts / seeds (chopping)
    "almond", "walnut", "pecan", "cashew", "peanut", "hazelnut",
    "pistachio", "macadamia", "nut",
    # Fresh herbs (chopping / tearing)
    "basil", "parsley", "cilantro", "thyme", "rosemary", "sage",
    "dill", "mint", "tarragon",
    # Other
    "bread",
 })
 def _is_prep_needing(name: str) -> bool:
    """True if the normalized ingredient name contains any prep-needing keyword."""
    nl = name.lower()
    return any(kw in nl for kw in _PREP_NEEDING)
 # ── Quantity extraction ───────────────────────────────────────────────────
 _FRAC_RE: re.Pattern[str] = re.compile(r"(\d+)\s*/\s*(\d+)")
 # Weight units → converted to pounds internally
 _WEIGHT_RE: re.Pattern[str] = re.compile(
    r"(\d+(?:\.\d+)?|\d+\s*/\s*\d+)\s*"
    r"(pound|lb|ounce|oz|gram|g(?![a-z])|kilogram|kg)\s*s?\b",
    re.IGNORECASE,
 )
 # Volume (cups only — the common recipe unit for quantity scaling)
 _VOLUME_CUP_RE: re.Pattern[str] = re.compile(
    r"(\d+(?:\.\d+)?|\d+\s*/\s*\d+)\s*cups?\b",
    re.IGNORECASE,
 )
 # Count — bare integer or decimal followed by optional size/unit word
 _COUNT_RE: re.Pattern[str] = re.compile(
    r"(?<!\d)(\d+(?:\.\d+)?)\s*"
    r"(?:large|medium|small|whole|clove|cloves|head|heads|ear|ears|"
    r"stalk|stalks|sprig|sprigs|bunch|bunches|fillet|fillets|"
    r"breast|breasts|piece|pieces|slice|slices)?\s*\b",
    re.IGNORECASE,
 )
 # Reference quantities: the "1× base" for each unit type.
 # Calibrated so that a typical single-ingredient amount = 1× prep time.
 _QTY_REFS: Final[dict[str, float]] = {
    "lb":    0.5,    # 0.5 lb is the base → 1 lb = 1.4×, 2 lb = 2.0×
    "cup":   1.0,    # 1 cup = base
    "count": 3.0,    # 3 items = base → 1 = 0.46×, 6 = 1.6×
 }
 _SCALE_POWER:  Final[float] = 0.75   # sub-linear; revisit with empirical data
 _MAX_SCALE:    Final[float] = 4.0    # cap at 4× regardless of quantity
 _MIN_SCALE:    Final[float] = 0.33   # floor at 1/3× for tiny amounts
 def _parse_fraction(s: str) -> float:
    m = _FRAC_RE.search(s)
    if m:
        try:
            return float(m.group(1)) / float(m.group(2))
        except (ValueError, ZeroDivisionError):
            return 1.0
    try:
        return float(s.replace(" ", ""))
    except ValueError:
        return 1.0
 def _extract_qty(text: str) -> tuple[float, str] | None:
    """Return (quantity_in_canonical_units, unit_type) or None.
    Unit types: "lb" (weight in pounds), "cup", "count".
    All weights are normalised to pounds.
    """
    # Weight (most specific — check first)
    m = _WEIGHT_RE.search(text)
    if m:
        qty = _parse_fraction(m.group(1))
        u = m.group(2).lower().rstrip("s")
        if u in ("pound", "lb"):
            return (qty, "lb")
        if u in ("ounce", "oz"):
            return (qty / 16.0, "lb")
        if u in ("gram", "g"):
            return (qty / 453.6, "lb")
        if u in ("kilogram", "kg"):
            return (qty * 2.205, "lb")
    # Volume (cups)
    m = _VOLUME_CUP_RE.search(text)
    if m:
        return (_parse_fraction(m.group(1)), "cup")
    # Count — only accept values in a sane range to avoid false positives
    m = _COUNT_RE.search(text)
    if m:
        qty = float(m.group(1))
        if 0 < qty <= 24:
            return (qty, "count")
    return None
 def _extract_inline_qty_for(text: str, ing_name: str) -> tuple[float, str] | None:
    """Extract the quantity specifically associated with `ing_name` in a direction step.
    Looks for a number immediately before the ingredient name (plus optional size/unit
    words). Falls back to None if the pattern does not match.
    Example: "Dice 2 large onions and 3 carrots" → for "onion" returns (2.0, "count").
    """
    pattern = re.compile(
        r"(\d+(?:\.\d+)?|\d+\s*/\s*\d+)\s*"
        r"(?:large|medium|small|whole|"
        r"(?:pound|lb|ounce|oz|gram|g|kilogram|kg|cup|clove|cloves|"
        r"head|heads|fillet|fillets|breast|breasts|piece|pieces)s?)??\s*"
        + re.escape(ing_name) + r"(?:es|s)?\b",
        re.IGNORECASE,
    )
    m = pattern.search(text)
    if m:
        # Re-extract with _extract_qty on the full matched span to get unit too
        span = text[m.start(): m.end()]
        result = _extract_qty(span)
        if result:
            return result
        # Fallback: bare count
        try:
            return (_parse_fraction(m.group(1)), "count")
        except Exception:
            pass
    return None
 def _quantity_scale(qty: float, unit: str) -> float:
    """Apply n^0.75 scaling relative to unit reference, clamped to [MIN, MAX]."""
    ref = _QTY_REFS.get(unit, 1.0)
    if ref <= 0 or qty <= 0:
        return 1.0
    raw = (qty / ref) ** _SCALE_POWER
    return max(_MIN_SCALE, min(_MAX_SCALE, raw))
 # ── Equipment detection ───────────────────────────────────────────────────
 _EQUIPMENT_RULES: Final[list[tuple[re.Pattern[str], str]]] = [
    (re.compile(r"\b(?:chop|dice|mince|slice|julienne)\b", re.IGNORECASE), "Knife"),
    (re.compile(r"\b(?:skillet|sauté|saute|fry|sear|pan-fry|pan fry)\b", re.IGNORECASE), "Skillet"),
    (re.compile(r"\b(?:wooden spoon|spatula|stir|fold)\b", re.IGNORECASE), "Spoon"),
    (re.compile(r"\b(?:pot|boil|simmer|blanch|stock)\b", re.IGNORECASE), "Pot"),
    (re.compile(r"\b(?:oven|bake|roast|preheat|broil)\b", re.IGNORECASE), "Oven"),
    (re.compile(r"\b(?:blender|blend|purée|puree|food processor)\b", re.IGNORECASE), "Blender"),
    (re.compile(r"\b(?:stand mixer|hand mixer|whip|beat)\b", re.IGNORECASE), "Mixer"),
    (re.compile(r"\b(?:grill|barbecue|char|griddle)\b", re.IGNORECASE), "Grill"),
    (re.compile(r"\b(?:slow cooker|crockpot|low and slow)\b", re.IGNORECASE), "Slow cooker"),
    (re.compile(r"\b(?:pressure cooker|instant pot)\b", re.IGNORECASE), "Pressure cooker"),
    (re.compile(r"\b(?:drain|strain|colander|rinse pasta)\b", re.IGNORECASE), "Colander"),
 ]
 def _detect_equipment(all_text: str, has_passive: bool) -> list[str]:
    seen: set[str] = set()
    result: list[str] = []
    for pattern, label in _EQUIPMENT_RULES:
        if label not in seen and pattern.search(all_text):
            seen.add(label)
            result.append(label)
    if has_passive and "Timer" not in seen:
        result.append("Timer")
    return result
 # ── Ingredient–step cross-reference ──────────────────────────────────────
 def _ingredient_mentioned(text: str, name: str) -> bool:
    """True if `name` appears in `text` as a whole word.
    Handles both regular plurals (onion → onions) and -es plurals
    (potato → potatoes, tomato → tomatoes).
    """
    pattern = re.compile(r"\b" + re.escape(name.lower()) + r"(?:es|s)?\b", re.IGNORECASE)
    return bool(pattern.search(text))
 def _build_step_ingredient_qtys(
    ingredients: list[str],
    ingredient_names: list[str],
    directions: list[str],
 ) -> list[dict[str, tuple[float, str]]]:
    """Return, for each direction step, {ing_name: (qty_for_this_step, unit)}.
    Strategy:
    - Filter ingredient pairs to prep-needing items only.
    - Parse total quantities from the raw ingredient strings.
    - For each step, try to find an inline quantity tied to that ingredient name.
    - If no inline quantity, distribute the total evenly across all steps that
      mention the ingredient (handles "3 onions" split across 2 steps).
    """
    # Build total qty map for prep-needing ingredients
    total_qtys: dict[str, tuple[float, str]] = {}
    for raw, name in zip(ingredients, ingredient_names):
        base = name.lower().strip()
        if not _is_prep_needing(base):
            continue
        result = _extract_qty(raw)
        if result is not None:
            total_qtys[base] = result
    if not total_qtys:
        return [{} for _ in directions]
    # Count how many steps mention each ingredient
    step_counts: dict[str, int] = {n: 0 for n in total_qtys}
    for step in directions:
        for name in total_qtys:
            if _ingredient_mentioned(step, name):
                step_counts[name] += 1
    # Build per-step qty maps
    per_step: list[dict[str, tuple[float, str]]] = []
    for step in directions:
        step_map: dict[str, tuple[float, str]] = {}
        for name, (total, unit) in total_qtys.items():
            if not _ingredient_mentioned(step, name):
                continue
            # Try ingredient-specific inline quantity first
            inline = _extract_inline_qty_for(step, name)
            if inline is not None:
                step_map[name] = inline
            else:
                # Distribute total across steps that reference this ingredient
                n = max(step_counts.get(name, 1), 1)
                step_map[name] = (total / n, unit)
        per_step.append(step_map)
    return per_step
 # ── Dataclasses ───────────────────────────────────────────────────────────
@dataclass(frozen=True)
 class StepAnalysis:
    """Analysis result for a single direction step."""
    is_passive: bool
    detected_minutes: int | None   # explicit or estimated time (None = no signal)
    prep_min: int | None = None    # estimated physical prep time from action detection
@dataclass(frozen=True)
 class TimeEffortProfile:
    """Aggregated time and effort profile for a full recipe."""
    active_min: int
    passive_min: int
    total_min: int
    step_analyses: list[StepAnalysis] = field(default_factory=list)
    equipment: list[str] = field(default_factory=list)
    effort_label: str = "moderate"   # "quick" | "moderate" | "involved"
 # ── Core parsing helpers ──────────────────────────────────────────────────
 def _extract_minutes(text: str) -> int | None:
    """Return explicit minutes from text, or None."""
    m = _TIME_RE.search(text)
    if m is None:
        return None
    if m.group(1) is not None:
        low, high = int(m.group(1)), int(m.group(2))
        unit = m.group(3).lower()
        raw: float = (low + high) / 2
    else:
        low = int(m.group(4))
        unit = m.group(5).lower()
        raw = float(low)
    if unit in ("hour", "hr"):
        minutes: float = raw * 60
    elif unit in ("second", "sec"):
        minutes = max(1.0, math.ceil(raw / 60))
    else:
        minutes = raw
    return min(int(minutes), _MAX_MINUTES_PER_STEP)
 def _classify_passive(text: str) -> bool:
    return _PASSIVE_RE.search(text) is not None
 def _passive_default(text: str) -> int | None:
    """Return estimated passive minutes from per-keyword defaults."""
    for pattern, minutes in _PASSIVE_DEFAULTS:
        if pattern.search(text):
            return minutes
    return None
 def _prep_estimate(
    text: str,
    step_ing_qtys: dict[str, tuple[float, str]],
 ) -> int:
    """Estimate active prep time from the first detected prep action + ingredient qtys.
    If no prep-needing ingredient is identified in the step, uses the action's
    base time at 1× (no scaling).
    """
    m = _PREP_RE.search(text)
    if m is None:
        return 0
    action = m.group(0).lower()
    base = _PREP_ACTION_BASES.get(action, _ACTIVE_STEP_DEFAULT_MIN)
    # Find which prep-needing ingredients this step mentions
    matches: list[tuple[float, str]] = [
        qty_unit
        for name, qty_unit in step_ing_qtys.items()
        if _ingredient_mentioned(text, name)
    ]
    if not matches:
        return round(base)   # no ingredient context — use base unscaled
    total = sum(base * _quantity_scale(qty, unit) for qty, unit in matches)
    return round(total)
 def _effort_label(total_min: int, step_count: int) -> str:
    """Effort label based on total estimated time; falls back to step count."""
    if total_min > 0:
        if total_min <= 20:
            return "quick"
        if total_min <= 45:
            return "moderate"
        return "involved"
    # No time signals at all — fall back to step count heuristic
    if step_count <= 3:
        return "quick"
    if step_count <= 7:
        return "moderate"
    return "involved"
 # ── Public API ────────────────────────────────────────────────────────────
 def parse_time_effort(
    directions: list[str],
    ingredients: list[str] | None = None,
    ingredient_names: list[str] | None = None,
 ) -> TimeEffortProfile:
    """Parse direction strings into a TimeEffortProfile.
    Args:
        directions:       List of step strings from the recipe corpus.
        ingredients:      Raw ingredient strings ("2 large onions", "1.5 lbs potatoes").
                          Parallel to ingredient_names.
        ingredient_names: Normalised ingredient names ("onion", "potato").
                          Required alongside ingredients to enable quantity scaling.
    Returns a zero-value profile with empty lists when directions is empty.
    Never raises — all failures produce sensible defaults.
    """
    if not directions:
        return TimeEffortProfile(
            active_min=0, passive_min=0, total_min=0,
            step_analyses=[], equipment=[], effort_label="quick",
        )
    # Build per-step ingredient quantity maps (empty dicts if no ingredient data)
    use_ingredients = (
        bool(ingredients)
        and bool(ingredient_names)
        and len(ingredients) == len(ingredient_names)
    )
    step_ing_qtys: list[dict[str, tuple[float, str]]]
    if use_ingredients:
        step_ing_qtys = _build_step_ingredient_qtys(
            list(ingredients),       # type: ignore[arg-type]
            list(ingredient_names),  # type: ignore[arg-type]
            directions,
        )
    else:
        step_ing_qtys = [{} for _ in directions]
    step_analyses: list[StepAnalysis] = []
    active_min = 0
    passive_min = 0
    has_any_passive = False
    for i, step in enumerate(directions):
        is_passive = _classify_passive(step)
        detected = _extract_minutes(step)
        prep_estimate: int | None = None
        if is_passive:
            has_any_passive = True
            if detected is not None:
                passive_min += detected
            else:
                # Fall back to per-technique default
                default = _passive_default(step)
                if default is not None:
                    passive_min += default
                    detected = default  # surface in UI as the hint time
        else:
            if detected is not None:
                active_min += detected
            # Estimate prep time from action detection + quantity scaling
            prep_est = _prep_estimate(step, step_ing_qtys[i])
            if prep_est > 0:
                prep_estimate = prep_est
                active_min += prep_est
            elif detected is None:
                # General active step with no time signal — apply a small default
                active_min += round(_ACTIVE_STEP_DEFAULT_MIN)
        step_analyses.append(StepAnalysis(
            is_passive=is_passive,
            detected_minutes=detected,
            prep_min=prep_estimate,
        ))
    combined_text = " ".join(directions)
    equipment = _detect_equipment(combined_text, has_any_passive)
    total = active_min + passive_min
    return TimeEffortProfile(
        active_min=active_min,
        passive_min=passive_min,
        total_min=total,
        step_analyses=step_analyses,
        equipment=equipment,
        effort_label=_effort_label(total, len(directions)),
    )
--- a/app/services/task_inference.py
+++ b/app/services/task_inference.py
@ -1,124 +0,0 @@
 # app/services/task_inference.py
 # BSL 1.1 — LLM feature
 """Task-based service allocation via the cf-orch coordinator.
 Calls POST /api/inference/task instead of a hardcoded service type.
 The coordinator resolves model_id and service_type from assignments.yaml.
 Fallback contract (for callers):
  - 404 → TaskNotRegistered  (fall back to direct client.allocate())
  - other error → RuntimeError
  - CF_ORCH_URL unset → RuntimeError (guard with os.environ.get first)
 """
 from __future__ import annotations
 import logging
 import os
 from collections.abc import Generator
 from contextlib import contextmanager
 from dataclasses import dataclass
 import httpx
 logger = logging.getLogger(__name__)
 class TaskNotRegistered(Exception):
    """Coordinator returned 404 for a product/task pair.
    Means the task is not yet in assignments.yaml. Callers should fall
    back to direct service allocation (client.allocate()).
    """
@dataclass(frozen=True)
 class Allocation:
    url: str
    allocation_id: str
    service: str
 def _orch_url() -> str:
    return os.environ.get("CF_ORCH_URL", "").rstrip("/")
@contextmanager
 def task_allocate(
    product: str,
    task: str,
    *,
    service_hint: str,
    ttl_s: float = 120.0,
 ) -> Generator[Allocation, None, None]:
    """Context manager: allocate a service via task-based routing.
    Calls POST /api/inference/task, yields Allocation, releases on exit.
    Supports both `with task_allocate(...) as alloc:` and manual
    `ctx = task_allocate(...); alloc = ctx.__enter__()` patterns.
    **Sync-only**: uses the synchronous httpx API. Do not call from an
    ``async def`` handler without wrapping in ``asyncio.to_thread``. Current
    call sites (``llm_router.py``, ``vl_model.py``) are synchronous.
    Args:
        product: CF product name (e.g. "kiwi")
        task: Task identifier (e.g. "meal_plan", "ocr")
        service_hint: Service type for the release DELETE call. The
            coordinator response does not include service_type, so the
            caller provides it. When the coordinator is updated to return
            service in the response (cf-orch#63), this becomes unused.
        ttl_s: Allocation TTL in seconds.
    Raises:
        TaskNotRegistered: Coordinator returned 404.
        RuntimeError: Coordinator unreachable, returned non-404 error, or
            returned a malformed (non-JSON / missing fields) response.
        RuntimeError: CF_ORCH_URL is not set.
    """
    base = _orch_url()
    if not base:
        raise RuntimeError("CF_ORCH_URL is not set")
    try:
        resp = httpx.post(
            f"{base}/api/inference/task",
            json={"product": product, "task": task, "payload": {}},
            timeout=30.0,
        )
    except httpx.RequestError as exc:
        raise RuntimeError(f"cf-orch unreachable: {exc}") from exc
    if resp.status_code == 404:
        raise TaskNotRegistered(
            f"No assignment for product={product!r} task={task!r} — "
            "ensure cf-orch#61/62 are deployed and coordinator reloaded"
        )
    if not resp.is_success:
        raise RuntimeError(
            f"cf-orch /api/inference/task failed: "
            f"HTTP {resp.status_code} — {resp.text[:200]}"
        )
    try:
        data = resp.json()
        alloc = Allocation(
            url=data["url"],
            allocation_id=data["allocation_id"],
            service=data.get("service") or service_hint,
        )
    except (KeyError, ValueError) as exc:
        raise RuntimeError(
            f"cf-orch /api/inference/task returned malformed response: {exc} — "
            f"body: {resp.text[:200]}"
        ) from exc
    try:
        yield alloc
    finally:
        try:
            httpx.delete(
                f"{base}/api/services/{alloc.service}/allocations/{alloc.allocation_id}",
                timeout=10.0,
            )
        except Exception as exc:
            logger.debug("cf-orch task allocation release failed (non-fatal): %s", exc)
--- a/app/tasks/runner.py
+++ b/app/tasks/runner.py
@ -22,7 +22,7 @@ from app.services.expiration_predictor import ExpirationPredictor
 log = logging.getLogger(__name__)
-LLM_TASK_TYPES: frozenset[str] = frozenset({"expiry_llm_fallback", "recipe_llm"})
+LLM_TASK_TYPES: frozenset[str] = frozenset({"expiry_llm_fallback"})
 VRAM_BUDGETS: dict[str, float] = {
    # ExpirationPredictor uses a small LLM (16 tokens out, single pass).
@ -88,8 +88,6 @@ def run_task(
    try:
        if task_type == "expiry_llm_fallback":
            _run_expiry_llm_fallback(db_path, job_id, params)
        elif task_type == "recipe_llm":
            _run_recipe_llm(db_path, job_id, params)
        else:
            raise ValueError(f"Unknown kiwi task type: {task_type!r}")
        _update_task_status(db_path, task_id, "completed")
@ -145,41 +143,3 @@ def _run_expiry_llm_fallback(
        expiry,
        days,
    )
 def _run_recipe_llm(db_path: Path, _job_id_int: int, params: str | None) -> None:
    """Run LLM recipe generation for an async recipe job.
    params JSON keys:
        job_id  (required) — recipe_jobs.job_id string (e.g. "rec_a1b2c3...")
    Creates its own Store — follows same pattern as _suggest_in_thread.
    MUST call store.fail_recipe_job() before re-raising so recipe_jobs.status
    doesn't stay 'running' while background_tasks shows 'failed'.
    """
    from app.db.store import Store
    from app.models.schemas.recipe import RecipeRequest
    from app.services.recipe.recipe_engine import RecipeEngine
    p = json.loads(params or "{}")
    recipe_job_id: str = p.get("job_id", "")
    if not recipe_job_id:
        raise ValueError("recipe_llm: 'job_id' is required in params")
    store = Store(db_path)
    try:
        store.update_recipe_job_running(recipe_job_id)
        row = store._fetch_one(
            "SELECT request FROM recipe_jobs WHERE job_id=?", (recipe_job_id,)
        )
        if row is None:
            raise ValueError(f"recipe_llm: recipe_jobs row not found: {recipe_job_id!r}")
        req = RecipeRequest.model_validate_json(row["request"])
        result = RecipeEngine(store).suggest(req)
        store.complete_recipe_job(recipe_job_id, result.model_dump_json())
        log.info("recipe_llm: job %s completed (%d suggestion(s))", recipe_job_id, len(result.suggestions))
    except Exception as exc:
        store.fail_recipe_job(recipe_job_id, str(exc))
        raise
    finally:
        store.close()
--- a/app/tasks/scheduler.py
+++ b/app/tasks/scheduler.py
@ -1,10 +1,5 @@
 # app/tasks/scheduler.py
-"""Kiwi LLM task scheduler — thin shim over circuitforge_core.tasks.scheduler.
+"""Kiwi LLM task scheduler — thin shim over circuitforge_core.tasks.scheduler."""
 Local mode (CLOUD_MODE unset): LocalScheduler — simple FIFO, no coordinator.
 Cloud mode (CLOUD_MODE=true): OrchestratedScheduler — coordinator-aware, fans
 out concurrent jobs across all registered cf-orch GPU nodes.
 """
 from __future__ import annotations
 from pathlib import Path
@ -12,68 +7,15 @@ from pathlib import Path
 from circuitforge_core.tasks.scheduler import (
    TaskScheduler,
    get_scheduler as _base_get_scheduler,
-    reset_scheduler as _reset_local,  # re-export for tests
+    reset_scheduler,  # re-export for tests
 )
 from app.cloud_session import CLOUD_MODE
 from app.core.config import settings
 from app.tasks.runner import LLM_TASK_TYPES, VRAM_BUDGETS, run_task
 def _orch_available() -> bool:
    """Return True if circuitforge_orch is installed in this environment."""
    try:
        import circuitforge_orch  # noqa: F401
        return True
    except ImportError:
        return False
 def _use_orch() -> bool:
    """Return True if the OrchestratedScheduler should be used.
    Priority order:
      1. USE_ORCH_SCHEDULER env var — explicit override always wins.
      2. CLOUD_MODE=true — use orch in managed cloud deployments.
      3. circuitforge_orch installed — paid+ local users who have cf-orch
         set up get coordinator-aware scheduling (local GPU first) automatically.
    """
    override = settings.USE_ORCH_SCHEDULER
    if override is not None:
        return override
    return CLOUD_MODE or _orch_available()
 def get_scheduler(db_path: Path) -> TaskScheduler:
-    """Return the process-level TaskScheduler singleton for Kiwi.
+    """Return the process-level TaskScheduler singleton for Kiwi."""
    OrchestratedScheduler: coordinator-aware, fans out concurrent jobs across
    all registered cf-orch GPU nodes. Active when USE_ORCH_SCHEDULER=true,
    CLOUD_MODE=true, or circuitforge_orch is installed locally (paid+ users
    running their own cf-orch stack get this automatically; local GPU is
    preferred by the coordinator's allocation queue).
    LocalScheduler: serial FIFO, no coordinator dependency. Free-tier local
    installs without circuitforge_orch installed use this automatically.
    """
    if _use_orch():
        try:
            from circuitforge_orch.scheduler import get_orch_scheduler
        except ImportError:
            import logging
            logging.getLogger(__name__).warning(
                "circuitforge_orch not installed — falling back to LocalScheduler"
            )
        else:
            return get_orch_scheduler(
                db_path=db_path,
                run_task_fn=run_task,
                task_types=LLM_TASK_TYPES,
                vram_budgets=VRAM_BUDGETS,
                coordinator_url=settings.COORDINATOR_URL,
                service_name="kiwi",
            )
    return _base_get_scheduler(
        db_path=db_path,
        run_task_fn=run_task,
@ -82,15 +24,3 @@ def get_scheduler(db_path: Path) -> TaskScheduler:
        coordinator_url=settings.COORDINATOR_URL,
        service_name="kiwi",
    )
 def reset_scheduler() -> None:
    """Shut down and clear the active scheduler singleton. TEST TEARDOWN ONLY."""
    if _use_orch():
        try:
            from circuitforge_orch.scheduler import reset_orch_scheduler
            reset_orch_scheduler()
            return
        except ImportError:
            pass
    _reset_local()
--- a/app/tiers.py
+++ b/app/tiers.py
@ -15,7 +15,6 @@ KIWI_BYOK_UNLOCKABLE: frozenset[str] = frozenset({
    "recipe_suggestions",
    "expiry_llm_matching",
    "receipt_ocr",
    "recipe_scan",
    "style_classifier",
    "meal_plan_llm",
    "meal_plan_llm_timing",
@ -45,7 +44,6 @@ KIWI_FEATURES: dict[str, str] = {
    # Paid tier
    "receipt_ocr":           "paid",   # BYOK-unlockable
    "visual_label_capture":  "paid",   # Camera capture for unenriched barcodes (kiwi#79)
    "recipe_suggestions":    "paid",   # BYOK-unlockable
    "expiry_llm_matching":   "paid",   # BYOK-unlockable
    "meal_planning":         "free",
@ -59,9 +57,6 @@ KIWI_FEATURES: dict[str, str] = {
    "community_publish":     "paid",    # Publish plans/outcomes to community feed
    "community_fork_adapt":  "paid",    # Fork with LLM pantry adaptation (BYOK-unlockable)
    # Paid tier (continued)
    "recipe_scan":           "paid",   # BYOK-unlockable: photo -> structured recipe
    # Premium tier
    "multi_household":       "premium",
    "background_monitoring": "premium",
--- a/compose.cloud.yml
+++ b/compose.cloud.yml
@ -21,12 +21,6 @@ services:
      CLOUD_AUTH_BYPASS_IPS: ${CLOUD_AUTH_BYPASS_IPS:-}
      # cf-orch: route LLM calls through the coordinator for managed GPU inference
      CF_ORCH_URL: http://host.docker.internal:7700
      # Product identifier for coordinator analytics — per-product VRAM/request breakdown
      CF_APP_NAME: kiwi
      # cf-orch streaming proxy — coordinator URL + product key for /proxy/authorize
      # COORDINATOR_KIWI_KEY must be set in .env (never commit the value)
      COORDINATOR_URL: http://10.1.10.71:7700
      COORDINATOR_KIWI_KEY: ${COORDINATOR_KIWI_KEY:-}
      # Community PostgreSQL — shared across CF products; unset = community features unavailable (fail soft)
      COMMUNITY_DB_URL: ${COMMUNITY_DB_URL:-}
      COMMUNITY_PSEUDONYM_SALT: ${COMMUNITY_PSEUDONYM_SALT:-}
--- a/compose.override.yml
+++ b/compose.override.yml
@ -8,6 +8,23 @@ services:
      # Docker can follow the symlink inside the container.
      - /Library/Assets/kiwi:/Library/Assets/kiwi:rw
-  # cf-orch agent sidecar removed 2026-04-24: Sif is now a dedicated compute node
+  # cf-orch agent sidecar: registers kiwi as a GPU node with the coordinator.
-  # with its own systemd cf-orch-agent service (port 7703, advertise-host 10.1.10.158).
+  # The API scheduler uses COORDINATOR_URL to lease VRAM cooperatively; this
-  # This sidecar was only valid when Kiwi ran on Sif directly.
+  # agent makes kiwi's VRAM usage visible on the orchestrator dashboard.
  cf-orch-agent:
    image: kiwi-api  # reuse local api image — cf-core already installed there
    network_mode: host
    env_file: .env
    environment:
      # Override coordinator URL here or via .env
      COORDINATOR_URL: ${COORDINATOR_URL:-http://10.1.10.71:7700}
    command: >
      conda run -n kiwi cf-orch agent
      --coordinator ${COORDINATOR_URL:-http://10.1.10.71:7700}
      --node-id kiwi
      --host 0.0.0.0
      --port 7702
      --advertise-host ${CF_ORCH_ADVERTISE_HOST:-10.1.10.71}
    restart: unless-stopped
    depends_on:
      - api
--- a/config/llm.yaml.example
+++ b/config/llm.yaml.example
@ -1,74 +0,0 @@
 # Kiwi — LLM backend configuration
 #
 # Copy to ~/.config/circuitforge/llm.yaml (shared across all CF products)
 # or to config/llm.yaml (Kiwi-local, takes precedence).
 #
 # Kiwi uses LLMs for:
 #   - Expiry prediction fallback (unknown products not in the lookup table)
 #   - Meal planning suggestions
 #
 # Local inference (Ollama / vLLM) is the default path — no API key required.
 # BYOK (bring your own key): set api_key_env to point at your API key env var.
 # cf-orch trunk: set CF_ORCH_URL env var to allocate cf-text on-demand via
 # the coordinator instead of hitting a static URL.
 backends:
  ollama:
    type: openai_compat
    enabled: true
    base_url: http://localhost:11434/v1
    model: llama3.2:3b
    api_key: ollama
    supports_images: false
  vllm:
    type: openai_compat
    enabled: false
    base_url: http://localhost:8000/v1
    model: __auto__             # resolved from /v1/models at runtime
    api_key: ''
    supports_images: false
  # ── cf-orch trunk services ──────────────────────────────────────────────────
  # These allocate via cf-orch rather than connecting to a static URL.
  # cf-orch starts the service on-demand and returns its live URL.
  # Set CF_ORCH_URL env var or fill in url below; leave enabled: false if
  # cf-orch is not deployed in your environment.
  cf_text:
    type: openai_compat
    enabled: false
    base_url: http://localhost:8008/v1   # fallback when cf-orch is not available
    model: __auto__
    api_key: any
    supports_images: false
    cf_orch:
      service: cf-text
      # model_candidates: leave empty to use the service's default_model,
      # or specify a catalog alias (e.g. "qwen2.5-3b").
      model_candidates: []
      ttl_s: 3600
  # ── Cloud / BYOK ───────────────────────────────────────────────────────────
  anthropic:
    type: anthropic
    enabled: false
    model: claude-haiku-4-5-20251001
    api_key_env: ANTHROPIC_API_KEY
    supports_images: false
  openai:
    type: openai_compat
    enabled: false
    base_url: https://api.openai.com/v1
    model: gpt-4o-mini
    api_key_env: OPENAI_API_KEY
    supports_images: false
 fallback_order:
  - cf_text
  - ollama
  - vllm
  - anthropic
  - openai
--- a/docker/web/nginx.cloud.conf
+++ b/docker/web/nginx.cloud.conf
@ -8,7 +8,7 @@ server {
    # Proxy API requests to the FastAPI container via Docker bridge network.
    location /api/ {
        proxy_pass http://api:8512;
-        proxy_set_header Host $http_host;
+        proxy_set_header Host $host;
        # Prefer X-Real-IP set by Caddy (real client address); fall back to $remote_addr
        # when accessed directly on LAN without Caddy in the path.
        proxy_set_header X-Real-IP $http_x_real_ip;
@ -18,28 +18,6 @@ server {
        proxy_set_header X-CF-Session $http_x_cf_session;
        # Allow image uploads (barcode/receipt photos from phone cameras).
        client_max_body_size 20m;
        # LLM inference (recipe suggestions, expiry fallback) can take 60-120s.
        # Default proxy_read_timeout is 60s which causes 504s on full recipe generation.
        proxy_read_timeout 180s;
        proxy_send_timeout 180s;
    }
    # Direct-port LAN access (localhost:8515): when VITE_API_BASE='/kiwi', the frontend
    # builds API calls as /kiwi/api/v1/... — proxy these to the API container.
    # Through Caddy the /kiwi prefix is stripped before reaching nginx, so this block
    # is only active for direct-port access without Caddy in the path.
    # Longer prefix (/kiwi/api/ = 10 chars) beats ^~/kiwi/ (6 chars) per nginx rules.
    location /kiwi/api/ {
        rewrite ^/kiwi(/api/.*)$ $1 break;
        proxy_pass http://api:8512;
        proxy_set_header Host $http_host;
        proxy_set_header X-Real-IP $http_x_real_ip;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $http_x_forwarded_proto;
        proxy_set_header X-CF-Session $http_x_cf_session;
        client_max_body_size 20m;
        proxy_read_timeout 180s;
        proxy_send_timeout 180s;
    }
    # When accessed directly (localhost:8515) instead of via Caddy (/kiwi path-strip),
--- a/frontend/index.html
+++ b/frontend/index.html
@ -2,13 +2,8 @@
 <html lang="en">
  <head>
    <meta charset="UTF-8" />
-    <link rel="icon" type="image/png" sizes="192x192" href="/icons/icon-192.png" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
    <link rel="apple-touch-icon" href="/icons/icon-192.png" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0, viewport-fit=cover" />
    <meta name="theme-color" content="#e8a820" />
    <meta name="apple-mobile-web-app-capable" content="yes" />
    <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
    <meta name="apple-mobile-web-app-title" content="Kiwi" />
    <title>Kiwi — Pantry Tracker</title>
    <link rel="preconnect" href="https://fonts.googleapis.com" />
    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
--- a/frontend/package.json
+++ b/frontend/package.json
@ -20,7 +20,6 @@
    "@vue/tsconfig": "^0.8.1",
    "typescript": "~5.9.3",
    "vite": "^7.1.7",
    "vite-plugin-pwa": "^1.2.0",
    "vue-tsc": "^3.1.0"
  }
 }
--- a/frontend/public/icons/icon-192.png
+++ b/frontend/public/icons/icon-192.png
--- a/frontend/public/icons/icon-512.png
+++ b/frontend/public/icons/icon-512.png
--- a/frontend/public/icons/maskable-192.png
+++ b/frontend/public/icons/maskable-192.png
--- a/frontend/public/icons/maskable-512.png
+++ b/frontend/public/icons/maskable-512.png
--- a/frontend/src/App.vue
+++ b/frontend/src/App.vue
@ -88,22 +88,22 @@
      <main class="app-main">
        <div class="container">
-          <div v-if="mountedTabs.has('inventory')" v-show="currentTab === 'inventory'" class="tab-content fade-in">
+          <div v-show="currentTab === 'inventory'" class="tab-content fade-in">
            <InventoryList />
          </div>
-          <div v-if="mountedTabs.has('receipts')" v-show="currentTab === 'receipts'" class="tab-content fade-in">
+          <div v-show="currentTab === 'receipts'" class="tab-content fade-in">
            <ReceiptsView />
          </div>
          <div v-show="currentTab === 'recipes'" class="tab-content fade-in">
            <RecipesView />
          </div>
-          <div v-if="mountedTabs.has('settings')" v-show="currentTab === 'settings'" class="tab-content fade-in">
+          <div v-show="currentTab === 'settings'" class="tab-content fade-in">
            <SettingsView />
          </div>
-          <div v-if="mountedTabs.has('mealplan')" v-show="currentTab === 'mealplan'" class="tab-content">
+          <div v-show="currentTab === 'mealplan'" class="tab-content">
            <MealPlanView />
          </div>
-          <div v-if="mountedTabs.has('shopping')" v-show="currentTab === 'shopping'" class="tab-content fade-in">
+          <div v-show="currentTab === 'shopping'" class="tab-content fade-in">
            <ShoppingView />
          </div>
        </div>
@ -204,7 +204,7 @@
 </template>
 <script setup lang="ts">
-import { ref, reactive, onMounted } from 'vue'
+import { ref, onMounted } from 'vue'
 import InventoryList from './components/InventoryList.vue'
 import ReceiptsView from './components/ReceiptsView.vue'
 import RecipesView from './components/RecipesView.vue'
@ -220,10 +220,6 @@ type Tab = 'inventory' | 'receipts' | 'recipes' | 'settings' | 'mealplan' | 'sho
 const currentTab = ref<Tab>('recipes')
 const sidebarCollapsed = ref(false)
 // Lazy-mount: tabs mount on first visit and stay mounted (KeepAlive-like behaviour).
 // Only 'recipes' is in the initial set so non-active tabs don't mount simultaneously
 // on page load — eliminates concurrent onMounted calls across all tab components.
 const mountedTabs = reactive(new Set<Tab>(['recipes']))
 const inventoryStore = useInventoryStore()
 const { kiwiVisible, kiwiDirection } = useEasterEggs()
@ -243,7 +239,6 @@ function onWordmarkClick() {
 }
 async function switchTab(tab: Tab) {
  mountedTabs.add(tab)
  currentTab.value = tab
  if (tab === 'recipes' && inventoryStore.items.length === 0) {
    await inventoryStore.fetchItems()
--- a/frontend/src/components/InventoryList.vue
+++ b/frontend/src/components/InventoryList.vue
@ -138,103 +138,6 @@
        </div>
      </div>
      <!-- Label Capture Panel (paid tier — appears after gap detection) -->
      <div v-if="capturePhase !== null" class="label-capture-panel">
        <!-- Offer phase -->
        <div v-if="capturePhase === 'offer'" class="capture-offer">
          <p class="capture-offer-text">We couldn't find this product. Photograph the nutrition label to add it.</p>
          <div class="capture-offer-actions">
            <button class="btn btn-primary" type="button" @click="triggerCaptureLabelInput">
              Capture label
            </button>
            <button class="btn btn-ghost" type="button" @click="dismissCapture">
              Skip
            </button>
          </div>
          <input
            ref="captureFileInput"
            type="file"
            accept="image/*"
            capture="environment"
            style="display: none"
            @change="handleLabelPhotoSelect"
          />
        </div>
        <!-- Uploading / processing phase -->
        <div v-else-if="capturePhase === 'uploading'" class="capture-processing">
          <div class="loading-inline">
            <div class="spinner spinner-sm"></div>
            <span>Reading the label…</span>
          </div>
        </div>
        <!-- Review phase -->
        <div v-else-if="capturePhase === 'reviewing' && captureExtraction" class="capture-review">
          <p class="capture-review-note">
            Check the details below.
            <span v-if="captureExtraction.needs_review" class="capture-review-low-conf">
              Fields highlighted in amber weren't fully legible — please verify them.
            </span>
          </p>
          <div class="form-row">
            <div class="form-group">
              <label class="form-label">Product name</label>
              <input v-model="captureReview.product_name" type="text" class="form-input" placeholder="Product name" />
            </div>
            <div class="form-group">
              <label class="form-label">Brand</label>
              <input v-model="captureReview.brand" type="text" class="form-input" placeholder="Brand (optional)" />
            </div>
          </div>
          <p class="form-section-label">Nutrition per serving</p>
          <div class="capture-nutrition-grid">
            <div
              v-for="field in captureNutritionFields"
              :key="field.key"
              class="form-group"
            >
              <label
                :class="['form-label', { 'capture-field-amber': captureExtraction.needs_review && captureExtraction[field.src as keyof typeof captureExtraction] == null }]"
              >{{ field.label }}</label>
              <input
                v-model="captureReview[field.key as keyof typeof captureReview]"
                type="number"
                min="0"
                step="0.1"
                class="form-input"
                :placeholder="field.unit"
              />
            </div>
          </div>
          <div class="form-group" style="margin-top: var(--spacing-sm)">
            <label class="form-label">Ingredients (comma-separated)</label>
            <input v-model="captureReview.ingredients" type="text" class="form-input" placeholder="flour, water, salt…" />
          </div>
          <div class="form-group">
            <label class="form-label">Allergens (comma-separated)</label>
            <input v-model="captureReview.allergens" type="text" class="form-input" placeholder="wheat, milk…" />
          </div>
          <div class="capture-review-actions">
            <button class="btn btn-primary" type="button" :disabled="captureLoading" @click="confirmCapture">
              <span v-if="captureLoading"><div class="spinner spinner-sm"></div></span>
              <span v-else>Looks good — save</span>
            </button>
            <button class="btn btn-ghost" type="button" @click="capturePhase = 'offer'">
              Retake photo
            </button>
            <button class="btn btn-ghost" type="button" @click="dismissCapture">
              Discard
            </button>
          </div>
        </div>
      </div>
      <!-- Camera Scan Panel -->
      <div v-if="scanMode === 'camera'" class="scan-panel">
        <div class="upload-area" @click="triggerBarcodeInput">
@ -719,7 +622,7 @@ import { storeToRefs } from 'pinia'
 import { useInventoryStore } from '../stores/inventory'
 import { useSettingsStore } from '../stores/settings'
 import { inventoryAPI } from '../services/api'
-import type { InventoryItem, LabelCaptureResult } from '../services/api'
+import type { InventoryItem } from '../services/api'
 import { formatQuantity } from '../utils/units'
 import EditItemModal from './EditItemModal.vue'
 import ConfirmDialog from './ConfirmDialog.vue'
@ -781,16 +684,6 @@ function daysLabel(dateStr: string): string {
 const scanMode = ref<'gun' | 'camera' | 'manual'>('gun')
 // Options for button groups
 // Label capture nutrition field descriptors used in the review form
 const captureNutritionFields = [
  { key: 'calories',      src: 'calories',      label: 'Calories',    unit: 'kcal' },
  { key: 'fat_g',         src: 'fat_g',         label: 'Total fat',   unit: 'g' },
  { key: 'saturated_fat_g', src: 'saturated_fat_g', label: 'Saturated fat', unit: 'g' },
  { key: 'carbs_g',       src: 'carbs_g',       label: 'Carbs',       unit: 'g' },
  { key: 'protein_g',     src: 'protein_g',     label: 'Protein',     unit: 'g' },
  { key: 'sodium_mg',     src: 'sodium_mg',     label: 'Sodium',      unit: 'mg' },
 ]
 const locations = [
  { value: 'fridge', label: 'Fridge', icon: '🧊' },
  { value: 'freezer', label: 'Freezer', icon: '❄️' },
@ -887,29 +780,6 @@ const barcodeQuantity = ref(1)
 const barcodeLoading = ref(false)
 const barcodeResults = ref<Array<{ type: string; message: string }>>([])
 // Label Capture Flow (kiwi#79)
 type CapturePhase = 'offer' | 'uploading' | 'reviewing' | null
 const capturePhase = ref<CapturePhase>(null)
 const captureBarcode = ref('')
 const captureLocation = ref('pantry')
 const captureQuantity = ref(1)
 const captureLoading = ref(false)
 const captureFileInput = ref<HTMLInputElement | null>(null)
 const captureExtraction = ref<LabelCaptureResult | null>(null)
 // Editable review form — populated from extraction, user may correct fields
 const captureReview = ref({
  product_name: '',
  brand: '',
  calories: '' as string,
  fat_g: '' as string,
  saturated_fat_g: '' as string,
  carbs_g: '' as string,
  protein_g: '' as string,
  sodium_mg: '' as string,
  ingredients: '',
  allergens: '',
 })
 // Manual Form
 const manualForm = ref({
  name: '',
@ -1065,15 +935,6 @@ async function handleScannerGunInput() {
        message: `Added: ${productName}${productBrand} to ${scannerLocation.value}`,
      })
      await refreshItems()
    } else if (item?.needs_visual_capture) {
      captureBarcode.value = barcode
      captureLocation.value = scannerLocation.value
      captureQuantity.value = scannerQuantity.value
      capturePhase.value = 'offer'
      scannerResults.value.push({
        type: 'info',
        message: item.message,
      })
    } else if (item?.needs_manual_entry) {
      // Barcode not found in any database — guide user to manual entry
      scannerResults.value.push({
@ -1146,88 +1007,6 @@ async function handleBarcodeImageSelect(e: Event) {
  }
 }
 // Label Capture Functions
 function triggerCaptureLabelInput() {
  captureFileInput.value?.click()
 }
 function dismissCapture() {
  capturePhase.value = null
  captureBarcode.value = ''
  captureExtraction.value = null
 }
 async function handleLabelPhotoSelect(e: Event) {
  const target = e.target as HTMLInputElement
  const file = target.files?.[0]
  if (!file) return
  captureLoading.value = true
  capturePhase.value = 'uploading'
  try {
    const result = await inventoryAPI.captureLabelPhoto(file, captureBarcode.value)
    captureExtraction.value = result
    // Pre-populate the review form with extracted values
    captureReview.value = {
      product_name: result.product_name || '',
      brand: result.brand || '',
      calories: result.calories != null ? String(result.calories) : '',
      fat_g: result.fat_g != null ? String(result.fat_g) : '',
      saturated_fat_g: result.saturated_fat_g != null ? String(result.saturated_fat_g) : '',
      carbs_g: result.carbs_g != null ? String(result.carbs_g) : '',
      protein_g: result.protein_g != null ? String(result.protein_g) : '',
      sodium_mg: result.sodium_mg != null ? String(result.sodium_mg) : '',
      ingredients: (result.ingredient_names || []).join(', '),
      allergens: (result.allergens || []).join(', '),
    }
    capturePhase.value = 'reviewing'
  } catch {
    showToast('Could not read the label. Please try again or add manually.', 'error')
    capturePhase.value = 'offer'
  } finally {
    captureLoading.value = false
    if (target) target.value = ''
  }
 }
 async function confirmCapture() {
  if (!captureBarcode.value) return
  captureLoading.value = true
  try {
    const toNum = (s: string) => s ? parseFloat(s) || null : null
    const toList = (s: string) => s.split(',').map(x => x.trim()).filter(Boolean)
    await inventoryAPI.confirmLabelCapture({
      barcode: captureBarcode.value,
      product_name: captureReview.value.product_name || null,
      brand: captureReview.value.brand || null,
      calories: toNum(captureReview.value.calories),
      fat_g: toNum(captureReview.value.fat_g),
      saturated_fat_g: toNum(captureReview.value.saturated_fat_g),
      carbs_g: toNum(captureReview.value.carbs_g),
      protein_g: toNum(captureReview.value.protein_g),
      sodium_mg: toNum(captureReview.value.sodium_mg),
      ingredient_names: toList(captureReview.value.ingredients),
      allergens: toList(captureReview.value.allergens),
      confidence: captureExtraction.value?.confidence ?? 0,
      location: captureLocation.value,
      quantity: captureQuantity.value,
      auto_add: true,
    })
    const name = captureReview.value.product_name || 'item'
    showToast(`${name} saved and added to ${captureLocation.value}`, 'success')
    await refreshItems()
    dismissCapture()
  } catch {
    showToast('Could not save. Please try again.', 'error')
  } finally {
    captureLoading.value = false
  }
 }
 // Manual Add Functions
 async function addManualItem() {
  const { name, brand, quantity, unit, location, expirationDate } = manualForm.value
@ -1835,79 +1614,6 @@ function getItemClass(item: InventoryItem): string {
  border: 1px solid var(--color-warning-border, #fcd34d);
 }
 /* ============================================
   LABEL CAPTURE FLOW (kiwi#79)
   ============================================ */
 .label-capture-panel {
  margin: var(--spacing-md) 0;
  padding: var(--spacing-md);
  background: var(--color-surface);
  border: 1px solid var(--color-border);
  border-radius: var(--radius-lg);
 }
 .capture-offer-text {
  font-size: var(--font-size-sm);
  color: var(--color-text-secondary);
  margin: 0 0 var(--spacing-md);
 }
 .capture-offer-actions {
  display: flex;
  gap: var(--spacing-sm);
  flex-wrap: wrap;
 }
 .capture-processing {
  display: flex;
  justify-content: center;
  padding: var(--spacing-md) 0;
 }
 .capture-review-note {
  font-size: var(--font-size-sm);
  color: var(--color-text-secondary);
  margin: 0 0 var(--spacing-md);
 }
 .capture-review-low-conf {
  color: var(--color-amber, #d97706);
  font-size: var(--font-size-xs);
  display: block;
  margin-top: var(--spacing-xs);
 }
 .form-section-label {
  font-size: var(--font-size-xs);
  font-weight: 600;
  color: var(--color-text-tertiary);
  text-transform: uppercase;
  letter-spacing: 0.05em;
  margin: var(--spacing-md) 0 var(--spacing-sm);
 }
 .capture-nutrition-grid {
  display: grid;
  grid-template-columns: repeat(auto-fill, minmax(120px, 1fr));
  gap: var(--spacing-sm);
 }
 /* Amber highlight for unread/low-confidence label fields */
 .capture-field-amber {
  color: var(--color-amber, #d97706);
 }
 .capture-field-amber + input {
  border-color: var(--color-amber, #d97706);
 }
 .capture-review-actions {
  display: flex;
  gap: var(--spacing-sm);
  flex-wrap: wrap;
  margin-top: var(--spacing-md);
 }
 /* ============================================
   EXPORT CARD
   ============================================ */
--- a/frontend/src/components/PublishOutcomeModal.vue
+++ b/frontend/src/components/PublishOutcomeModal.vue
@ -106,39 +106,6 @@
          <span class="form-hint">How you appear on posts -- not your real name or email.</span>
        </div>
        <!-- Similarity check results -->
        <div
          v-if="similarPosts.length > 0"
          class="similar-panel"
          role="region"
          aria-label="Similar stories found"
        >
          <p class="similar-heading text-sm">
            <strong>Similar stories already exist.</strong>
            You can publish as-is, mark yours as a variation, or cancel.
          </p>
          <ul class="similar-list" aria-label="Existing similar posts">
            <li
              v-for="hit in similarPosts"
              :key="hit.slug"
              class="similar-item"
            >
              <span class="similar-tier-badge" :class="`tier-${hit.similarity_tier}`">
                {{ tierLabel(hit.similarity_tier) }}
              </span>
              <span class="similar-title">{{ hit.title }}</span>
              <span class="similar-by text-muted text-xs">by {{ hit.pseudonym }}</span>
              <button
                class="btn-link text-xs"
                :class="{ 'selected-ref': selectedRef === hit.slug }"
                @click="toggleRef(hit.slug)"
              >
                {{ selectedRef === hit.slug ? 'Unmark variation' : 'Mark as variation' }}
              </button>
            </li>
          </ul>
        </div>
        <!-- Submission feedback (aria-live region, always rendered) -->
        <div
          class="feedback-region"
@ -152,24 +119,13 @@
        <!-- Footer actions -->
        <div class="modal-footer flex gap-sm">
          <button
            v-if="!similarPosts.length || similarChecked"
            class="btn btn-primary"
            :disabled="submitting || !title.trim()"
            :aria-busy="submitting"
            @click="onSubmit"
          >
            <span v-if="submitting" class="spinner spinner-sm" aria-hidden="true"></span>
-            {{ submitting ? 'Publishing...' : (selectedRef ? 'Publish as variation' : 'Publish') }}
+            {{ submitting ? 'Publishing...' : 'Publish' }}
          </button>
          <button
            v-else
            class="btn btn-primary"
            :disabled="checking || !title.trim()"
            :aria-busy="checking"
            @click="onCheckThenSubmit"
          >
            <span v-if="checking" class="spinner spinner-sm" aria-hidden="true"></span>
            {{ checking ? 'Checking...' : 'Publish' }}
          </button>
          <button class="btn btn-secondary" @click="$emit('close')">
            Cancel
@ -183,7 +139,7 @@
 <script setup lang="ts">
 import { ref, onMounted, onUnmounted, nextTick } from 'vue'
 import { useCommunityStore } from '../stores/community'
-import type { PublishPayload, SimilarPost, SimilarityTier } from '../stores/community'
+import type { PublishPayload } from '../stores/community'
 const props = defineProps<{
  recipeId: number | null
@ -206,21 +162,6 @@ const submitting = ref(false)
 const submitError = ref<string | null>(null)
 const submitSuccess = ref<string | null>(null)
 const checking = ref(false)
 const similarChecked = ref(false)
 const similarPosts = ref<SimilarPost[]>([])
 const selectedRef = ref<string | null>(null)
 function tierLabel(tier: SimilarityTier): string {
  if (tier === 'exact_recipe') return 'Same recipe'
  if (tier === 'very_similar') return 'Very similar'
  return 'Similar'
 }
 function toggleRef(slug: string) {
  selectedRef.value = selectedRef.value === slug ? null : slug
 }
 const dialogRef = ref<HTMLElement | null>(null)
 const firstFocusRef = ref<HTMLButtonElement | null>(null)
 let previousFocus: HTMLElement | null = null
@ -274,17 +215,6 @@ onUnmounted(() => {
  previousFocus?.focus()
 })
 async function onCheckThenSubmit() {
  if (!title.value.trim()) return
  checking.value = true
  similarPosts.value = await store.checkSimilar(title.value.trim(), props.recipeId, postType.value)
  similarChecked.value = true
  checking.value = false
  if (!similarPosts.value.length) {
    await onSubmit()
  }
 }
 async function onSubmit() {
  submitError.value = null
  submitSuccess.value = null
@ -298,7 +228,6 @@ async function onSubmit() {
  if (outcomeNotes.value.trim()) payload.outcome_notes = outcomeNotes.value.trim()
  if (pseudonymName.value.trim()) payload.pseudonym_name = pseudonymName.value.trim()
  if (props.recipeId != null) payload.recipe_id = props.recipeId
  if (selectedRef.value) payload.similar_to_ref = selectedRef.value
  submitting.value = true
  try {
@ -420,82 +349,6 @@ async function onSubmit() {
  flex-wrap: wrap;
 }
 .similar-panel {
  background: var(--color-surface-alt, var(--color-surface));
  border: 1px solid var(--color-warning, #f59e0b);
  border-radius: var(--radius-md);
  padding: var(--spacing-sm) var(--spacing-md);
  margin-bottom: var(--spacing-md);
 }
 .similar-heading {
  margin: 0 0 var(--spacing-sm);
 }
 .similar-list {
  list-style: none;
  margin: 0;
  padding: 0;
  display: flex;
  flex-direction: column;
  gap: var(--spacing-xs);
 }
 .similar-item {
  display: flex;
  align-items: baseline;
  gap: var(--spacing-xs);
  flex-wrap: wrap;
 }
 .similar-tier-badge {
  font-size: var(--font-size-xs);
  font-weight: 700;
  padding: 1px 6px;
  border-radius: var(--radius-sm);
  flex-shrink: 0;
 }
 .tier-exact_recipe {
  background: var(--color-error-bg, #fee2e2);
  color: var(--color-error, #dc2626);
 }
 .tier-very_similar {
  background: var(--color-warning-bg, #fef3c7);
  color: var(--color-warning-text, #92400e);
 }
 .tier-somewhat_similar {
  background: var(--color-surface-alt, #f3f4f6);
  color: var(--color-text-secondary);
 }
 .similar-title {
  font-weight: 600;
  font-size: var(--font-size-sm);
 }
 .similar-by {
  flex-shrink: 0;
 }
 .btn-link {
  background: none;
  border: none;
  color: var(--color-primary);
  cursor: pointer;
  padding: 0;
  text-decoration: underline;
  font-size: var(--font-size-xs);
  margin-left: auto;
 }
 .btn-link.selected-ref {
  color: var(--color-success);
  font-weight: 700;
 }
@media (max-width: 480px) {
  .modal-panel {
    max-height: 95vh;
--- a/frontend/src/components/PublishPlanModal.vue
+++ b/frontend/src/components/PublishPlanModal.vue
@ -78,39 +78,6 @@
          <span class="form-hint">How you appear on posts -- not your real name or email.</span>
        </div>
        <!-- Similarity check results (shown before final confirm) -->
        <div
          v-if="similarPosts.length > 0"
          class="similar-panel"
          role="region"
          aria-label="Similar posts found"
        >
          <p class="similar-heading text-sm">
            <strong>Similar plans already exist.</strong>
            You can publish as-is, mark yours as a variation, or cancel.
          </p>
          <ul class="similar-list" aria-label="Existing similar posts">
            <li
              v-for="hit in similarPosts"
              :key="hit.slug"
              class="similar-item"
            >
              <span class="similar-tier-badge" :class="`tier-${hit.similarity_tier}`">
                {{ tierLabel(hit.similarity_tier) }}
              </span>
              <span class="similar-title">{{ hit.title }}</span>
              <span class="similar-by text-muted text-xs">by {{ hit.pseudonym }}</span>
              <button
                class="btn-link text-xs"
                :class="{ 'selected-ref': selectedRef === hit.slug }"
                @click="toggleRef(hit.slug)"
              >
                {{ selectedRef === hit.slug ? 'Unmark variation' : 'Mark as variation' }}
              </button>
            </li>
          </ul>
        </div>
        <!-- Submission feedback (aria-live region, always rendered) -->
        <div
          class="feedback-region"
@ -124,24 +91,13 @@
        <!-- Footer actions -->
        <div class="modal-footer flex gap-sm">
          <button
            v-if="!similarPosts.length || similarChecked"
            class="btn btn-primary"
            :disabled="submitting || !title.trim()"
            :aria-busy="submitting"
            @click="onSubmit"
          >
            <span v-if="submitting" class="spinner spinner-sm" aria-hidden="true"></span>
-            {{ submitting ? 'Publishing...' : (selectedRef ? 'Publish as variation' : 'Publish') }}
+            {{ submitting ? 'Publishing...' : 'Publish' }}
          </button>
          <button
            v-else
            class="btn btn-primary"
            :disabled="checking || !title.trim()"
            :aria-busy="checking"
            @click="onCheckThenSubmit"
          >
            <span v-if="checking" class="spinner spinner-sm" aria-hidden="true"></span>
            {{ checking ? 'Checking...' : 'Publish' }}
          </button>
          <button class="btn btn-secondary" @click="$emit('close')">
            Cancel
@ -155,7 +111,7 @@
 <script setup lang="ts">
 import { ref, onMounted, onUnmounted, nextTick } from 'vue'
 import { useCommunityStore } from '../stores/community'
-import type { PublishPayload, SimilarPost, SimilarityTier } from '../stores/community'
+import type { PublishPayload } from '../stores/community'
 const props = defineProps<{
  plan?: {
@ -180,21 +136,6 @@ const submitting = ref(false)
 const submitError = ref<string | null>(null)
 const submitSuccess = ref<string | null>(null)
 const checking = ref(false)
 const similarChecked = ref(false)
 const similarPosts = ref<SimilarPost[]>([])
 const selectedRef = ref<string | null>(null)
 function tierLabel(tier: SimilarityTier): string {
  if (tier === 'exact_recipe') return 'Same recipe'
  if (tier === 'very_similar') return 'Very similar'
  return 'Similar'
 }
 function toggleRef(slug: string) {
  selectedRef.value = selectedRef.value === slug ? null : slug
 }
 const dialogRef = ref<HTMLElement | null>(null)
 const firstFocusRef = ref<HTMLInputElement | null>(null)
 let previousFocus: HTMLElement | null = null
@ -248,19 +189,6 @@ onUnmounted(() => {
  previousFocus?.focus()
 })
 async function onCheckThenSubmit() {
  if (!title.value.trim()) return
  checking.value = true
  const planRecipeIds = props.plan?.slots?.map((s) => s.recipe_id) ?? []
  const firstRecipeId = planRecipeIds[0] ?? null
  similarPosts.value = await store.checkSimilar(title.value.trim(), firstRecipeId, 'plan')
  similarChecked.value = true
  checking.value = false
  if (!similarPosts.value.length) {
    await onSubmit()
  }
 }
 async function onSubmit() {
  submitError.value = null
  submitSuccess.value = null
@ -277,7 +205,6 @@ async function onSubmit() {
  if (props.plan?.slots?.length) {
    payload.slots = props.plan.slots.map(({ day, meal_type, recipe_id }) => ({ day, meal_type, recipe_id }))
  }
  if (selectedRef.value) payload.similar_to_ref = selectedRef.value
  submitting.value = true
  try {
@ -368,82 +295,6 @@ async function onSubmit() {
  flex-wrap: wrap;
 }
 .similar-panel {
  background: var(--color-surface-alt, var(--color-surface));
  border: 1px solid var(--color-warning, #f59e0b);
  border-radius: var(--radius-md);
  padding: var(--spacing-sm) var(--spacing-md);
  margin-bottom: var(--spacing-md);
 }
 .similar-heading {
  margin: 0 0 var(--spacing-sm);
 }
 .similar-list {
  list-style: none;
  margin: 0;
  padding: 0;
  display: flex;
  flex-direction: column;
  gap: var(--spacing-xs);
 }
 .similar-item {
  display: flex;
  align-items: baseline;
  gap: var(--spacing-xs);
  flex-wrap: wrap;
 }
 .similar-tier-badge {
  font-size: var(--font-size-xs);
  font-weight: 700;
  padding: 1px 6px;
  border-radius: var(--radius-sm);
  flex-shrink: 0;
 }
 .tier-exact_recipe {
  background: var(--color-error-bg, #fee2e2);
  color: var(--color-error, #dc2626);
 }
 .tier-very_similar {
  background: var(--color-warning-bg, #fef3c7);
  color: var(--color-warning-text, #92400e);
 }
 .tier-somewhat_similar {
  background: var(--color-surface-alt, #f3f4f6);
  color: var(--color-text-secondary);
 }
 .similar-title {
  font-weight: 600;
  font-size: var(--font-size-sm);
 }
 .similar-by {
  flex-shrink: 0;
 }
 .btn-link {
  background: none;
  border: none;
  color: var(--color-primary);
  cursor: pointer;
  padding: 0;
  text-decoration: underline;
  font-size: var(--font-size-xs);
  margin-left: auto;
 }
 .btn-link.selected-ref {
  color: var(--color-success);
  font-weight: 700;
 }
@media (max-width: 480px) {
  .modal-panel {
    max-height: 95vh;
--- a/frontend/src/components/ReceiptsView.vue
+++ b/frontend/src/components/ReceiptsView.vue
@ -175,8 +175,7 @@ async function uploadFile(file: File) {
 async function loadReceipts() {
  try {
-    const raw = await receiptsAPI.listReceipts()
+    const data = await receiptsAPI.listReceipts()
    const data = Array.isArray(raw) ? raw : []
    // Fetch OCR data for each receipt
    receipts.value = await Promise.all(
      data.map(async (receipt: any) => {
--- a/frontend/src/components/RecipeBrowserPanel.vue
+++ b/frontend/src/components/RecipeBrowserPanel.vue
@ -6,7 +6,6 @@
        v-for="domain in domains"
        :key="domain.id"
        :class="['btn', activeDomain === domain.id ? 'btn-primary' : 'btn-secondary']"
        :aria-pressed="activeDomain === domain.id"
        @click="selectDomain(domain.id)"
      >
        {{ domain.label }}
@ -16,30 +15,16 @@
    <div v-if="loadingDomains" class="text-secondary text-sm">Loading…</div>
    <div v-else-if="activeDomain" class="browser-body">
      <!-- Corpus unavailable notice — shown when all category counts are 0 -->
      <div v-if="allCountsZero" class="browser-unavailable card p-md text-secondary text-sm">
        Recipe library is not available on this instance yet. Browse categories will appear once the recipe corpus is loaded.
      </div>
      <!-- Category list + Surprise Me -->
-      <div v-else class="category-list mb-sm flex flex-wrap gap-xs">
+      <div class="category-list mb-md flex flex-wrap gap-xs">
        <button
          :class="['btn', 'btn-secondary', 'cat-btn', { active: activeCategory === '_all' }]"
          :aria-pressed="activeCategory === '_all'"
          @click="selectCategory('_all')"
        >
          All
        </button>
        <button
          v-for="cat in categories"
          :key="cat.category"
          :class="['btn', 'btn-secondary', 'cat-btn', { active: activeCategory === cat.category }]"
          :aria-pressed="activeCategory === cat.category"
          @click="selectCategory(cat.category)"
        >
          {{ cat.category }}
          <span class="cat-count">{{ cat.recipe_count }}</span>
          <span v-if="cat.has_subcategories" class="cat-drill-indicator" title="Has subcategories">›</span>
        </button>
        <button
          v-if="categories.length > 1"
@ -51,132 +36,26 @@
        </button>
      </div>
      <!-- Subcategory row — shown when the active category has subcategories -->
      <div
        v-if="activeCategoryHasSubs && (subcategories.length > 0 || loadingSubcategories)"
        class="subcategory-list mb-md flex flex-wrap gap-xs"
      >
        <span v-if="loadingSubcategories" class="text-secondary text-xs">Loading…</span>
        <template v-else>
          <button
            :class="['btn', 'btn-secondary', 'subcat-btn', { active: activeSubcategory === null }]"
            :aria-pressed="activeSubcategory === null"
            @click="selectSubcategory(null)"
          >
            All {{ activeCategory }}
          </button>
          <button
            v-for="sub in subcategories"
            :key="sub.subcategory"
            :class="['btn', 'btn-secondary', 'subcat-btn', { active: activeSubcategory === sub.subcategory }]"
            :aria-pressed="activeSubcategory === sub.subcategory"
            @click="selectSubcategory(sub.subcategory)"
          >
            {{ sub.subcategory }}
            <span class="cat-count">{{ sub.recipe_count }}</span>
            <span
              v-if="sub.recipe_count === 0"
              class="tag-cta"
              title="Know a recipe in this category? Tag it!"
              @click.stop="openTagModal(sub.subcategory)"
            >＋</span>
          </button>
        </template>
      </div>
      <!-- Browse breadcrumb — shows current position in domain > category > subcategory hierarchy -->
      <nav v-if="activeDomain && activeCategory" class="browse-breadcrumb" aria-label="Browse location">
        <button
          class="crumb-btn"
          @click="selectDomain(activeDomain)"
          :aria-current="!activeCategory ? 'page' : undefined"
        >{{ domains.find(d => d.id === activeDomain)?.label ?? activeDomain }}</button>
        <span class="crumb-sep" aria-hidden="true">›</span>
        <button
          class="crumb-btn"
          @click="selectCategory(activeCategory)"
          :aria-current="!activeSubcategory ? 'page' : undefined"
        >{{ activeCategory === '_all' ? 'All' : activeCategory }}</button>
        <template v-if="activeSubcategory">
          <span class="crumb-sep" aria-hidden="true">›</span>
          <span class="crumb-current" aria-current="page">{{ activeSubcategory }}</span>
        </template>
      </nav>
      <!-- Recipe grid -->
      <template v-if="activeCategory">
        <div v-if="loadingRecipes" class="text-secondary text-sm">Loading recipes…</div>
        <template v-else>
          <!-- Search + sort controls -->
          <div class="browser-controls flex gap-sm mb-sm flex-wrap align-center">
            <input
              v-model="searchQuery"
              @input="onSearchInput"
              type="search"
              placeholder="Filter by title…"
              class="browser-search"
            />
            <input
              v-model="requiredIngredient"
              @keyup.enter="onRequiredIngredientCommit"
              @search="onRequiredIngredientCommit"
              type="search"
              placeholder="Must include ingredient… (Enter)"
              class="browser-search"
              title="Type an ingredient and press Enter to filter"
            />
            <div class="sort-btns flex gap-xs">
              <button
                :class="['btn', 'btn-secondary', 'sort-btn', { active: sortOrder === 'default' }]"
                :aria-pressed="sortOrder === 'default'"
                @click="setSort('default')"
                title="Corpus order"
              >Default</button>
              <button
                :class="['btn', 'btn-secondary', 'sort-btn', { active: sortOrder === 'alpha' }]"
                :aria-pressed="sortOrder === 'alpha'"
                @click="setSort('alpha')"
                title="Alphabetical A→Z"
              >A→Z</button>
              <button
                :class="['btn', 'btn-secondary', 'sort-btn', { active: sortOrder === 'alpha_desc' }]"
                :aria-pressed="sortOrder === 'alpha_desc'"
                @click="setSort('alpha_desc')"
                title="Alphabetical Z→A"
              >Z→A</button>
              <button
                :class="['btn', 'btn-secondary', 'sort-btn', { active: sortOrder === 'match' }]"
                :aria-pressed="sortOrder === 'match'"
                :disabled="pantryCount === 0"
                @click="setSort('match')"
                :title="pantryCount > 0 ? 'Sort by pantry match %' : 'Add items to pantry to sort by match'"
              >Best match</button>
            </div>
          </div>
          <div class="results-header flex-between mb-sm">
-            <span
+            <span class="text-sm text-secondary">
              class="text-sm text-secondary"
              aria-live="polite"
              aria-atomic="true"
            >
              {{ total }} recipes
              <span v-if="pantryCount > 0"> — pantry match shown</span>
              <span v-if="requiredIngredient.trim()"> — must include "{{ requiredIngredient.trim() }}"</span>
            </span>
            <div class="pagination flex gap-xs">
              <button
                class="btn btn-secondary btn-xs"
                :disabled="page <= 1"
                aria-label="Previous page"
                @click="changePage(page - 1)"
              >‹ Prev</button>
-              <span class="text-sm text-secondary page-indicator" aria-live="polite">{{ page }} / {{ totalPages }}</span>
+              <span class="text-sm text-secondary page-indicator">{{ page }} / {{ totalPages }}</span>
              <button
                class="btn btn-secondary btn-xs"
                :disabled="page >= totalPages"
                aria-label="Next page"
                @click="changePage(page + 1)"
              >Next ›</button>
            </div>
@ -207,19 +86,6 @@
                  {{ Math.round(recipe.match_pct * 100) }}%
                </span>
                <!-- Time & effort split pill -->
                <span
                  v-if="recipe.active_min !== null"
                  class="time-split-pill"
                  :title="`~${formatMin(recipe.active_min)} active · ~${formatMin(recipe.passive_min ?? 0)} passive`"
                >
                  <span class="pill-active">🧑‍🍳 ~{{ formatMin(recipe.active_min) }}</span>
                  <span
                    v-if="recipe.passive_min !== null && recipe.passive_min > 0"
                    class="pill-passive"
                  >💤 ~{{ formatMin(recipe.passive_min) }}</span>
                </span>
                <!-- Save toggle -->
                <button
                  class="btn btn-secondary btn-xs"
@ -235,7 +101,7 @@
        </template>
      </template>
-      <div v-else-if="!allCountsZero" class="text-secondary text-sm">Loading recipes…</div>
+      <div v-else class="text-secondary text-sm">Loading recipes…</div>
    </div>
    <div v-else-if="!loadingDomains" class="text-secondary text-sm">Loading…</div>
@ -249,85 +115,12 @@
      @saved="savingRecipe = null"
      @unsave="savingRecipe && doUnsave(savingRecipe.id)"
    />
    <!-- Community tag modal — opened from zero-count subcategory CTA -->
    <div v-if="tagModal.open" class="modal-backdrop" @click.self="tagModal.open = false">
      <div class="modal-box" role="dialog" aria-modal="true" aria-label="Tag a recipe">
        <h3 class="text-md font-semibold mb-sm">Tag a recipe as {{ tagModal.subcategory }}</h3>
        <p class="text-sm text-secondary mb-sm">
          Search for a recipe you know belongs here. Your tag helps other users discover it.
        </p>
        <!-- Recipe search -->
        <input
          class="form-input mb-xs"
          v-model="tagModal.searchQuery"
          placeholder="Search recipe title…"
          @input="onTagSearchInput"
          autocomplete="off"
        />
        <div v-if="tagModal.searching" class="text-sm text-secondary mb-xs">Searching…</div>
        <ul v-else-if="tagModal.results.length > 0" class="tag-search-results mb-sm">
          <li
            v-for="r in tagModal.results"
            :key="r.id"
            :class="['tag-result-row', { selected: tagModal.selectedRecipe?.id === r.id }]"
            @click="tagModal.selectedRecipe = r"
          >
            <span class="tag-result-title">{{ r.title }}</span>
            <span class="tag-result-check" v-if="tagModal.selectedRecipe?.id === r.id">✓</span>
          </li>
        </ul>
        <p v-else-if="tagModal.searchQuery.length > 2" class="text-sm text-secondary mb-sm">
          No results — try a different title.
        </p>
        <!-- Location correction (pre-filled from active browse context) -->
        <div class="form-group mb-xs">
          <label class="form-label text-xs">Domain</label>
          <select class="form-input" v-model="tagModal.domain">
            <option v-for="d in domains" :key="d.id" :value="d.id">{{ d.label }}</option>
          </select>
        </div>
        <div class="form-group mb-xs">
          <label class="form-label text-xs">Category</label>
          <select class="form-input" v-model="tagModal.category">
            <option v-for="c in categories" :key="c.category" :value="c.category">
              {{ c.category }}
            </option>
          </select>
        </div>
        <div class="form-group mb-sm">
          <label class="form-label text-xs">Subcategory (optional)</label>
          <select class="form-input" v-model="tagModal.subcategoryEdit">
            <option value="">— none (category level) —</option>
            <option v-for="s in subcategories" :key="s.subcategory" :value="s.subcategory">
              {{ s.subcategory }}
            </option>
          </select>
        </div>
        <div class="flex gap-sm">
          <button
            class="btn btn-primary btn-sm"
            :disabled="!tagModal.selectedRecipe || tagModal.submitting"
            @click="submitTag"
          >
            <span v-if="tagModal.submitting">Submitting…</span>
            <span v-else>Tag this recipe</span>
          </button>
          <button class="btn btn-secondary btn-sm" @click="tagModal.open = false">Cancel</button>
        </div>
        <p v-if="tagModal.error" class="text-sm status-badge status-error mt-xs">{{ tagModal.error }}</p>
        <p v-if="tagModal.success" class="text-sm status-badge status-ok mt-xs">{{ tagModal.success }}</p>
      </div>
    </div>
  </div>
 </template>
 <script setup lang="ts">
-import { ref, computed, onMounted, watch } from 'vue'
+import { ref, computed, onMounted } from 'vue'
-import { browserAPI, type BrowserDomain, type BrowserCategory, type BrowserSubcategory, type BrowserRecipe } from '../services/api'
+import { browserAPI, type BrowserDomain, type BrowserCategory, type BrowserRecipe } from '../services/api'
 import { useSavedRecipesStore } from '../stores/savedRecipes'
 import { useInventoryStore } from '../stores/inventory'
 import SaveRecipeModal from './SaveRecipeModal.vue'
@ -343,9 +136,6 @@ const domains = ref<BrowserDomain[]>([])
 const activeDomain = ref<string | null>(null)
 const categories = ref<BrowserCategory[]>([])
 const activeCategory = ref<string | null>(null)
 const subcategories = ref<BrowserSubcategory[]>([])
 const activeSubcategory = ref<string | null>(null)
 const loadingSubcategories = ref(false)
 const recipes = ref<BrowserRecipe[]>([])
 const total = ref(0)
 const page = ref(1)
@ -353,37 +143,8 @@ const pageSize = 20
 const loadingDomains = ref(false)
 const loadingRecipes = ref(false)
 const savingRecipe = ref<BrowserRecipe | null>(null)
 const searchQuery = ref('')
 const requiredIngredient = ref('')
 const sortOrder = ref<'default' | 'alpha' | 'alpha_desc' | 'match'>('default')
 let searchDebounce: ReturnType<typeof setTimeout> | null = null
 let tagSearchDebounce: ReturnType<typeof setTimeout> | null = null
 // ── Tag modal state ────────────────────────────────────────────────────────
 const tagModal = ref({
  open: false,
  subcategory: '',       // display label (pre-filled from CTA)
  domain: '',            // editable, pre-filled
  category: '',          // editable, pre-filled
  subcategoryEdit: '',   // editable, pre-filled
  searchQuery: '',
  searching: false,
  results: [] as Array<{ id: number; title: string }>,
  selectedRecipe: null as { id: number; title: string } | null,
  submitting: false,
  error: '',
  success: '',
 })
 const totalPages = computed(() => Math.max(1, Math.ceil(total.value / pageSize)))
 const allCountsZero = computed(() =>
  categories.value.length > 0 && categories.value.every(c => c.recipe_count === 0)
 )
 const activeCategoryHasSubs = computed(() => {
  if (!activeCategory.value || activeCategory.value === '_all') return false
  return categories.value.find(c => c.category === activeCategory.value)?.has_subcategories ?? false
 })
 const pantryItems = computed(() =>
  inventoryStore.items
@ -398,18 +159,6 @@ function matchBadgeClass(pct: number): string {
  return 'status-secondary'
 }
 /**
 * Format minutes as a compact display string.
 * < 60 → "15m"
 * >= 60 → "1h 30m" (omits minutes when zero: "2h")
 */
 function formatMin(minutes: number): string {
  if (minutes < 60) return `${minutes}m`
  const h = Math.floor(minutes / 60)
  const m = minutes % 60
  return m === 0 ? `${h}h` : `${h}h ${m}m`
 }
 onMounted(async () => {
  loadingDomains.value = true
  try {
@ -423,58 +172,15 @@ onMounted(async () => {
  if (!savedStore.savedIds.size) savedStore.load()
 })
 function onSearchInput() {
  if (searchDebounce) clearTimeout(searchDebounce)
  searchDebounce = setTimeout(() => {
    page.value = 1
    loadRecipes()
  }, 350)
 }
 function onRequiredIngredientCommit() {
  page.value = 1
  loadRecipes()
 }
 // Auto-clear results when the field is emptied via backspace/select-delete
 watch(requiredIngredient, (val, prev) => {
  if (val === '' && prev !== '') {
    page.value = 1
    loadRecipes()
  }
 })
 function setSort(s: 'default' | 'alpha' | 'alpha_desc' | 'match') {
  if (sortOrder.value === s) return
  sortOrder.value = s
  page.value = 1
  loadRecipes()
 }
 // When pantry items first become available while browsing, auto-engage match sort.
 // When pantry empties out mid-session, drop back to default so the button disables cleanly.
 watch(pantryCount, (newCount, oldCount) => {
  if (newCount > 0 && oldCount === 0 && activeCategory.value) {
    setSort('match')
  } else if (newCount === 0 && sortOrder.value === 'match') {
    setSort('default')
  }
 })
 async function selectDomain(domainId: string) {
  activeDomain.value = domainId
  activeCategory.value = null
  recipes.value = []
  total.value = 0
  page.value = 1
  searchQuery.value = ''
  requiredIngredient.value = ''
  sortOrder.value = 'default'
  categories.value = await browserAPI.listCategories(domainId)
-  // Auto-select the most-populated category so content appears immediately.
+  // Auto-select the most-populated category so content appears immediately
-  // Skip when all counts are 0 (corpus not seeded) — no point loading an empty result.
+  if (categories.value.length > 0) {
  const hasRecipes = categories.value.some(c => c.recipe_count > 0)
  if (hasRecipes) {
    const top = categories.value.reduce((best, c) =>
      c.recipe_count > best.recipe_count ? c : best, categories.value[0]!)
    selectCategory(top.category)
@ -489,27 +195,6 @@ function surpriseMe() {
 async function selectCategory(category: string) {
  activeCategory.value = category
  activeSubcategory.value = null
  subcategories.value = []
  page.value = 1
  searchQuery.value = ''
  sortOrder.value = 'default'
  // Fetch subcategories in the background when the category supports them,
  // then immediately start loading recipes at the full-category level.
  const catMeta = categories.value.find(c => c.category === category)
  if (catMeta?.has_subcategories) {
    loadingSubcategories.value = true
    browserAPI.listSubcategories(activeDomain.value!, category)
      .then(subs => { subcategories.value = subs })
      .finally(() => { loadingSubcategories.value = false })
  }
  await loadRecipes()
 }
 async function selectSubcategory(subcat: string | null) {
  activeSubcategory.value = subcat
  page.value = 1
  await loadRecipes()
 }
@ -532,10 +217,6 @@ async function loadRecipes() {
        pantry_items: pantryItems.value.length > 0
          ? pantryItems.value.join(',')
          : undefined,
        subcategory: activeSubcategory.value ?? undefined,
        q: searchQuery.value.trim() || undefined,
        sort: sortOrder.value !== 'default' ? sortOrder.value : undefined,
        required_ingredient: requiredIngredient.value.trim() || undefined,
      }
    )
    recipes.value = result.recipes
@ -557,75 +238,6 @@ async function doUnsave(recipeId: number) {
  savingRecipe.value = null
  await savedStore.unsave(recipeId)
 }
 // ── Tag modal ──────────────────────────────────────────────────────────────
 function openTagModal(subcategoryName: string) {
  Object.assign(tagModal.value, {
    open: true,
    subcategory: subcategoryName,
    domain: activeDomain.value ?? '',
    category: activeCategory.value ?? '',
    subcategoryEdit: subcategoryName,
    searchQuery: '',
    searching: false,
    results: [],
    selectedRecipe: null,
    submitting: false,
    error: '',
    success: '',
  })
 }
 function onTagSearchInput() {
  if (tagSearchDebounce) clearTimeout(tagSearchDebounce)
  const q = tagModal.value.searchQuery.trim()
  if (q.length < 3) {
    tagModal.value.results = []
    return
  }
  tagSearchDebounce = setTimeout(async () => {
    tagModal.value.searching = true
    try {
      // Use the first available domain with category=_all to search all recipes by title.
      // Domain must be a real domain slug — '_all' is not valid at the browse endpoint.
      const searchDomain = domains.value[0]?.id ?? 'cuisine'
      const res = await browserAPI.browse(searchDomain, '_all', { page: 1, q })
      tagModal.value.results = (res.recipes ?? []).slice(0, 8).map(
        (r: { id: number; title: string }) => ({ id: r.id, title: r.title })
      )
    } catch {
      tagModal.value.results = []
    } finally {
      tagModal.value.searching = false
    }
  }, 350)
 }
 async function submitTag() {
  const m = tagModal.value
  if (!m.selectedRecipe) return
  m.submitting = true
  m.error = ''
  m.success = ''
  try {
    await browserAPI.submitRecipeTag({
      recipe_id: m.selectedRecipe.id,
      domain: m.domain,
      category: m.category,
      subcategory: m.subcategoryEdit || null,
      pseudonym: 'anon',  // TODO: wire real pseudonym from community store
    })
    m.success = `Tagged! It will appear here once a second user confirms.`
    setTimeout(() => { m.open = false }, 2500)
  } catch (err: any) {
    m.error = err?.message === '409'
      ? 'You have already tagged this recipe here.'
      : 'Failed to submit — please try again.'
  } finally {
    m.submitting = false
  }
 }
 </script>
 <style scoped>
@ -667,68 +279,6 @@ async function submitTag() {
  opacity: 1;
 }
 .cat-drill-indicator {
  margin-left: var(--spacing-xs);
  opacity: 0.5;
  font-size: var(--font-size-sm);
 }
 .subcategory-list {
  padding-left: var(--spacing-sm);
  border-left: 2px solid var(--color-border);
  margin-left: var(--spacing-xs);
 }
 .subcat-btn {
  font-size: var(--font-size-xs, 0.78rem);
  padding: var(--spacing-xs) var(--spacing-sm);
  opacity: 0.9;
 }
 .subcat-btn.active {
  background: var(--color-primary);
  color: white;
  border-color: var(--color-primary);
  opacity: 1;
 }
 .subcat-btn.active .cat-count {
  background: rgba(255, 255, 255, 0.2);
  color: white;
 }
 .browser-controls {
  align-items: center;
 }
 .browser-search {
  flex: 1;
  min-width: 120px;
  max-width: 260px;
  padding: var(--spacing-xs) var(--spacing-sm);
  font-size: var(--font-size-sm);
  border: 1px solid var(--color-border);
  border-radius: var(--radius-sm);
  background: var(--color-bg);
  color: var(--color-text);
 }
 .browser-search:focus {
  outline: none;
  border-color: var(--color-primary);
 }
 .sort-btn {
  font-size: var(--font-size-xs, 0.75rem);
  padding: 2px var(--spacing-sm);
 }
 .sort-btn.active {
  background: var(--color-primary);
  color: white;
  border-color: var(--color-primary);
 }
 .recipe-grid {
  display: flex;
  flex-direction: column;
@ -786,142 +336,4 @@ async function submitTag() {
 .flex-shrink-0 {
  flex-shrink: 0;
 }
 /* ── Time & effort split pill ──────────────────────────────────────────── */
 .time-split-pill {
  display: inline-flex;
  align-items: stretch;
  border-radius: var(--radius-pill, 999px);
  overflow: hidden;
  font-size: var(--font-size-xs, 0.72rem);
  white-space: nowrap;
  flex-shrink: 0;
  border: 1px solid transparent;
 }
 .pill-active {
  padding: 2px 6px;
  background: rgba(232, 168, 32, 0.18);
  color: #f0bc48;
  border-radius: var(--radius-pill, 999px) 0 0 var(--radius-pill, 999px);
 }
 /* When there is no passive segment, active gets full pill rounding */
 .time-split-pill:not(:has(.pill-passive)) .pill-active {
  border-radius: var(--radius-pill, 999px);
 }
 .pill-passive {
  padding: 2px 6px;
  background: rgba(41, 128, 185, 0.15);
  color: #5dade2;
  border-radius: 0 var(--radius-pill, 999px) var(--radius-pill, 999px) 0;
 }
 /* ── Community tag CTA ──────────────────────────────────────────────────── */
 .tag-cta {
  display: inline-flex;
  align-items: center;
  justify-content: center;
  margin-left: 0.25rem;
  width: 1.1rem;
  height: 1.1rem;
  border-radius: 50%;
  font-size: 0.75rem;
  background: var(--color-accent, #7c6fcd);
  color: #fff;
  opacity: 0.75;
  cursor: pointer;
  transition: opacity 0.15s;
 }
 .tag-cta:hover {
  opacity: 1;
 }
 /* ── Tag modal ──────────────────────────────────────────────────────────── */
 .modal-backdrop {
  position: fixed;
  inset: 0;
  background: rgba(0, 0, 0, 0.45);
  display: flex;
  align-items: center;
  justify-content: center;
  z-index: 200;
 }
 .modal-box {
  background: var(--color-surface, #fff);
  border-radius: var(--radius-md, 0.5rem);
  padding: 1.5rem;
  max-width: 28rem;
  width: 90vw;
  max-height: 85vh;
  overflow-y: auto;
  box-shadow: 0 8px 32px rgba(0,0,0,0.18);
 }
 .tag-search-results {
  list-style: none;
  padding: 0;
  margin: 0;
  border: 1px solid var(--color-border, #e0e0e0);
  border-radius: var(--radius-sm, 0.25rem);
  max-height: 12rem;
  overflow-y: auto;
 }
 .tag-result-row {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: 0.4rem 0.75rem;
  cursor: pointer;
  transition: background 0.1s;
 }
 .tag-result-row:hover,
 .tag-result-row.selected {
  background: var(--color-hover, #f0eeff);
 }
 .tag-result-title {
  font-size: 0.875rem;
  flex: 1;
 }
 .tag-result-check {
  color: var(--color-accent, #7c6fcd);
  font-size: 0.875rem;
  margin-left: 0.5rem;
 }
 /* ── Browse breadcrumb ───────────────────────────────────────────────────── */
 .browse-breadcrumb {
  display: flex;
  align-items: center;
  flex-wrap: wrap;
  gap: 2px;
  margin-bottom: var(--spacing-sm);
  font-size: var(--font-size-xs, 0.78rem);
  color: var(--color-text-secondary);
 }
 .crumb-btn {
  background: none;
  border: none;
  padding: 2px 4px;
  cursor: pointer;
  color: var(--color-primary);
  font-size: inherit;
  border-radius: var(--radius-sm);
 }
 .crumb-btn:hover {
  text-decoration: underline;
 }
 .crumb-sep {
  opacity: 0.5;
  padding: 0 2px;
 }
 .crumb-current {
  padding: 2px 4px;
  color: var(--color-text);
  font-weight: 500;
 }
 </style>
--- a/frontend/src/components/RecipeDetailPanel.vue
+++ b/frontend/src/components/RecipeDetailPanel.vue
@ -20,16 +20,6 @@
              @click="showSaveModal = true"
              :aria-label="isSaved ? 'Edit saved recipe' : 'Save recipe'"
            >{{ isSaved ? '★ Saved' : '☆ Save' }}</button>
            <!-- Cook mode toggle -->
          <button
            v-if="recipe.directions.length > 0"
            class="btn btn-cook"
            :class="{ 'btn-cook--active': cookModeActive }"
            @click="cookModeActive ? exitCookMode() : enterCookMode()"
            :aria-label="cookModeActive ? 'Exit cook mode' : 'Enter cook mode'"
            :aria-pressed="cookModeActive"
          >{{ cookModeActive ? '✕ Exit' : 'Cook' }}</button>
            <button class="btn-close" @click="$emit('close')" aria-label="Close panel">✕</button>
          </div>
        </div>
@ -43,19 +33,8 @@
        >View original ↗</a>
      </div>
-      <!-- Cook mode bar: progress + step counter -->
+      <!-- Scrollable body -->
-      <div v-if="cookModeActive" class="cook-mode-bar" role="status" :aria-label="`Step ${cookStep + 1} of ${cookStepCount}`">
+      <div class="detail-body">
        <div class="cook-progress-track">
          <div
            class="cook-progress-fill"
            :style="{ width: `${cookProgress * 100}%` }"
          ></div>
        </div>
        <span class="cook-step-counter">Step {{ cookStep + 1 }} of {{ cookStepCount }}</span>
      </div>
      <!-- Normal scrollable body -->
      <div v-if="!cookModeActive" class="detail-body">
        <!-- Serving multiplier -->
        <div class="serving-scale-row">
@ -72,14 +51,6 @@
        </div>
        <!-- Ingredients: have vs. need in a two-column layout -->
        <details open class="ingredients-collapsible">
          <summary class="ingredients-collapsible-summary">
            Ingredients
            <span class="ingr-summary-counts">
              <span v-if="recipe.matched_ingredients?.length" class="ingr-count ingr-count-have">{{ recipe.matched_ingredients.length }} ✓</span>
              <span v-if="recipe.missing_ingredients?.length" class="ingr-count ingr-count-need">{{ recipe.missing_ingredients.length }} needed</span>
            </span>
          </summary>
        <div class="ingredients-grid">
          <div v-if="recipe.matched_ingredients?.length > 0" class="ingredient-col ingredient-col-have">
            <h3 class="col-label col-label-have">From your pantry</h3>
@ -127,35 +98,6 @@
            >{{ checkedIngredients.size === recipe.missing_ingredients.length ? 'Deselect all' : 'Select all' }}</button>
          </div>
        </div>
        </details>
        <!-- Time & effort summary cards -->
        <div v-if="recipe.time_effort" class="effort-summary">
          <div class="effort-card effort-card-active">
            <span class="effort-label">Active</span>
            <span class="effort-value">{{ formatDetailMin(recipe.time_effort.active_min) }}</span>
          </div>
          <div v-if="recipe.time_effort.passive_min > 0" class="effort-card effort-card-passive">
            <span class="effort-label">Hands-off</span>
            <span class="effort-value">{{ formatDetailMin(recipe.time_effort.passive_min) }}</span>
          </div>
          <div class="effort-card effort-card-total">
            <span class="effort-label">Total</span>
            <span class="effort-value">{{ formatDetailMin(recipe.time_effort.total_min) }}</span>
          </div>
          <div class="effort-level-badge" :class="'effort-' + recipe.time_effort.effort_label">
            {{ recipe.time_effort.effort_label }}
          </div>
        </div>
        <!-- Equipment chips -->
        <div v-if="recipe.time_effort?.equipment?.length" class="equipment-chips">
          <span
            v-for="eq in recipe.time_effort.equipment"
            :key="eq"
            class="equipment-chip"
          >{{ EQUIPMENT_ICONS[eq] ?? '🍴' }} {{ eq }}</span>
        </div>
        <!-- Swap candidates -->
        <details v-if="recipe.swap_candidates.length > 0" class="detail-collapsible">
@ -203,121 +145,24 @@
          </ul>
        </div>
-        <!-- Directions (annotated) -->
+        <!-- Directions -->
-        <details open v-if="recipe.directions.length > 0" class="steps-collapsible">
+        <div v-if="recipe.directions.length > 0" class="detail-section">
-          <summary class="steps-collapsible-summary">
+          <h3 class="section-label">Steps</h3>
-            Steps <span class="steps-count">({{ recipe.directions.length }})</span>
+          <ol class="directions-list">
-          </summary>
+            <li v-for="(step, i) in recipe.directions" :key="i" class="text-sm direction-step">{{ step }}</li>
          <ol class="directions-list directions-list-annotated">
            <li
              v-for="(step, i) in recipe.directions"
              :key="i"
              class="text-sm direction-step direction-step-annotated"
              :class="{ 'step-passive': stepAnalysis(i)?.is_passive }"
            >
              <div class="step-badge-row">
                <span v-if="stepAnalysis(i)?.is_passive" class="step-type-badge step-type-wait">Wait</span>
                <span v-else-if="stepAnalysis(i)" class="step-type-badge step-type-active">Active</span>
              </div>
              <p class="step-text">{{ step }}</p>
              <p v-if="passiveHint(stepAnalysis(i))" class="step-passive-hint">{{ passiveHint(stepAnalysis(i)) }}</p>
            </li>
          </ol>
        </details>
        <!-- Community tags — accepted location tags from other users -->
        <div v-if="communityTags.length > 0" class="detail-section community-tags-section">
          <h3 class="section-label">Community categories</h3>
          <div class="community-tags-list">
            <span
              v-for="tag in communityTags"
              :key="tag.id"
              class="community-tag-chip"
              :class="{ 'community-tag-chip--accepted': tag.accepted }"
              :title="tag.accepted ? 'Confirmed by the community' : 'Pending confirmation'"
            >
              {{ tag.domain }} › {{ tag.category }}<template v-if="tag.subcategory"> › {{ tag.subcategory }}</template>
              <span v-if="tag.accepted" class="community-tag-check" aria-label="Confirmed">✓</span>
            </span>
          </div>
        </div>
        <!-- Bottom padding so last step isn't hidden behind sticky footer -->
        <div style="height: var(--spacing-xl)" />
      </div>
      <!-- Cook mode: single-step view -->
      <div
        v-else
        class="detail-body cook-step-view"
        @touchstart.passive="onTouchStart"
        @touchend.passive="onTouchEnd"
      >
        <div class="cook-step-label">STEP {{ cookStep + 1 }}</div>
        <div v-if="currentStepAnalysis" class="cook-step-badge-row">
          <span
            class="cook-step-badge"
            :class="currentStepAnalysis.is_passive ? 'cook-badge--wait' : 'cook-badge--active'"
          >{{ currentStepAnalysis.is_passive ? 'Wait' : 'Active' }}</span>
        </div>
        <p class="cook-step-text">{{ recipe.directions[cookStep] }}</p>
        <p
          v-if="currentStepAnalysis?.detected_minutes != null"
          class="cook-step-hint"
        >~{{ currentStepAnalysis.detected_minutes }} min hands-off</p>
        <div class="cook-nav">
          <button
            class="btn cook-nav-prev"
            :class="{ 'cook-nav--disabled': cookStep === 0 }"
            :disabled="cookStep === 0"
            :aria-label="cookStep === 0 ? 'No previous step' : 'Previous step'"
            @click="prevStep"
          >← Prev</button>
          <button
            class="btn cook-nav-next"
            :class="{ 'cook-nav--done': isLastStep }"
            :aria-label="isLastStep ? 'Done cooking' : 'Next step'"
            @click="nextStep"
          >{{ isLastStep ? 'Done ✓' : 'Next →' }}</button>
        </div>
      </div>
      <!-- Sticky footer -->
      <div class="detail-footer">
        <div v-if="cookDone" class="cook-success">
          <span class="cook-success-icon">✓</span>
          Enjoy your meal! Recipe dismissed from suggestions.
          <button class="btn btn-secondary btn-sm mt-xs" @click="$emit('close')">Close</button>
          <!-- Leftover shelf-life section -->
          <div v-if="leftoversLoading" class="leftovers-panel text-sm text-secondary mt-sm">
            Working out storage info…
          </div>
          <div v-else-if="leftovers && !leftoversDismissed" class="leftovers-panel mt-sm">
            <div class="leftovers-header flex-between">
              <span class="text-sm font-semibold">Leftovers</span>
              <button class="btn-icon btn-xs" @click="leftoversDismissed = true" aria-label="Dismiss storage info">✕</button>
            </div>
            <div class="leftovers-grid mt-xs">
              <div class="leftovers-cell">
                <span class="leftovers-icon">❄️</span>
                <span class="text-sm">Fridge: <strong>{{ leftovers.fridge_days }} day{{ leftovers.fridge_days !== 1 ? 's' : '' }}</strong></span>
              </div>
              <div v-if="leftovers.freeze_days !== null" class="leftovers-cell">
                <span class="leftovers-icon">🧊</span>
                <span class="text-sm">Freezer: <strong>{{ leftovers.freeze_days }} day{{ leftovers.freeze_days !== 1 ? 's' : '' }}</strong></span>
              </div>
            </div>
            <p v-if="leftovers.freeze_by_day" class="text-xs text-secondary mt-xs">
              Freeze by day {{ leftovers.freeze_by_day }} for best results.
            </p>
            <p class="text-xs text-secondary mt-xs">{{ leftovers.storage_advice }}</p>
          </div>
        </div>
        <template v-else>
          <button class="btn btn-secondary" @click="$emit('close')">Back</button>
@ -371,27 +216,15 @@
 import { ref, computed, onMounted, onUnmounted, nextTick } from 'vue'
 import { useRecipesStore } from '../stores/recipes'
 import { useSavedRecipesStore } from '../stores/savedRecipes'
-import { inventoryAPI, recipesAPI, browserAPI } from '../services/api'
+import { inventoryAPI } from '../services/api'
-import type { RecipeSuggestion, GroceryLink, StepAnalysis } from '../services/api'
+import type { RecipeSuggestion, GroceryLink } from '../services/api'
 import SaveRecipeModal from './SaveRecipeModal.vue'
 const dialogRef = ref<HTMLElement | null>(null)
 let previousFocus: HTMLElement | null = null
 function handleKeydown(e: KeyboardEvent) {
-  if (e.key === 'Escape') {
+  if (e.key === 'Escape') emit('close')
    emit('close')
    return
  }
  if (cookModeActive.value) {
    if (e.key === 'ArrowRight') {
      e.preventDefault()
      nextStep()
    } else if (e.key === 'ArrowLeft') {
      e.preventDefault()
      prevStep()
    }
  }
 }
 onMounted(() => {
@ -403,12 +236,6 @@ onMounted(() => {
    )
    ;(focusable ?? dialogRef.value)?.focus()
  })
  // Load community tags in the background — non-critical, silently skip on error
  browserAPI.listRecipeTags(props.recipe.id).then((tags) => {
    communityTags.value = tags
  }).catch(() => {
    // Community tags are supplemental; silently skip on error
  })
 })
 onUnmounted(() => {
@ -433,77 +260,6 @@ const showSaveModal = ref(false)
 const isSaved = computed(() => savedStore.isSaved(props.recipe.id))
 const cookDone = ref(false)
 // ── Community tags ────────────────────────────────────────
 type CommunityTag = { id: number; domain: string; category: string; subcategory: string | null; pseudonym: string; upvotes: number; accepted: boolean }
 const communityTags = ref<CommunityTag[]>([])
 // ── Leftover shelf-life ────────────────────────────────────
 type LeftoversData = { fridge_days: number; freeze_days: number | null; freeze_by_day: number | null; storage_advice: string }
 const leftovers = ref<LeftoversData | null>(null)
 const leftoversLoading = ref(false)
 const leftoversDismissed = ref(false)
 // ── Cook mode ─────────────────────────────────────────────
 const cookModeActive = ref(false)
 const cookStep = ref(0) // 0-indexed
 function enterCookMode() {
  cookModeActive.value = true
  cookStep.value = 0
 }
 function exitCookMode() {
  cookModeActive.value = false
  cookStep.value = 0
 }
 function nextStep() {
  const lastIdx = props.recipe.directions.length - 1
  if (cookStep.value < lastIdx) {
    cookStep.value++
  } else {
    handleCook()
    exitCookMode()
  }
 }
 function prevStep() {
  if (cookStep.value > 0) cookStep.value--
 }
 // Reads step_analyses from kiwi#50 time_effort — null-safe
 const currentStepAnalysis = computed(() => {
  return props.recipe.time_effort?.step_analyses?.[cookStep.value] ?? null
 })
 const cookStepCount = computed(() => props.recipe.directions.length)
 const isLastStep = computed(() => cookStep.value === cookStepCount.value - 1)
 const cookProgress = computed(() =>
  cookStepCount.value > 1 ? cookStep.value / (cookStepCount.value - 1) : 1
 )
 // Touch state for swipe navigation
 const touchStartX = ref(0)
 const touchStartY = ref(0)
 function onTouchStart(e: TouchEvent) {
  touchStartX.value = e.changedTouches[0]!.clientX
  touchStartY.value = e.changedTouches[0]!.clientY
 }
 function onTouchEnd(e: TouchEvent) {
  const dx = e.changedTouches[0]!.clientX - touchStartX.value
  const dy = e.changedTouches[0]!.clientY - touchStartY.value
  // Require predominantly horizontal gesture
  if (Math.abs(dx) >= 40 && Math.abs(dy) < 80) {
    if (dx < 0) {
      nextStep()    // swipe left → next
    } else {
      prevStep()    // swipe right → prev
    }
  }
 }
 const shareCopied = ref(false)
 // Serving scale multiplier: 1×, 2×, 3×, 4×
@ -569,39 +325,6 @@ function scaleIngredient(ing: string, scale: number): string {
  return scaled + ing.slice(m[0].length)
 }
 // Time & effort helpers
 function formatDetailMin(minutes: number): string {
  if (minutes < 60) return `${minutes} min`
  const h = Math.floor(minutes / 60)
  const m = minutes % 60
  return m === 0 ? `${h} hr` : `${h} hr ${m} min`
 }
 const EQUIPMENT_ICONS: Record<string, string> = {
  oven: '♨',
  stovetop: '🔥',
  blender: '⚡',
  'food processor': '⚡',
  microwave: '📡',
  grill: '🔥',
  'slow cooker': '⏲',
  'instant pot': '⏲',
  mixer: '🌀',
  skillet: '🍳',
  'cast iron': '🍳',
  wok: '🍳',
 }
 function stepAnalysis(i: number): StepAnalysis | null {
  return props.recipe.time_effort?.step_analyses?.[i] ?? null
 }
 function passiveHint(analysis: StepAnalysis | null): string {
  if (!analysis?.is_passive) return ''
  if (analysis.detected_minutes) return `~${analysis.detected_minutes} min hands-off`
  return 'Hands-off time'
 }
 // Shopping: add purchased ingredients to pantry
 const checkedIngredients = ref<Set<string>>(new Set())
 const addingToPantry = ref(false)
@ -680,20 +403,10 @@ function groceryLinkFor(ingredient: string): GroceryLink | undefined {
  return props.groceryLinks.find((l) => l.ingredient.toLowerCase() === needle)
 }
-async function handleCook() {
+function handleCook() {
  recipesStore.logCook(props.recipe.id, props.recipe.title)
  cookDone.value = true
  emit('cooked', props.recipe)
  if (props.recipe.id) {
    leftoversLoading.value = true
    try {
      leftovers.value = await recipesAPI.getLeftovers(props.recipe.id)
    } catch {
      // Silently skip — shelf life is supplemental info, not critical
    } finally {
      leftoversLoading.value = false
    }
  }
 }
 </script>
@ -777,36 +490,6 @@ async function handleCook() {
  border-color: var(--color-warning);
 }
 /* ── Cook mode button ───────────────────────────────────── */
 .btn-cook {
  font-size: var(--font-size-sm);
  padding: var(--spacing-xs) var(--spacing-sm);
  background: rgba(232, 168, 32, 0.15);
  border: 1px solid rgba(232, 168, 32, 0.3);
  color: #f0bc48;
  border-radius: var(--radius-sm, 4px);
  cursor: pointer;
  white-space: nowrap;
  transition: background 0.15s, border-color 0.15s;
 }
 .btn-cook:hover {
  background: rgba(232, 168, 32, 0.25);
  border-color: rgba(232, 168, 32, 0.5);
 }
 .btn-cook--active {
  background: rgba(232, 168, 32, 0.22);
  border-color: rgba(232, 168, 32, 0.5);
 }
@media (max-width: 380px) {
  .btn-cook {
    padding: 2px 8px;
    font-size: var(--font-size-xs);
  }
 }
 .btn-close {
  background: transparent;
  border: none;
@ -1188,377 +871,6 @@ async function handleCook() {
  line-height: 1.6;
 }
 /* ── Ingredients collapsible ────────────────────────────── */
 .ingredients-collapsible {
  margin-bottom: var(--spacing-md);
 }
 .ingredients-collapsible-summary {
  font-size: var(--font-size-sm);
  font-weight: 600;
  cursor: pointer;
  list-style: none;
  display: flex;
  align-items: center;
  gap: var(--spacing-sm);
  padding: var(--spacing-xs) 0;
  color: var(--color-text-primary);
 }
 .ingredients-collapsible-summary::-webkit-details-marker {
  display: none;
 }
 .ingredients-collapsible-summary::before {
  content: '\25B6';
  font-size: 10px;
  color: var(--color-text-muted);
  transition: transform 0.15s;
  display: inline-block;
 }
 details[open].ingredients-collapsible .ingredients-collapsible-summary::before {
  transform: rotate(90deg);
 }
 .ingr-summary-counts {
  display: flex;
  gap: var(--spacing-xs);
  margin-left: auto;
 }
 .ingr-count {
  font-size: var(--font-size-xs);
  padding: 1px 6px;
  border-radius: var(--radius-pill);
 }
 .ingr-count-have {
  background: var(--color-success-bg, #dcfce7);
  color: var(--color-success, #16a34a);
 }
 .ingr-count-need {
  background: var(--color-warning-bg, #fef9c3);
  color: var(--color-warning, #ca8a04);
 }
 /* ── Effort summary cards ───────────────────────────────── */
 .effort-summary {
  display: flex;
  gap: var(--spacing-xs);
  flex-wrap: wrap;
  align-items: center;
  margin-bottom: var(--spacing-sm);
 }
 .effort-card {
  display: flex;
  flex-direction: column;
  align-items: center;
  padding: var(--spacing-xs) var(--spacing-sm);
  border-radius: var(--radius-md, 8px);
  min-width: 64px;
 }
 .effort-card-active {
  background: var(--color-success-bg, #dcfce7);
 }
 .effort-card-passive {
  background: var(--color-info-bg, #dbeafe);
 }
 .effort-card-total {
  background: var(--color-bg-secondary, #f5f5f5);
 }
 .effort-label {
  font-size: var(--font-size-xs);
  color: var(--color-text-muted);
  text-transform: uppercase;
  letter-spacing: 0.05em;
 }
 .effort-value {
  font-size: var(--font-size-sm);
  font-weight: 700;
  color: var(--color-text-primary);
 }
 .effort-level-badge {
  font-size: var(--font-size-xs);
  font-weight: 600;
  text-transform: capitalize;
  padding: 2px 10px;
  border-radius: var(--radius-pill);
  margin-left: auto;
 }
 .effort-quick {
  background: var(--color-success-bg, #dcfce7);
  color: var(--color-success, #16a34a);
 }
 .effort-moderate {
  background: var(--color-info-bg, #dbeafe);
  color: var(--color-info-light, #2563eb);
 }
 .effort-involved {
  background: var(--color-warning-bg, #fef9c3);
  color: var(--color-warning, #ca8a04);
 }
 /* ── Equipment chips ────────────────────────────────────── */
 .equipment-chips {
  display: flex;
  gap: var(--spacing-xs);
  flex-wrap: wrap;
  margin-bottom: var(--spacing-md);
 }
 .equipment-chip {
  font-size: var(--font-size-xs);
  padding: 2px 8px;
  border-radius: var(--radius-pill);
  background: var(--color-bg-secondary, #f5f5f5);
  color: var(--color-text-secondary);
  border: 1px solid var(--color-border);
 }
 /* ── Steps collapsible ──────────────────────────────────── */
 .steps-collapsible {
  margin-bottom: var(--spacing-md);
 }
 .steps-collapsible-summary {
  font-size: var(--font-size-sm);
  font-weight: 600;
  cursor: pointer;
  list-style: none;
  padding: var(--spacing-xs) 0;
  color: var(--color-text-primary);
  display: flex;
  align-items: center;
  gap: var(--spacing-xs);
 }
 .steps-collapsible-summary::-webkit-details-marker {
  display: none;
 }
 .steps-collapsible-summary::before {
  content: '\25B6';
  font-size: 10px;
  color: var(--color-text-muted);
  transition: transform 0.15s;
  display: inline-block;
 }
 details[open].steps-collapsible .steps-collapsible-summary::before {
  transform: rotate(90deg);
 }
 .steps-count {
  color: var(--color-text-muted);
  font-weight: 400;
 }
 .directions-list-annotated {
  padding-left: var(--spacing-md);
 }
 .direction-step-annotated {
  margin-bottom: var(--spacing-md);
  padding: var(--spacing-sm);
  border-radius: var(--radius-sm, 4px);
  border-left: 3px solid var(--color-border);
 }
 .step-passive {
  border-left-color: var(--color-info-light, #60a5fa);
  background: var(--color-info-bg, #dbeafe);
 }
 .step-badge-row {
  margin-bottom: 4px;
 }
 .step-type-badge {
  font-size: 10px;
  font-weight: 700;
  text-transform: uppercase;
  letter-spacing: 0.06em;
  padding: 1px 6px;
  border-radius: var(--radius-pill);
 }
 .step-type-active {
  background: var(--color-success-bg, #dcfce7);
  color: var(--color-success, #16a34a);
 }
 .step-type-wait {
  background: var(--color-info-bg, #dbeafe);
  color: var(--color-info-light, #2563eb);
 }
 .step-text {
  margin: 0;
  line-height: 1.6;
 }
 .step-passive-hint {
  margin: 4px 0 0;
  font-size: var(--font-size-xs);
  color: var(--color-info-light, #2563eb);
  font-style: italic;
 }
 /* ── Cook mode bar ──────────────────────────────────────── */
 .cook-mode-bar {
  flex-shrink: 0;
  padding: var(--spacing-sm) var(--spacing-md) var(--spacing-xs);
  border-bottom: 1px solid var(--color-border);
  display: flex;
  flex-direction: column;
  gap: 4px;
 }
 .cook-progress-track {
  height: 4px;
  border-radius: 2px;
  background: rgba(255, 255, 255, 0.08);
  overflow: hidden;
 }
 .cook-progress-fill {
  height: 100%;
  border-radius: 2px;
  background: #f0bc48;
  transition: width 0.25s ease;
 }
 .cook-step-counter {
  font-size: 11px;
  color: rgba(255, 248, 235, 0.38);
  letter-spacing: 0.02em;
 }
 /* ── Cook mode step view ────────────────────────────────── */
 .cook-step-view {
  flex: 1;
  overflow-y: auto;
  display: flex;
  flex-direction: column;
  padding: var(--spacing-lg) var(--spacing-md) var(--spacing-md);
  gap: var(--spacing-sm);
 }
 .cook-step-label {
  font-size: 10px;
  font-weight: 700;
  text-transform: uppercase;
  letter-spacing: 0.12em;
  color: rgba(255, 248, 235, 0.35);
 }
 .cook-step-badge-row {
  display: flex;
  gap: var(--spacing-xs);
 }
 .cook-step-badge {
  display: inline-flex;
  align-items: center;
  padding: 2px 10px;
  border-radius: var(--radius-pill);
  font-size: var(--font-size-xs);
  font-weight: 600;
  letter-spacing: 0.03em;
 }
 .cook-badge--active {
  background: rgba(232, 168, 32, 0.18);
  color: #f0bc48;
  border: 1px solid rgba(232, 168, 32, 0.35);
 }
 .cook-badge--wait {
  background: rgba(96, 165, 250, 0.15);
  color: #93c5fd;
  border: 1px solid rgba(96, 165, 250, 0.3);
 }
 .cook-step-text {
  font-size: 15px;
  font-weight: 500;
  color: rgba(255, 248, 235, 0.92);
  line-height: 1.5;
  margin: 0;
 }
 .cook-step-hint {
  font-size: 11px;
  color: rgba(255, 248, 235, 0.38);
  margin: 0;
 }
 /* ── Cook mode navigation ───────────────────────────────── */
 .cook-nav {
  display: flex;
  gap: var(--spacing-sm);
  margin-top: auto;
  padding-top: var(--spacing-md);
 }
 .cook-nav-prev {
  flex: 1;
  background: transparent;
  border: 1px solid var(--color-border);
  color: var(--color-text-secondary);
  border-radius: var(--radius-md, 8px);
  padding: var(--spacing-sm) var(--spacing-md);
  font-size: var(--font-size-sm);
  font-weight: 500;
  cursor: pointer;
  transition: opacity 0.15s;
 }
 .cook-nav--disabled {
  opacity: 0.35;
  pointer-events: none;
  cursor: default;
 }
 .cook-nav-next {
  flex: 2;
  background: rgba(232, 168, 32, 0.18);
  border: 1px solid rgba(232, 168, 32, 0.4);
  color: #f0bc48;
  border-radius: var(--radius-md, 8px);
  padding: var(--spacing-sm) var(--spacing-md);
  font-size: var(--font-size-sm);
  font-weight: 600;
  cursor: pointer;
  transition: background 0.15s, border-color 0.15s, color 0.15s;
 }
 .cook-nav-next:hover {
  background: rgba(232, 168, 32, 0.28);
 }
 .cook-nav--done {
  background: rgba(127, 192, 115, 0.18);
  border-color: rgba(127, 192, 115, 0.4);
  color: #7fc073;
 }
 .cook-nav--done:hover {
  background: rgba(127, 192, 115, 0.28);
 }
 /* ── Sticky footer ──────────────────────────────────────── */
 .detail-footer {
  padding: var(--spacing-md);
@ -1626,68 +938,4 @@ details[open].steps-collapsible .steps-collapsible-summary::before {
  padding: var(--spacing-xs) var(--spacing-sm);
  font-size: var(--font-size-sm);
 }
 .leftovers-panel {
  background: var(--color-surface-alt, var(--color-surface));
  border: 1px solid var(--color-border);
  border-radius: var(--radius-md);
  padding: var(--spacing-sm);
  text-align: left;
 }
 .leftovers-header {
  align-items: center;
 }
 .leftovers-grid {
  display: flex;
  gap: var(--spacing-md);
  flex-wrap: wrap;
 }
 .leftovers-cell {
  display: flex;
  align-items: center;
  gap: var(--spacing-xs);
 }
 .leftovers-icon {
  font-size: 1rem;
  line-height: 1;
 }
 /* ── Community tags section ──────────────────────────────── */
 .community-tags-section {
  padding-top: var(--spacing-sm);
 }
 .community-tags-list {
  display: flex;
  flex-wrap: wrap;
  gap: var(--spacing-xs);
 }
 .community-tag-chip {
  display: inline-flex;
  align-items: center;
  gap: 0.25rem;
  padding: 2px var(--spacing-sm);
  border-radius: var(--radius-pill, 999px);
  font-size: var(--font-size-xs, 0.72rem);
  background: var(--color-bg-secondary);
  color: var(--color-text-secondary);
  border: 1px solid var(--color-border);
  white-space: nowrap;
 }
 .community-tag-chip--accepted {
  background: rgba(124, 111, 205, 0.12);
  color: var(--color-accent, #7c6fcd);
  border-color: rgba(124, 111, 205, 0.3);
 }
 .community-tag-check {
  font-size: 0.65rem;
  opacity: 0.8;
 }
 </style>
--- a/frontend/src/components/RecipeScanModal.vue
+++ b/frontend/src/components/RecipeScanModal.vue
@ -1,849 +0,0 @@
 <template>
  <div class="modal-overlay" @click.self="close" role="dialog" aria-modal="true" :aria-labelledby="titleId">
    <div class="modal-panel scan-modal">
      <!-- Header -->
      <div class="modal-header">
        <h2 :id="titleId" class="modal-title">
          <span v-if="phase === 'upload'">Scan a Recipe</span>
          <span v-else-if="phase === 'processing'">Scanning...</span>
          <span v-else>Review Recipe</span>
        </h2>
        <button class="btn-icon close-btn" @click="close" aria-label="Close">
          <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
            <line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
          </svg>
        </button>
      </div>
      <!-- ── Upload phase ── -->
      <div v-if="phase === 'upload'" class="modal-body">
        <p class="hint-text">
          Photograph a recipe card, cookbook page, or handwritten note.
          For multi-page recipes (ingredients on one page, directions on another)
          select both photos together — up to 4 images.
        </p>
        <!-- Drop zone -->
        <div
          class="drop-zone"
          :class="{ 'drop-zone-active': isDragging, 'has-files': selectedFiles.length > 0 }"
          @dragover.prevent="isDragging = true"
          @dragleave="isDragging = false"
          @drop.prevent="onDrop"
          @click="fileInput?.click()"
          role="button"
          tabindex="0"
          @keydown.enter.space="fileInput?.click()"
          aria-label="Click or drop photos here"
        >
          <input
            ref="fileInput"
            type="file"
            accept="image/jpeg,image/jpg,image/png,image/webp,image/heic,image/heif"
            multiple
            class="hidden-input"
            @change="onFileChange"
          />
          <div v-if="selectedFiles.length === 0" class="drop-zone-empty">
            <svg width="40" height="40" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" class="camera-icon">
              <path d="M23 19a2 2 0 0 1-2 2H3a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h4l2-3h6l2 3h4a2 2 0 0 1 2 2z"/>
              <circle cx="12" cy="13" r="4"/>
            </svg>
            <p class="drop-zone-label">Tap or drop photos here</p>
            <p class="drop-zone-sub">JPEG, PNG, WebP, HEIC — up to 4 photos</p>
          </div>
          <div v-else class="file-preview-grid">
            <div
              v-for="(_file, i) in selectedFiles"
              :key="i"
              class="file-preview-item"
            >
              <img :src="previewUrls[i]" :alt="`Photo ${i + 1}`" class="preview-img" />
              <button
                class="remove-file-btn"
                @click.stop="removeFile(i)"
                :aria-label="`Remove photo ${i + 1}`"
              >
                <svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3">
                  <line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
                </svg>
              </button>
              <p class="preview-label">Page {{ i + 1 }}</p>
            </div>
            <div
              v-if="selectedFiles.length < 4"
              class="file-preview-add"
              @click.stop="fileInput?.click()"
              role="button"
              tabindex="0"
              @keydown.enter.space.stop="fileInput?.click()"
              aria-label="Add another photo"
            >
              <svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
                <line x1="12" y1="5" x2="12" y2="19"/><line x1="5" y1="12" x2="19" y2="12"/>
              </svg>
            </div>
          </div>
        </div>
        <div v-if="uploadError" class="status-badge status-error mt-sm" role="alert">
          {{ uploadError }}
        </div>
        <div class="modal-footer">
          <button class="btn btn-secondary" @click="close">Cancel</button>
          <button
            class="btn btn-primary"
            :disabled="selectedFiles.length === 0"
            @click="startScan"
          >
            Scan Recipe
          </button>
        </div>
      </div>
      <!-- ── Processing phase ── -->
      <div v-else-if="phase === 'processing'" class="modal-body processing-body">
        <div class="scan-spinner" aria-live="polite" aria-label="Scanning recipe">
          <svg class="spin-icon" width="48" height="48" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5">
            <path d="M23 19a2 2 0 0 1-2 2H3a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h4l2-3h6l2 3h4a2 2 0 0 1 2 2z"/>
            <circle cx="12" cy="13" r="4"/>
          </svg>
          <p class="processing-label">{{ scanStatusMessage }}</p>
          <p class="processing-sub">This can take up to a minute on first use.</p>
        </div>
      </div>
      <!-- ── Review phase ── -->
      <div v-else-if="phase === 'review' && extracted" class="modal-body review-body">
        <!-- Confidence banner -->
        <div
          v-if="extracted.confidence !== 'high' || extracted.warnings.length > 0"
          :class="['status-badge', extracted.confidence === 'low' ? 'status-warning' : 'status-info', 'mb-sm']"
          role="status"
        >
          <span v-if="extracted.confidence === 'low'">Low confidence scan — handwritten or degraded text. Please review carefully.</span>
          <span v-else>Medium confidence. Check the fields below.</span>
          <ul v-if="extracted.warnings.length > 0" class="warning-list">
            <li v-for="w in extracted.warnings" :key="w">{{ w }}</li>
          </ul>
        </div>
        <!-- Pantry match badge -->
        <div v-if="extracted.ingredients.length > 0" class="pantry-match-row mb-sm">
          <span class="pantry-badge" :class="pantryMatchClass">
            {{ extracted.pantry_match_pct }}% pantry match
            ({{ pantryCount }} of {{ extracted.ingredients.length }} ingredients on hand)
          </span>
        </div>
        <!-- Editable fields -->
        <div class="review-form">
          <div class="form-group">
            <label class="form-label" for="scan-title">Recipe name</label>
            <input
              id="scan-title"
              v-model="editTitle"
              class="form-input"
              type="text"
              placeholder="Recipe name"
              required
            />
          </div>
          <div class="form-row-2">
            <div class="form-group">
              <label class="form-label" for="scan-servings">Servings</label>
              <input id="scan-servings" v-model="editServings" class="form-input" type="text" placeholder="e.g. 2" />
            </div>
            <div class="form-group">
              <label class="form-label" for="scan-cooktime">Cook time</label>
              <input id="scan-cooktime" v-model="editCookTime" class="form-input" type="text" placeholder="e.g. 25 min" />
            </div>
          </div>
          <!-- Ingredients -->
          <div class="form-group">
            <label class="form-label">Ingredients</label>
            <div class="ingredient-list">
              <div
                v-for="(ingr, i) in editIngredients"
                :key="i"
                :class="['ingredient-row', ingr.in_pantry ? 'in-pantry' : '']"
              >
                <span v-if="ingr.in_pantry" class="pantry-dot" title="In your pantry" aria-label="In pantry"></span>
                <input
                  v-model="ingr.qty"
                  class="form-input ingr-qty"
                  type="text"
                  placeholder="qty"
                  :aria-label="`Ingredient ${i + 1} quantity`"
                />
                <input
                  v-model="ingr.unit"
                  class="form-input ingr-unit"
                  type="text"
                  placeholder="unit"
                  :aria-label="`Ingredient ${i + 1} unit`"
                />
                <input
                  v-model="ingr.name"
                  class="form-input ingr-name"
                  type="text"
                  placeholder="ingredient"
                  :aria-label="`Ingredient ${i + 1} name`"
                />
                <button
                  class="btn-icon remove-ingr-btn"
                  @click="removeIngredient(i)"
                  :aria-label="`Remove ingredient ${i + 1}`"
                >
                  <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5">
                    <line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
                  </svg>
                </button>
              </div>
            </div>
            <button class="btn btn-ghost btn-sm mt-xs" @click="addIngredient">+ Add ingredient</button>
          </div>
          <!-- Steps -->
          <div class="form-group">
            <label class="form-label">Steps</label>
            <div class="step-list">
              <div v-for="(_step, i) in editSteps" :key="i" class="step-row">
                <span class="step-num">{{ i + 1 }}</span>
                <textarea
                  v-model="editSteps[i]"
                  class="form-input step-textarea"
                  rows="2"
                  :aria-label="`Step ${i + 1}`"
                ></textarea>
                <button
                  class="btn-icon remove-step-btn"
                  @click="removeStep(i)"
                  :aria-label="`Remove step ${i + 1}`"
                >
                  <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5">
                    <line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/>
                  </svg>
                </button>
              </div>
            </div>
            <button class="btn btn-ghost btn-sm mt-xs" @click="addStep">+ Add step</button>
          </div>
          <!-- Notes (optional) -->
          <div class="form-group">
            <label class="form-label" for="scan-notes">Notes <span class="optional-label">(optional)</span></label>
            <textarea id="scan-notes" v-model="editNotes" class="form-input" rows="2" placeholder="Tips, variations, storage..."></textarea>
          </div>
          <!-- Source attribution -->
          <div v-if="extracted.source_note" class="source-note">
            Source: {{ extracted.source_note }}
          </div>
        </div>
        <div v-if="saveError" class="status-badge status-error mt-sm" role="alert">
          {{ saveError }}
        </div>
        <div class="modal-footer">
          <button class="btn btn-secondary" @click="phase = 'upload'">Re-scan</button>
          <button
            class="btn btn-primary"
            :disabled="!editTitle.trim() || saving"
            @click="save"
          >
            {{ saving ? 'Saving...' : 'Save Recipe' }}
          </button>
        </div>
      </div>
    </div>
  </div>
 </template>
 <script setup lang="ts">
 import { ref, computed, onBeforeUnmount } from 'vue'
 import { type ScannedRecipe, type ScannedIngredient, recipeScanAPI } from '@/services/api'
 type Phase = 'upload' | 'processing' | 'review'
 const emit = defineEmits<{
  (e: 'close'): void
  (e: 'saved', recipe: { id: number; title: string }): void
 }>()
 const titleId = 'scan-modal-title'
 // ── Upload state ──────────────────────────────────────────────────────────────
 const phase = ref<Phase>('upload')
 const fileInput = ref<HTMLInputElement | null>(null)
 const selectedFiles = ref<File[]>([])
 const previewUrls = ref<string[]>([])
 const isDragging = ref(false)
 const uploadError = ref('')
 function onDrop(e: DragEvent) {
  isDragging.value = false
  const dt = e.dataTransfer
  if (!dt) return
  addFiles(Array.from(dt.files))
 }
 function onFileChange(e: Event) {
  const input = e.target as HTMLInputElement
  if (!input.files) return
  addFiles(Array.from(input.files))
  // Reset so the same file can be re-selected after removal
  input.value = ''
 }
 function addFiles(incoming: File[]) {
  uploadError.value = ''
  const combined = [...selectedFiles.value, ...incoming]
  if (combined.length > 4) {
    uploadError.value = 'Maximum 4 photos per scan.'
    return
  }
  // Revoke old preview URLs before replacing
  previewUrls.value.forEach((url) => URL.revokeObjectURL(url))
  selectedFiles.value = combined
  previewUrls.value = combined.map((f) => URL.createObjectURL(f))
 }
 function removeFile(index: number) {
  URL.revokeObjectURL(previewUrls.value[index] ?? '')
  selectedFiles.value = selectedFiles.value.filter((_, i) => i !== index)
  previewUrls.value = previewUrls.value.filter((_, i) => i !== index)
 }
 // ── Scan ──────────────────────────────────────────────────────────────────────
 const extracted = ref<ScannedRecipe | null>(null)
 const scanStatusMessage = ref('Uploading photos...')
 async function startScan() {
  if (selectedFiles.value.length === 0) return
  uploadError.value = ''
  scanStatusMessage.value = 'Uploading photos...'
  phase.value = 'processing'
  try {
    const result = await recipeScanAPI.scanStream(
      selectedFiles.value,
      (_status: string, message: string) => { scanStatusMessage.value = message },
    )
    extracted.value = result
    initEditState(result)
    phase.value = 'review'
  } catch (err: unknown) {
    const msg = err instanceof Error ? err.message : String(err)
    uploadError.value = msg.includes('not appear to contain a recipe')
      ? 'This photo does not look like a recipe. Please try a different photo.'
      : msg.includes('No vision backend')
      ? 'Recipe scanning is not available right now. Check your BYOK settings.'
      : `Scan failed: ${msg}`
    phase.value = 'upload'
  }
 }
 // ── Review/edit state ─────────────────────────────────────────────────────────
 const editTitle = ref('')
 const editServings = ref('')
 const editCookTime = ref('')
 const editIngredients = ref<ScannedIngredient[]>([])
 const editSteps = ref<string[]>([])
 const editNotes = ref('')
 function initEditState(r: ScannedRecipe) {
  editTitle.value = r.title ?? ''
  editServings.value = r.servings ?? ''
  editCookTime.value = r.cook_time ?? ''
  editIngredients.value = r.ingredients.map((i) => ({ ...i }))
  editSteps.value = [...r.steps]
  editNotes.value = r.notes ?? ''
 }
 function removeIngredient(i: number) {
  editIngredients.value = editIngredients.value.filter((_, idx) => idx !== i)
 }
 function addIngredient() {
  editIngredients.value = [...editIngredients.value, { name: '', qty: null, unit: null, raw: null, in_pantry: false }]
 }
 function removeStep(i: number) {
  editSteps.value = editSteps.value.filter((_, idx) => idx !== i)
 }
 function addStep() {
  editSteps.value = [...editSteps.value, '']
 }
 // ── Pantry match display ──────────────────────────────────────────────────────
 const pantryCount = computed(() =>
  editIngredients.value.filter((i) => i.in_pantry).length
 )
 const pantryMatchClass = computed(() => {
  const pct = extracted.value?.pantry_match_pct ?? 0
  if (pct >= 80) return 'pantry-high'
  if (pct >= 50) return 'pantry-mid'
  return 'pantry-low'
 })
 // ── Save ──────────────────────────────────────────────────────────────────────
 const saving = ref(false)
 const saveError = ref('')
 async function save() {
  if (!editTitle.value.trim()) return
  saving.value = true
  saveError.value = ''
  try {
    const payload = {
      title: editTitle.value.trim(),
      subtitle: extracted.value?.subtitle ?? null,
      servings: editServings.value || null,
      cook_time: editCookTime.value || null,
      source_note: extracted.value?.source_note ?? null,
      ingredients: editIngredients.value.filter((i) => i.name.trim()),
      steps: editSteps.value.filter((s) => s.trim()),
      notes: editNotes.value.trim() || null,
      tags: extracted.value?.tags ?? [],
      source: 'scan' as const,
    }
    const saved = await recipeScanAPI.saveScanned(payload)
    emit('saved', { id: saved.id, title: saved.title })
    close()
  } catch (err: unknown) {
    saveError.value = err instanceof Error ? err.message : 'Failed to save recipe.'
  } finally {
    saving.value = false
  }
 }
 // ── Cleanup ───────────────────────────────────────────────────────────────────
 function close() {
  previewUrls.value.forEach((url) => URL.revokeObjectURL(url))
  emit('close')
 }
 onBeforeUnmount(() => {
  previewUrls.value.forEach((url) => URL.revokeObjectURL(url))
 })
 </script>
 <style scoped>
 .modal-overlay {
  position: fixed;
  inset: 0;
  background: rgba(0, 0, 0, 0.5);
  display: flex;
  align-items: center;
  justify-content: center;
  z-index: var(--z-modal, 1000);
  padding: var(--spacing-md);
 }
 .modal-panel {
  background: var(--bg-card, #fff);
  border-radius: var(--radius-lg, 12px);
  box-shadow: var(--shadow-xl, 0 20px 60px rgba(0,0,0,0.2));
  width: 100%;
  max-width: 560px;
  max-height: 90vh;
  display: flex;
  flex-direction: column;
  overflow: hidden;
 }
 .modal-header {
  display: flex;
  align-items: center;
  justify-content: space-between;
  padding: var(--spacing-md) var(--spacing-lg);
  border-bottom: 1px solid var(--border-color, #e5e7eb);
  flex-shrink: 0;
 }
 .modal-title {
  font-size: var(--font-lg, 1.125rem);
  font-weight: 600;
  color: var(--text-primary, #111);
  margin: 0;
 }
 .close-btn {
  background: none;
  border: none;
  cursor: pointer;
  padding: 4px;
  color: var(--text-secondary, #6b7280);
  border-radius: var(--radius-sm, 4px);
  display: flex;
  align-items: center;
  justify-content: center;
 }
 .close-btn:hover {
  background: var(--bg-hover, #f3f4f6);
  color: var(--text-primary, #111);
 }
 .modal-body {
  padding: var(--spacing-lg);
  overflow-y: auto;
  flex: 1;
 }
 .modal-footer {
  display: flex;
  justify-content: flex-end;
  gap: var(--spacing-sm);
  padding-top: var(--spacing-md);
  border-top: 1px solid var(--border-color, #e5e7eb);
  margin-top: var(--spacing-md);
 }
 /* ── Upload ── */
 .hint-text {
  color: var(--text-secondary, #6b7280);
  font-size: var(--font-sm, 0.875rem);
  margin-bottom: var(--spacing-md);
  line-height: 1.5;
 }
 .drop-zone {
  border: 2px dashed var(--border-color, #d1d5db);
  border-radius: var(--radius-md, 8px);
  padding: var(--spacing-xl);
  text-align: center;
  cursor: pointer;
  transition: border-color 0.15s, background 0.15s;
  min-height: 160px;
  display: flex;
  align-items: center;
  justify-content: center;
 }
 .drop-zone:hover,
 .drop-zone-active {
  border-color: var(--color-primary, #4f46e5);
  background: var(--bg-hover, #f5f3ff);
 }
 .drop-zone.has-files {
  border-style: solid;
  border-color: var(--color-primary, #4f46e5);
  padding: var(--spacing-md);
 }
 .hidden-input {
  display: none;
 }
 .drop-zone-empty {
  display: flex;
  flex-direction: column;
  align-items: center;
  gap: var(--spacing-xs);
 }
 .camera-icon {
  color: var(--text-secondary, #9ca3af);
  margin-bottom: var(--spacing-xs);
 }
 .drop-zone-label {
  font-weight: 600;
  color: var(--text-primary, #111);
  margin: 0;
 }
 .drop-zone-sub {
  color: var(--text-secondary, #6b7280);
  font-size: var(--font-sm, 0.875rem);
  margin: 0;
 }
 .file-preview-grid {
  display: flex;
  gap: var(--spacing-sm);
  flex-wrap: wrap;
  align-items: center;
  width: 100%;
 }
 .file-preview-item {
  position: relative;
  width: 100px;
 }
 .preview-img {
  width: 100px;
  height: 100px;
  object-fit: cover;
  border-radius: var(--radius-sm, 6px);
  border: 1px solid var(--border-color, #e5e7eb);
 }
 .remove-file-btn {
  position: absolute;
  top: -6px;
  right: -6px;
  background: var(--color-danger, #ef4444);
  color: white;
  border: none;
  border-radius: 50%;
  width: 20px;
  height: 20px;
  display: flex;
  align-items: center;
  justify-content: center;
  cursor: pointer;
  padding: 0;
 }
 .preview-label {
  text-align: center;
  font-size: var(--font-xs, 0.75rem);
  color: var(--text-secondary, #6b7280);
  margin: 4px 0 0;
 }
 .file-preview-add {
  width: 100px;
  height: 100px;
  border: 2px dashed var(--border-color, #d1d5db);
  border-radius: var(--radius-sm, 6px);
  display: flex;
  align-items: center;
  justify-content: center;
  cursor: pointer;
  color: var(--text-secondary, #9ca3af);
  transition: border-color 0.15s;
 }
 .file-preview-add:hover {
  border-color: var(--color-primary, #4f46e5);
  color: var(--color-primary, #4f46e5);
 }
 /* ── Processing ── */
 .processing-body {
  display: flex;
  align-items: center;
  justify-content: center;
  min-height: 200px;
 }
 .scan-spinner {
  display: flex;
  flex-direction: column;
  align-items: center;
  gap: var(--spacing-sm);
 }
 .spin-icon {
  color: var(--color-primary, #4f46e5);
  animation: spin 1.5s linear infinite;
 }
@keyframes spin {
  from { transform: rotate(0deg); }
  to { transform: rotate(360deg); }
 }
 .processing-label {
  font-weight: 600;
  color: var(--text-primary, #111);
  margin: 0;
 }
 .processing-sub {
  color: var(--text-secondary, #6b7280);
  font-size: var(--font-sm, 0.875rem);
  margin: 0;
 }
 /* ── Review ── */
 .review-body {
  padding-bottom: var(--spacing-sm);
 }
 .pantry-match-row {
  display: flex;
  align-items: center;
 }
 .pantry-badge {
  display: inline-block;
  font-size: var(--font-sm, 0.875rem);
  font-weight: 600;
  padding: 3px 10px;
  border-radius: 999px;
 }
 .pantry-high { background: var(--color-success-bg, #d1fae5); color: var(--color-success, #065f46); }
 .pantry-mid  { background: var(--color-info-bg, #dbeafe);    color: var(--color-info, #1e40af); }
 .pantry-low  { background: var(--bg-secondary, #f3f4f6);     color: var(--text-secondary, #374151); }
 .review-form {
  display: flex;
  flex-direction: column;
  gap: var(--spacing-md);
 }
 .form-row-2 {
  display: grid;
  grid-template-columns: 1fr 1fr;
  gap: var(--spacing-sm);
 }
 /* Ingredients */
 .ingredient-list {
  display: flex;
  flex-direction: column;
  gap: 6px;
 }
 .ingredient-row {
  display: flex;
  align-items: center;
  gap: 6px;
 }
 .pantry-dot {
  width: 8px;
  height: 8px;
  border-radius: 50%;
  background: var(--color-success, #10b981);
  flex-shrink: 0;
 }
 .in-pantry {
  background: var(--color-success-bg-faint, #f0fdf4);
  border-radius: var(--radius-sm, 4px);
  padding: 2px 4px;
 }
 .ingr-qty   { width: 60px; flex-shrink: 0; }
 .ingr-unit  { width: 70px; flex-shrink: 0; }
 .ingr-name  { flex: 1; }
 .remove-ingr-btn,
 .remove-step-btn {
  background: none;
  border: none;
  cursor: pointer;
  padding: 4px;
  color: var(--text-secondary, #9ca3af);
  border-radius: var(--radius-sm, 4px);
  display: flex;
  align-items: center;
  justify-content: center;
  flex-shrink: 0;
 }
 .remove-ingr-btn:hover,
 .remove-step-btn:hover {
  background: var(--color-danger-bg, #fee2e2);
  color: var(--color-danger, #ef4444);
 }
 /* Steps */
 .step-list {
  display: flex;
  flex-direction: column;
  gap: 8px;
 }
 .step-row {
  display: flex;
  align-items: flex-start;
  gap: 8px;
 }
 .step-num {
  width: 24px;
  height: 24px;
  border-radius: 50%;
  background: var(--bg-secondary, #f3f4f6);
  color: var(--text-secondary, #374151);
  font-size: var(--font-xs, 0.75rem);
  font-weight: 700;
  display: flex;
  align-items: center;
  justify-content: center;
  flex-shrink: 0;
  margin-top: 8px;
 }
 .step-textarea {
  flex: 1;
  resize: vertical;
  min-height: 60px;
 }
 /* Source */
 .source-note {
  font-size: var(--font-xs, 0.75rem);
  color: var(--text-secondary, #9ca3af);
  text-align: right;
  font-style: italic;
 }
 .optional-label {
  color: var(--text-secondary, #9ca3af);
  font-weight: normal;
  font-size: var(--font-xs, 0.75rem);
 }
 .warning-list {
  margin: 4px 0 0;
  padding-left: 16px;
  font-size: var(--font-sm, 0.875rem);
 }
 .btn-ghost {
  background: none;
  border: none;
  cursor: pointer;
  color: var(--color-primary, #4f46e5);
  padding: 4px 8px;
  font-size: var(--font-sm, 0.875rem);
  border-radius: var(--radius-sm, 4px);
 }
 .btn-ghost:hover {
  background: var(--bg-hover, #f5f3ff);
 }
 .btn-sm {
  padding: 4px 10px;
  font-size: var(--font-sm, 0.875rem);
 }
 .mt-xs { margin-top: var(--spacing-xs, 4px); }
 .mt-sm { margin-top: var(--spacing-sm, 8px); }
 .mb-sm { margin-bottom: var(--spacing-sm, 8px); }
@media (max-width: 480px) {
  .form-row-2 {
    grid-template-columns: 1fr;
  }
  .modal-panel {
    border-radius: var(--radius-md, 8px);
    max-height: 95vh;
  }
 }
 </style>
--- a/frontend/src/components/RecipesView.vue
+++ b/frontend/src/components/RecipesView.vue
--- a/frontend/src/components/SaveRecipeModal.vue
+++ b/frontend/src/components/SaveRecipeModal.vue
@ -46,14 +46,7 @@
        <!-- Style tags -->
        <div class="form-group">
-          <div class="flex-between mb-xs">
+          <label class="form-label">Style tags</label>
            <label class="form-label" style="margin-bottom: 0;">Style tags</label>
            <button
              class="btn btn-secondary btn-xs"
              :disabled="classifying"
              @click="suggestTags"
            >{{ classifying ? 'Suggesting…' : 'Suggest tags' }}</button>
          </div>
          <div class="tags-wrap flex flex-wrap gap-xs mb-xs">
            <span
              v-for="tag in localTags"
@ -96,7 +89,6 @@
 <script setup lang="ts">
 import { ref, computed, onMounted, onUnmounted, nextTick } from 'vue'
 import { useSavedRecipesStore } from '../stores/savedRecipes'
 import { savedRecipesAPI } from '../services/api'
 const SUGGESTED_TAGS = [
  'comforting', 'light', 'spicy', 'umami', 'sweet', 'savory', 'rich',
@ -148,7 +140,6 @@ const localTags = ref<string[]>([...(existing.value?.style_tags ?? [])])
 const hoverRating = ref<number | null>(null)
 const tagInput = ref('')
 const saving = ref(false)
 const classifying = ref(false)
 const unusedSuggestions = computed(() =>
  SUGGESTED_TAGS.filter((s) => !localTags.value.includes(s))
@ -183,23 +174,6 @@ function onTagKey(e: KeyboardEvent) {
  }
 }
 async function suggestTags() {
  classifying.value = true
  try {
    const suggestions = await savedRecipesAPI.classifyStyle(props.recipeId)
    // Merge suggestions into localTags — new ones only, preserving user's existing tags
    for (const tag of suggestions) {
      if (!localTags.value.includes(tag)) {
        localTags.value = [...localTags.value, tag]
      }
    }
  } catch {
    // Silently ignore — tier gate returns 403, no LLM returns empty list
  } finally {
    classifying.value = false
  }
 }
 async function submit() {
  saving.value = true
  try {
--- a/frontend/src/components/SavedRecipesPanel.vue
+++ b/frontend/src/components/SavedRecipesPanel.vue
@ -32,7 +32,6 @@
            <option value="saved_at">Recently saved</option>
            <option value="rating">Highest rated</option>
            <option value="title">A–Z</option>
            <option value="last_cooked">Last cooked</option>
          </select>
        </div>
@ -47,7 +46,7 @@
      <!-- Recipe cards -->
      <div class="saved-list flex-col gap-sm">
        <div
-          v-for="recipe in sortedSaved"
+          v-for="recipe in store.saved"
          :key="recipe.id"
          class="card-sm saved-card"
          :class="{ 'card-success': recipe.rating !== null && recipe.rating >= 4 }"
@ -80,8 +79,8 @@
            >{{ tag }}</span>
          </div>
-          <!-- Last cooked chip (orbital cadence: neutral, no urgency) -->
+          <!-- Last cooked hint -->
-          <div v-if="lastCookedLabel(recipe.recipe_id)" class="last-cooked-chip text-xs mt-xs">
+          <div v-if="lastCookedLabel(recipe.recipe_id)" class="last-cooked-hint text-xs text-muted mt-xs">
            {{ lastCookedLabel(recipe.recipe_id) }}
          </div>
@ -166,32 +165,20 @@ const recipesStore = useRecipesStore()
 const editingRecipe = ref<SavedRecipe | null>(null)
 function lastCookedLabel(recipeId: number): string | null {
-  const days = recipesStore.lastCookedDaysAgo(recipeId)
+  const entries = recipesStore.cookLog.filter((e) => e.id === recipeId)
-  if (days === null) return null
+  if (entries.length === 0) return null
-  if (days === 0) return 'made today'
+  const latestMs = Math.max(...entries.map((e) => e.cookedAt))
-  if (days === 1) return 'made yesterday'
+  const diffMs = Date.now() - latestMs
-  if (days < 7) return `made ${days} days ago`
+  const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24))
-  if (days < 14) return 'made 1 week ago'
+  if (diffDays === 0) return 'Last made: today'
-  const weeks = Math.floor(days / 7)
+  if (diffDays === 1) return 'Last made: yesterday'
-  if (days < 60) return `made ${weeks} weeks ago`
+  if (diffDays < 7) return `Last made: ${diffDays} days ago`
-  const months = Math.floor(days / 30)
+  if (diffDays < 14) return 'Last made: 1 week ago'
-  return `made ${months} month${months !== 1 ? 's' : ''} ago`
+  const diffWeeks = Math.floor(diffDays / 7)
  if (diffDays < 60) return `Last made: ${diffWeeks} weeks ago`
  const diffMonths = Math.floor(diffDays / 30)
  return `Last made: ${diffMonths} month${diffMonths !== 1 ? 's' : ''} ago`
 }
 // Client-side last_cooked sort — resolves from localStorage cook log so no API change needed.
 // Recipes with a cook date surface oldest-first (natural "due for a revisit" order without
 // framing it that way). Recipes never cooked sort to the end.
 const sortedSaved = computed(() => {
  if (store.sortBy !== 'last_cooked') return store.saved
  return [...store.saved].sort((a, b) => {
    const daysA = recipesStore.lastCookedDaysAgo(a.recipe_id)
    const daysB = recipesStore.lastCookedDaysAgo(b.recipe_id)
    if (daysA === null && daysB === null) return 0
    if (daysA === null) return 1   // never cooked → end
    if (daysB === null) return -1  // never cooked → end
    return daysB - daysA           // oldest cooked first (largest days value first)
  })
 })
 const showNewCollection = ref(false)
 // #44: two-step remove confirmation
@ -376,14 +363,9 @@ async function createCollection() {
  padding: var(--spacing-xl);
 }
-.last-cooked-chip {
+.last-cooked-hint {
-  display: inline-block;
+  font-style: italic;
-  color: var(--color-text-muted, var(--color-secondary, #888));
+  opacity: 0.75;
  background: var(--color-surface-subtle, transparent);
  border-radius: var(--radius-sm, 4px);
  padding: 0 var(--spacing-xs, 4px);
  font-style: normal;
  opacity: 0.8;
 }
 .modal-overlay {
--- a/frontend/src/components/SettingsView.vue
+++ b/frontend/src/components/SettingsView.vue
@ -2,7 +2,6 @@
  <div class="settings-view">
    <div class="card">
      <h2 class="section-title text-xl mb-md">Settings</h2>
      <p class="text-xs text-muted mb-md">Changes save automatically.</p>
      <!-- Cooking Equipment -->
      <section>
@ -20,7 +19,7 @@
            class="tag-chip status-badge status-info"
          >
            {{ item }}
-            <button class="chip-remove" @click="removeEquipment(item)" :aria-label="'Remove equipment: ' + item">×</button>
+            <button class="chip-remove" @click="removeEquipment(item)" aria-label="Remove">×</button>
          </span>
        </div>
@ -51,78 +50,18 @@
          </div>
        </div>
-      </section>
+        <!-- Save button -->
-
+        <div class="flex-start gap-sm">
      <!-- Sensory Preferences -->
      <section class="mt-md">
        <h3 class="text-lg font-semibold mb-xs">Sensory Preferences</h3>
        <p class="text-sm text-secondary mb-md">
          Tell Kiwi what your senses prefer. Recipes that don't match will be
          filtered out quietly in Browse and Find. Leave everything unset and nothing is filtered.
        </p>
        <!-- Texture avoid pills -->
        <div class="form-group">
          <label class="form-label">
            <span class="mr-xs">Texture — avoid</span>
            <span class="text-xs text-muted">(select any textures you'd rather skip)</span>
          </label>
          <div class="flex flex-wrap gap-xs mt-xs" role="group" aria-label="Texture avoidance">
          <button
-              v-for="tex in TEXTURE_OPTIONS"
+            class="btn btn-primary"
-              :key="tex.tag"
+            :disabled="settingsStore.loading"
-              :class="[
+            @click="settingsStore.save()"
-                'sensory-pill',
+          >
-                settingsStore.sensoryPreferences.avoid_textures.includes(tex.tag)
+            <span v-if="settingsStore.loading">Saving…</span>
-                  ? 'sensory-pill--avoided'
+            <span v-else-if="settingsStore.saved">✓ Saved!</span>
-                  : 'sensory-pill--neutral',
+            <span v-else>Save Settings</span>
-              ]"
+          </button>
              :aria-pressed="settingsStore.sensoryPreferences.avoid_textures.includes(tex.tag)"
              @click="toggleTexture(tex.tag)"
            >{{ tex.emoji }} {{ tex.label }}</button>
        </div>
        </div>
        <!-- Smell tolerance -->
        <div class="form-group mt-sm">
          <label class="form-label">
            <span class="mr-xs">Smell — max I'm ok with</span>
            <span class="text-xs text-muted">(tap to set your limit; tap again to clear)</span>
          </label>
          <div class="flex flex-wrap gap-xs mt-xs" role="group" aria-label="Smell tolerance">
            <button
              v-for="(level, idx) in SMELL_LEVELS"
              :key="String(level.value)"
              :class="['sensory-pill', getSmellClass(level.value, idx)]"
              :aria-pressed="settingsStore.sensoryPreferences.max_smell === level.value"
              @click="toggleSmell(level.value)"
            >{{ level.emoji }} {{ level.label }}</button>
          </div>
          <p v-if="settingsStore.sensoryPreferences.max_smell" class="text-xs text-muted mt-xs">
            Recipes stronger than <strong>{{ smellLabel(settingsStore.sensoryPreferences.max_smell) }}</strong> will be hidden.
          </p>
        </div>
        <!-- Noise tolerance -->
        <div class="form-group mt-sm">
          <label class="form-label">
            <span class="mr-xs">Noise — max I'm ok with</span>
            <span class="text-xs text-muted">(tap to set your limit; tap again to clear)</span>
          </label>
          <div class="flex flex-wrap gap-xs mt-xs" role="group" aria-label="Noise tolerance">
            <button
              v-for="(level, idx) in NOISE_LEVELS"
              :key="String(level.value)"
              :class="['sensory-pill', getNoiseClass(level.value, idx)]"
              :aria-pressed="settingsStore.sensoryPreferences.max_noise === level.value"
              @click="toggleNoise(level.value)"
            >{{ level.emoji }} {{ level.label }}</button>
          </div>
          <p v-if="settingsStore.sensoryPreferences.max_noise" class="text-xs text-muted mt-xs">
            Recipes louder than <strong>{{ noiseLabel(settingsStore.sensoryPreferences.max_noise) }}</strong> will be hidden.
          </p>
        </div>
      </section>
      <!-- Units -->
@ -147,93 +86,19 @@
            Imperial (oz, cups, °F)
          </button>
        </div>
-      </section>
+        <div class="flex-start gap-sm">
-
+          <button
-      <!-- Shopping Locale -->
+            class="btn btn-primary btn-sm"
-      <section class="mt-md">
+            :disabled="settingsStore.loading"
-        <h3 class="text-lg font-semibold mb-xs">Shopping Region</h3>
+            @click="settingsStore.save()"
        <p class="text-sm text-secondary mb-sm">
          Sets your Amazon storefront and which retailers appear in shopping links.
          Instacart and Walmart are US/CA only — other regions get Amazon.
        </p>
        <select
          class="form-input"
          v-model="settingsStore.shoppingLocale"
          aria-label="Shopping region"
          style="max-width: 20rem;"
          >
-          <optgroup label="North America">
+            <span v-if="settingsStore.loading">Saving…</span>
-            <option value="us">United States (USD $)</option>
+            <span v-else-if="settingsStore.saved">✓ Saved!</span>
-            <option value="ca">Canada (CAD CA$)</option>
+            <span v-else>Save</span>
-            <option value="mx">Mexico (MXN MX$)</option>
+          </button>
          </optgroup>
          <optgroup label="Europe">
            <option value="gb">United Kingdom (GBP £)</option>
            <option value="de">Germany (EUR €)</option>
            <option value="fr">France (EUR €)</option>
            <option value="it">Italy (EUR €)</option>
            <option value="es">Spain (EUR €)</option>
            <option value="nl">Netherlands (EUR €)</option>
            <option value="se">Sweden (SEK kr)</option>
          </optgroup>
          <optgroup label="Asia Pacific">
            <option value="au">Australia (AUD A$)</option>
            <option value="nz">New Zealand (NZD NZ$) — via Amazon AU</option>
            <option value="jp">Japan (JPY ¥)</option>
            <option value="in">India (INR ₹)</option>
            <option value="sg">Singapore (SGD S$)</option>
          </optgroup>
          <optgroup label="South America">
            <option value="br">Brazil (BRL R$)</option>
          </optgroup>
        </select>
      </section>
      <!-- Time-First Layout -->
      <section class="mt-md">
        <h3 class="text-lg font-semibold mb-xs">Recipe Search Layout</h3>
        <p class="text-sm text-secondary mb-sm">
          Choose how the Find tab looks when you search for recipes.
        </p>
        <div class="flex flex-col gap-xs" role="radiogroup" aria-label="Recipe search layout">
          <label
            v-for="opt in timeFirstLayoutOptions"
            :key="opt.value"
            class="flex-start gap-sm time-layout-option"
          >
            <input
              type="radio"
              name="time_first_layout"
              :value="opt.value"
              :checked="settingsStore.timeFirstLayout === opt.value"
              @change="settingsStore.timeFirstLayout = opt.value"
            />
            <span>
              <strong>{{ opt.label }}</strong>
              <span class="text-xs text-muted ml-xs">{{ opt.description }}</span>
            </span>
          </label>
        </div>
      </section>
      <!-- Data Sharing (cloud only) -->
      <section v-if="isCloudMode" class="mt-md">
        <h3 class="text-lg font-semibold mb-xs">Data Sharing</h3>
        <label class="data-sharing-toggle flex-start gap-sm text-sm">
          <input
            type="checkbox"
            :checked="magpieOptIn"
            @change="setMagpieOptIn(($event.target as HTMLInputElement).checked)"
          />
          Share anonymized recipe ratings to help improve suggestions
        </label>
        <p class="text-xs text-muted mt-xs">
          When enabled, Kiwi sends the recipe source ID, your star rating, and
          style tags to CircuitForge. No personal information or pantry contents
          are included.
        </p>
      </section>
      <!-- Display Preferences -->
      <section class="mt-md">
        <h3 class="text-lg font-semibold mb-xs">Display</h3>
@ -338,50 +203,19 @@
      </template>
    </div>
  </div>
  <Transition name="autosave-fade">
    <div v-if="settingsStore.saved" class="autosave-toast" role="status" aria-live="polite">
      ✓ Saved
    </div>
  </Transition>
 </template>
 <script setup lang="ts">
 import { ref, computed, onMounted } from 'vue'
 import { useSettingsStore } from '../stores/settings'
 import { useRecipesStore } from '../stores/recipes'
-import { householdAPI, settingsAPI, type HouseholdStatus } from '../services/api'
+import { householdAPI, type HouseholdStatus } from '../services/api'
 import type { TextureTag, SmellLevel, NoiseLevel } from '../services/api'
 import type { TimeFirstLayout } from '../stores/settings'
 import { useOrchUsage } from '../composables/useOrchUsage'
 const settingsStore = useSettingsStore()
 const recipesStore = useRecipesStore()
 const { enabled: orchPillEnabled, setEnabled: setOrchPillEnabled } = useOrchUsage()
 // Cloud mode — baked in at build time via VITE_CLOUD_MODE=true in cloud builds
 const isCloudMode = import.meta.env.VITE_CLOUD_MODE === 'true'
 // Data sharing — magpie opt-in (cloud mode only)
 const magpieOptIn = ref(false)
 async function loadMagpieOptIn(): Promise<void> {
  if (!isCloudMode) return
  const value = await settingsAPI.getSetting('magpie_opt_in')
  magpieOptIn.value = value === 'true'
 }
 async function setMagpieOptIn(enabled: boolean): Promise<void> {
  magpieOptIn.value = enabled
  await settingsAPI.setSetting('magpie_opt_in', enabled ? 'true' : 'false')
 }
 const timeFirstLayoutOptions: Array<{ value: TimeFirstLayout; label: string; description: string }> = [
  { value: 'auto',       label: 'Auto',        description: 'Shows a time selector when recipes are available.' },
  { value: 'time_first', label: 'Time First',  description: 'Always show the time bucket selector at the top.' },
  { value: 'normal',     label: 'Normal',      description: 'Standard layout — no time selector shown.' },
 ]
 const sortedCookLog = computed(() =>
  [...recipesStore.cookLog].sort((a, b) => b.cookedAt - a.cookedAt)
 )
@ -525,86 +359,7 @@ async function handleRemoveMember(userId: string) {
 onMounted(async () => {
  await settingsStore.load()
  await loadHouseholdStatus()
  await loadMagpieOptIn()
 })
 // ── Sensory taxonomy ───────────────────────────────────────────────────────
 const TEXTURE_OPTIONS: { tag: TextureTag; label: string; emoji: string }[] = [
  { tag: 'mushy',   label: 'Mushy',   emoji: '🦫' },
  { tag: 'slimy',   label: 'Slimy',   emoji: '🫙' },
  { tag: 'crunchy', label: 'Crunchy', emoji: '🥜' },
  { tag: 'chewy',   label: 'Chewy',   emoji: '🍖' },
  { tag: 'creamy',  label: 'Creamy',  emoji: '🥣' },
  { tag: 'chunky',  label: 'Chunky',  emoji: '🫕' },
 ]
 const SMELL_LEVELS: { value: SmellLevel; label: string; emoji: string }[] = [
  { value: 'mild',      label: 'Mild',      emoji: '🌿' },
  { value: 'aromatic',  label: 'Aromatic',  emoji: '🌸' },
  { value: 'pungent',   label: 'Pungent',   emoji: '🧄' },
  { value: 'fermented', label: 'Fermented', emoji: '🧀' },
 ]
 const NOISE_LEVELS: { value: NoiseLevel; label: string; emoji: string }[] = [
  { value: 'quiet',    label: 'Quiet',    emoji: '🤫' },
  { value: 'moderate', label: 'Moderate', emoji: '🍳' },
  { value: 'loud',     label: 'Loud',     emoji: '🔥' },
  { value: 'very_loud', label: 'Very loud', emoji: '💥' },
 ]
 function smellLabel(value: SmellLevel): string {
  return SMELL_LEVELS.find(l => l.value === value)?.label ?? ''
 }
 function noiseLabel(value: NoiseLevel): string {
  return NOISE_LEVELS.find(l => l.value === value)?.label ?? ''
 }
 function toggleTexture(tag: TextureTag) {
  const current = settingsStore.sensoryPreferences.avoid_textures
  const updated = current.includes(tag)
    ? current.filter(t => t !== tag)
    : [...current, tag]
  settingsStore.sensoryPreferences = {
    ...settingsStore.sensoryPreferences,
    avoid_textures: updated,
  }
 }
 function toggleSmell(value: SmellLevel) {
  const current = settingsStore.sensoryPreferences.max_smell
  settingsStore.sensoryPreferences = {
    ...settingsStore.sensoryPreferences,
    max_smell: current === value ? null : value,
  }
 }
 function toggleNoise(value: NoiseLevel) {
  const current = settingsStore.sensoryPreferences.max_noise
  settingsStore.sensoryPreferences = {
    ...settingsStore.sensoryPreferences,
    max_noise: current === value ? null : value,
  }
 }
 function getSmellClass(_value: SmellLevel, idx: number): string {
  const maxSmell = settingsStore.sensoryPreferences.max_smell
  if (!maxSmell) return 'sensory-pill--neutral'
  const maxIdx = SMELL_LEVELS.findIndex(l => l.value === maxSmell)
  if (idx === maxIdx) return 'sensory-pill--limit'
  if (idx < maxIdx) return 'sensory-pill--ok'
  return 'sensory-pill--neutral'
 }
 function getNoiseClass(_value: NoiseLevel, idx: number): string {
  const maxNoise = settingsStore.sensoryPreferences.max_noise
  if (!maxNoise) return 'sensory-pill--neutral'
  const maxIdx = NOISE_LEVELS.findIndex(l => l.value === maxNoise)
  if (idx === maxIdx) return 'sensory-pill--limit'
  if (idx < maxIdx) return 'sensory-pill--ok'
  return 'sensory-pill--neutral'
 }
 </script>
 <style scoped>
@ -749,105 +504,16 @@ function getNoiseClass(_value: NoiseLevel, idx: number): string {
  color: var(--color-text-muted);
 }
-.orch-pill-toggle,
+.orch-pill-toggle {
 .data-sharing-toggle {
  cursor: pointer;
  align-items: center;
  color: var(--color-text);
 }
-.orch-pill-toggle input[type="checkbox"],
+.orch-pill-toggle input[type="checkbox"] {
 .data-sharing-toggle input[type="checkbox"] {
  accent-color: var(--color-primary);
  width: 1rem;
  height: 1rem;
  flex-shrink: 0;
 }
 /* ── Time-first layout option ────────────────────────────────────────────── */
 .time-layout-option {
  cursor: pointer;
  padding: var(--spacing-xs, 0.25rem) 0;
  align-items: flex-start;
 }
 .time-layout-option input[type="radio"] {
  accent-color: var(--color-primary);
  margin-top: 0.15rem;
  flex-shrink: 0;
 }
 /* ── Sensory pills ───────────────────────────────────────────────────────── */
 .sensory-pill {
  display: inline-flex;
  align-items: center;
  gap: 0.25rem;
  padding: 0.3rem 0.75rem;
  border-radius: 9999px;
  border: 1.5px solid var(--color-border, #e0e0e0);
  background: transparent;
  color: var(--color-text-secondary, #888);
  font-size: 0.85rem;
  cursor: pointer;
  transition: background 0.15s, border-color 0.15s, color 0.15s;
  user-select: none;
 }
 .sensory-pill:hover {
  opacity: 0.85;
 }
 .sensory-pill--avoided {
  background: rgba(220, 80, 60, 0.18);
  border-color: rgba(220, 80, 60, 0.40);
  color: #f08070;
 }
 .sensory-pill--ok {
  background: rgba(74, 140, 64, 0.15);
  border-color: rgba(74, 140, 64, 0.35);
  color: #7fc073;
 }
 .sensory-pill--limit {
  background: rgba(200, 140, 30, 0.18);
  border-color: rgba(200, 140, 30, 0.45);
  color: #c8a020;
 }
 .sensory-pill--neutral {
  background: transparent;
  border-color: var(--color-border, #e0e0e0);
  color: var(--color-text-secondary, #888);
 }
 /* ── Autosave toast ──────────────────────────────────────────────────────── */
 .autosave-toast {
  position: fixed;
  bottom: 1.5rem;
  right: 1.5rem;
  background: var(--color-surface, #fff);
  border: 1px solid var(--color-border, #e0e0e0);
  border-radius: var(--radius-md, 0.5rem);
  padding: 0.4rem 0.9rem;
  font-size: var(--font-size-sm);
  color: var(--color-success, #4a8c40);
  box-shadow: 0 2px 8px rgba(0, 0, 0, 0.12);
  z-index: 500;
  pointer-events: none;
 }
 .autosave-fade-enter-active,
 .autosave-fade-leave-active {
  transition: opacity 0.25s ease, transform 0.25s ease;
 }
 .autosave-fade-enter-from,
 .autosave-fade-leave-to {
  opacity: 0;
  transform: translateY(0.5rem);
 }
 </style>
--- a/frontend/src/services/api.ts
+++ b/frontend/src/services/api.ts
@ -111,50 +111,9 @@ export interface BarcodeScanResult {
  inventory_item: InventoryItem | null
  added_to_inventory: boolean
  needs_manual_entry: boolean
  needs_visual_capture: boolean
  message: string
 }
 export interface LabelCaptureResult {
  barcode: string
  product_name: string | null
  brand: string | null
  serving_size_g: number | null
  calories: number | null
  fat_g: number | null
  saturated_fat_g: number | null
  carbs_g: number | null
  sugar_g: number | null
  fiber_g: number | null
  protein_g: number | null
  sodium_mg: number | null
  ingredient_names: string[]
  allergens: string[]
  confidence: number
  needs_review: boolean
 }
 export interface LabelConfirmRequest {
  barcode: string
  product_name?: string | null
  brand?: string | null
  serving_size_g?: number | null
  calories?: number | null
  fat_g?: number | null
  saturated_fat_g?: number | null
  carbs_g?: number | null
  sugar_g?: number | null
  fiber_g?: number | null
  protein_g?: number | null
  sodium_mg?: number | null
  ingredient_names?: string[]
  allergens?: string[]
  confidence?: number
  location?: string
  quantity?: number
  auto_add?: boolean
 }
 export interface BarcodeScanResponse {
  success: boolean
  barcodes_found: number
@ -385,32 +344,6 @@ export const inventoryAPI = {
    })
    return response.data
  },
  /**
   * Upload a nutrition label photo for an unenriched barcode (paid tier).
   * Returns extracted fields + confidence score for user review.
   */
  async captureLabelPhoto(
    file: File,
    barcode: string
  ): Promise<LabelCaptureResult> {
    const formData = new FormData()
    formData.append('file', file)
    formData.append('barcode', barcode)
    const response = await api.post('/inventory/scan/label-capture', formData, {
      headers: { 'Content-Type': 'multipart/form-data' },
      timeout: 60000, // vision inference can take ~5–10s
    })
    return response.data
  },
  /**
   * Confirm a user-reviewed label extraction and save to the local cache.
   */
  async confirmLabelCapture(data: LabelConfirmRequest): Promise<{ ok: boolean; product_id?: number; inventory_item_id?: number; message: string }> {
    const response = await api.post('/inventory/scan/label-confirm', data)
    return response.data
  },
 }
 // ========== Receipts API ==========
@ -567,7 +500,6 @@ export interface RecipeSuggestion {
  source_url: string | null
  complexity: 'easy' | 'moderate' | 'involved' | null
  estimated_time_min: number | null
  time_effort: TimeEffortProfile | null
 }
 export interface NutritionFilters {
@ -592,24 +524,8 @@ export interface RecipeResult {
  rate_limit_count: number
 }
 export interface StreamTokenResponse {
  stream_url: string
  token: string
  expires_in_s: number
 }
 export type RecipeJobStatusValue = 'queued' | 'running' | 'done' | 'failed'
 export interface RecipeJobStatus {
  job_id: string
  status: RecipeJobStatusValue
  result: RecipeResult | null
  error: string | null
 }
 export interface RecipeRequest {
  pantry_items: string[]
  secondary_pantry_items: Record<string, string>
  level: number
  constraints: string[]
  allergies: string[]
@ -621,13 +537,10 @@ export interface RecipeRequest {
  wildcard_confirmed: boolean
  nutrition_filters: NutritionFilters
  excluded_ids: number[]
  exclude_ingredients: string[]
  shopping_mode: boolean
  pantry_match_only: boolean
  complexity_filter: string | null
  max_time_min: number | null
  max_total_min: number | null
  max_active_min: number | null
 }
 export interface Staple {
@ -671,21 +584,6 @@ export interface BuildRequest {
  role_overrides: Record<string, string>
 }
 // ── Ask/RAG types ──────────────────────────────────────────────────────────
 export interface AskRecipeHit {
  id: number
  title: string
  match_pct: number | null
  category: string | null
 }
 export interface AskResponse {
  answer: string | null
  recipes: AskRecipeHit[]
  tier: string
 }
 // ========== Recipes API ==========
 export const recipesAPI = {
@ -694,26 +592,10 @@ export const recipesAPI = {
    const response = await api.post('/recipes/suggest', req, { timeout: 120000 })
    return response.data
  },
  /** Submit an async job for L3/L4 generation. Returns job_id + initial status. */
  async suggestAsync(req: RecipeRequest): Promise<{ job_id: string; status: string }> {
    const response = await api.post('/recipes/suggest', req, { params: { async: 'true' }, timeout: 15000 })
    return response.data
  },
  /** Poll an async job. Returns the full status including result once done. */
  async pollJob(jobId: string): Promise<RecipeJobStatus> {
    const response = await api.get(`/recipes/jobs/${jobId}`, { timeout: 10000 })
    return response.data
  },
  async getRecipe(id: number): Promise<RecipeSuggestion> {
    const response = await api.get(`/recipes/${id}`)
    return response.data
  },
  async getLeftovers(id: number): Promise<{ fridge_days: number; freeze_days: number | null; freeze_by_day: number | null; storage_advice: string }> {
    const response = await api.post(`/recipes/${id}/leftovers`)
    return response.data
  },
  async listStaples(dietary?: string): Promise<Staple[]> {
    const response = await api.get('/staples/', { params: dietary ? { dietary } : undefined })
    return response.data
@ -740,72 +622,6 @@ export const recipesAPI = {
    const response = await api.post('/recipes/build', req)
    return response.data
  },
  /** Issue a one-time stream token for LLM recipe generation (Paid tier / BYOK only). */
  async getRecipeStreamToken(params: {
    level: 3 | 4
    wildcard_confirmed?: boolean
  }): Promise<StreamTokenResponse> {
    const response = await api.post('/recipes/stream-token', {
      level: params.level,
      wildcard_confirmed: params.wildcard_confirmed ?? false,
    })
    return response.data
  },
  /** Natural-language recipe search with optional LLM synthesis (Paid tier). */
  async ask(question: string, pantryItems: string[] = []): Promise<AskResponse> {
    const response = await api.post('/recipes/ask', { question, pantry_items: pantryItems }, { timeout: 30000 })
    return response.data
  },
  /** Stream a recipe via native SSE (Ollama fallback). Calls callbacks as tokens arrive. */
  async suggestRecipeStream(
    req: RecipeRequest,
    onChunk: (chunk: string) => void,
    onDone: () => void,
    onError: (err: string) => void,
  ): Promise<void> {
    const baseUrl = (api.defaults.baseURL ?? '') as string
    let response: Response
    try {
      response = await fetch(`${baseUrl}/recipes/suggest?stream=true`, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify(req),
      })
    } catch (err: unknown) {
      onError(err instanceof Error ? err.message : 'Network error')
      return
    }
    if (!response.ok) {
      onError(`HTTP ${response.status}`)
      return
    }
    const reader = response.body?.getReader()
    if (!reader) { onError('No response body'); return }
    const decoder = new TextDecoder()
    let buffer = ''
    while (true) {
      const { done, value } = await reader.read()
      if (done) { onDone(); break }
      buffer += decoder.decode(value, { stream: true })
      const parts = buffer.split('\n\n')
      buffer = parts.pop() ?? ''
      for (const part of parts) {
        if (!part.startsWith('data: ')) continue
        try {
          const data = JSON.parse(part.slice(6))
          if (data.done) { onDone(); return }
          else if (data.error) { onError(data.error); return }
          else if (data.chunk) { onChunk(data.chunk) }
        } catch { /* ignore malformed events */ }
      }
    }
  },
 }
 // ========== Settings API ==========
@ -931,10 +747,6 @@ export const savedRecipesAPI = {
  async removeFromCollection(collection_id: number, saved_recipe_id: number): Promise<void> {
    await api.delete(`/recipes/saved/collections/${collection_id}/members/${saved_recipe_id}`)
  },
  async classifyStyle(recipe_id: number): Promise<string[]> {
    const response = await api.post(`/recipes/saved/${recipe_id}/classify-style`)
    return response.data.suggested_tags
  },
 }
 // --- Meal Plan types ---
@ -1068,28 +880,6 @@ export interface BrowserDomain {
 export interface BrowserCategory {
  category: string
  recipe_count: number
  has_subcategories: boolean
 }
 export interface BrowserSubcategory {
  subcategory: string
  recipe_count: number
 }
 // ── Time & Effort types ───────────────────────────────────────────────────
 export interface StepAnalysis {
  is_passive: boolean
  detected_minutes: number | null
 }
 export interface TimeEffortProfile {
  active_min: number
  passive_min: number
  total_min: number
  effort_label: 'quick' | 'moderate' | 'involved'
  equipment: string[]
  step_analyses: StepAnalysis[]
 }
 export interface BrowserRecipe {
@ -1097,8 +887,6 @@ export interface BrowserRecipe {
  title: string
  category: string | null
  match_pct: number | null
  active_min: number | null
  passive_min: number | null
 }
 export interface BrowserResult {
@ -1118,46 +906,14 @@ export const browserAPI = {
    const response = await api.get(`/recipes/browse/${domain}`)
    return response.data
  },
  async listSubcategories(domain: string, category: string): Promise<BrowserSubcategory[]> {
    const response = await api.get(
      `/recipes/browse/${domain}/${encodeURIComponent(category)}/subcategories`
    )
    return response.data
  },
  async browse(domain: string, category: string, params?: {
    page?: number
    page_size?: number
    pantry_items?: string
    subcategory?: string
    q?: string
    sort?: string
    required_ingredient?: string
  }): Promise<BrowserResult> {
    const response = await api.get(`/recipes/browse/${domain}/${encodeURIComponent(category)}`, { params })
    return response.data
  },
  async submitRecipeTag(body: {
    recipe_id: number
    domain: string
    category: string
    subcategory: string | null
    pseudonym: string
  }): Promise<void> {
    await api.post('/recipes/community-tags', body)
  },
  async upvoteRecipeTag(tagId: number, pseudonym: string): Promise<void> {
    await api.post(`/recipes/community-tags/${tagId}/upvote`, null, { params: { pseudonym } })
  },
  async listRecipeTags(recipeId: number): Promise<Array<{
    id: number; domain: string; category: string; subcategory: string | null;
    pseudonym: string; upvotes: number; accepted: boolean
  }>> {
    const response = await api.get(`/recipes/community-tags/${recipeId}`)
    return response.data
  },
 }
 // ── Shopping List ─────────────────────────────────────────────────────────────
@ -1256,145 +1012,4 @@ export async function bootstrapSession(): Promise<SessionInfo | null> {
  }
 }
 // ========== Sensory Preferences Types ==========
 export type TextureTag = 'mushy' | 'slimy' | 'crunchy' | 'chewy' | 'creamy' | 'chunky'
 export type SmellLevel = 'mild' | 'aromatic' | 'pungent' | 'fermented' | null
 export type NoiseLevel = 'quiet' | 'moderate' | 'loud' | 'very_loud' | null
 export interface SensoryPreferences {
  avoid_textures: TextureTag[]
  max_smell: SmellLevel
  max_noise: NoiseLevel
 }
 export const DEFAULT_SENSORY_PREFERENCES: SensoryPreferences = {
  avoid_textures: [],
  max_smell: null,
  max_noise: null,
 }
 // ── Recipe Scanner (kiwi#9) ───────────────────────────────────────────────────
 export interface ScannedIngredient {
  name: string
  qty: string | null
  unit: string | null
  raw: string | null
  in_pantry: boolean
 }
 export interface ScannedRecipe {
  title: string | null
  subtitle: string | null
  servings: string | null
  cook_time: string | null
  source_note: string | null
  ingredients: ScannedIngredient[]
  steps: string[]
  notes: string | null
  tags: string[]
  pantry_match_pct: number
  confidence: 'high' | 'medium' | 'low'
  warnings: string[]
 }
 export interface UserRecipe {
  id: number
  title: string
  subtitle: string | null
  servings: string | null
  cook_time: string | null
  source_note: string | null
  ingredients: ScannedIngredient[]
  steps: string[]
  notes: string | null
  tags: string[]
  source: string
  pantry_match_pct: number | null
  created_at: string
 }
 export const recipeScanAPI = {
  /** Scan 1-4 recipe photos. Returns structured recipe for review (not saved). */
  scan(files: File[]): Promise<ScannedRecipe> {
    const form = new FormData()
    files.forEach((f) => form.append('files', f))
    return api.post('/recipes/scan', form, {
      headers: { 'Content-Type': 'multipart/form-data' },
      timeout: 120_000, // VLM can be slow on first call
    }).then((r) => r.data)
  },
  /** Scan recipe photos with live SSE progress events.
   *
   * Calls onProgress(status, message) for each intermediate event
   * ("allocating", "scanning", "structuring"), then resolves with the final
   * ScannedRecipe on success. Rejects on error or timeout.
   */
  async scanStream(
    files: File[],
    onProgress: (status: string, message: string) => void,
  ): Promise<ScannedRecipe> {
    const form = new FormData()
    files.forEach((f) => form.append('files', f))
    const response = await fetch(`${API_BASE_URL}/recipes/scan/stream`, {
      method: 'POST',
      body: form,
    })
    if (!response.ok || !response.body) {
      let detail = ''
      try { detail = await response.text() } catch (_) { /* ignore */ }
      throw new Error(detail || `Scan failed (${response.status})`)
    }
    const reader = response.body.getReader()
    const decoder = new TextDecoder()
    let buffer = ''
    while (true) {
      const { done, value } = await reader.read()
      if (done) break
      buffer += decoder.decode(value, { stream: true })
      const lines = buffer.split('\n')
      buffer = lines.pop() ?? ''
      for (const line of lines) {
        if (!line.startsWith('data: ')) continue
        let data: Record<string, unknown>
        try { data = JSON.parse(line.slice(6)) } catch { continue }
        if (data.status === 'done') return data.recipe as ScannedRecipe
        if (data.status === 'error') throw new Error((data.message as string) || 'Scan failed')
        onProgress(data.status as string, data.message as string)
      }
    }
    throw new Error('Stream ended without a result')
  },
  /** Save a reviewed/edited scanned recipe to user_recipes. */
  saveScanned(recipe: Omit<ScannedRecipe, 'pantry_match_pct' | 'confidence' | 'warnings'> & { source?: string }): Promise<UserRecipe> {
    return api.post('/recipes/scan/save', recipe).then((r) => r.data)
  },
  /** List all user-created recipes (scan + manual). */
  listUserRecipes(): Promise<UserRecipe[]> {
    return api.get('/recipes/user').then((r) => r.data)
  },
  /** Get a single user recipe by ID. */
  getUserRecipe(id: number): Promise<UserRecipe> {
    return api.get(`/recipes/user/${id}`).then((r) => r.data)
  },
  /** Delete a user recipe. */
  deleteUserRecipe(id: number): Promise<void> {
    return api.delete(`/recipes/user/${id}`).then(() => undefined)
  },
 }
 export default api
--- a/frontend/src/stores/community.ts
+++ b/frontend/src/stores/community.ts
@ -64,20 +64,6 @@ export interface PublishPayload {
  recipe_id?: number
  outcome_notes?: string
  slots?: CommunityPostSlot[]
  similar_to_ref?: string
 }
 export type SimilarityTier = 'exact_recipe' | 'very_similar' | 'somewhat_similar'
 export interface SimilarPost {
  slug: string
  title: string
  recipe_name: string | null
  pseudonym: string
  published: string
  similarity_tier: SimilarityTier
  jaccard_score: number | null
  tier_description: string
 }
 export interface PublishResult {
@ -121,25 +107,6 @@ export const useCommunityStore = defineStore('community', () => {
    return response.data
  }
  async function checkSimilar(
    title: string,
    recipeId?: number | null,
    postType?: string,
  ): Promise<SimilarPost[]> {
    try {
      const body: Record<string, unknown> = { title }
      if (recipeId != null) body.recipe_id = recipeId
      if (postType) body.post_type = postType
      const response = await api.post<{ similar_posts: SimilarPost[] }>(
        '/community/check-similar',
        body,
      )
      return response.data.similar_posts
    } catch {
      return []
    }
  }
  return {
    posts,
    loading,
@ -148,6 +115,5 @@ export const useCommunityStore = defineStore('community', () => {
    fetchPosts,
    forkPost,
    publishPost,
    checkSimilar,
  }
 })
--- a/frontend/src/stores/inventory.ts
+++ b/frontend/src/stores/inventory.ts
@ -55,12 +55,11 @@ export const useInventoryStore = defineStore('inventory', () => {
    error.value = null
    try {
-      const result = await inventoryAPI.listItems({
+      items.value = await inventoryAPI.listItems({
        item_status: statusFilter.value === 'all' ? undefined : statusFilter.value,
        location: locationFilter.value === 'all' ? undefined : locationFilter.value,
        limit: 1000,
      })
      items.value = Array.isArray(result) ? result : []
    } catch (err: any) {
      error.value = err.response?.data?.detail || 'Failed to fetch inventory items'
      console.error('Error fetching inventory:', err)
--- a/frontend/src/stores/mealPlan.ts
+++ b/frontend/src/stores/mealPlan.ts
@ -34,8 +34,7 @@ export const useMealPlanStore = defineStore('mealPlan', () => {
  async function loadPlans() {
    loading.value = true
    try {
-      const result = await mealPlanAPI.list()
+      plans.value = await mealPlanAPI.list()
      plans.value = Array.isArray(result) ? result : []
    } finally {
      loading.value = false
    }
--- a/frontend/src/stores/recipes.ts
+++ b/frontend/src/stores/recipes.ts
@ -7,7 +7,7 @@
 import { defineStore } from 'pinia'
 import { ref, computed, watch } from 'vue'
-import { recipesAPI, type RecipeResult, type RecipeSuggestion, type RecipeRequest, type RecipeJobStatusValue, type NutritionFilters } from '../services/api'
+import { recipesAPI, type RecipeResult, type RecipeSuggestion, type RecipeRequest, type NutritionFilters } from '../services/api'
 const DISMISSED_KEY = 'kiwi:dismissed_recipes'
 const DISMISS_TTL_MS = 7 * 24 * 60 * 60 * 1000
@ -23,7 +23,6 @@ const FILTER_MODE_KEY = 'kiwi:builder_filter_mode'
 const CONSTRAINTS_KEY = 'kiwi:constraints'
 const ALLERGIES_KEY = 'kiwi:allergies'
 const EXCLUDE_INGREDIENTS_KEY = 'kiwi:exclude_ingredients'
 function loadConstraints(): string[] {
  try {
@ -51,19 +50,6 @@ function saveAllergies(vals: string[]) {
  localStorage.setItem(ALLERGIES_KEY, JSON.stringify(vals))
 }
 function loadExcludeIngredients(): string[] {
  try {
    const raw = localStorage.getItem(EXCLUDE_INGREDIENTS_KEY)
    return raw ? JSON.parse(raw) : []
  } catch {
    return []
  }
 }
 function saveExcludeIngredients(vals: string[]) {
  localStorage.setItem(EXCLUDE_INGREDIENTS_KEY, JSON.stringify(vals))
 }
 type MissingIngredientMode = 'hidden' | 'greyed' | 'add-to-cart'
 type BuilderFilterMode = 'text' | 'tags'
@ -135,13 +121,11 @@ export const useRecipesStore = defineStore('recipes', () => {
  const result = ref<RecipeResult | null>(null)
  const loading = ref(false)
  const error = ref<string | null>(null)
  const jobStatus = ref<RecipeJobStatusValue | null>(null)
  // Request parameters
  const level = ref(1)
  const constraints = ref<string[]>(loadConstraints())
  const allergies = ref<string[]>(loadAllergies())
  const excludeIngredients = ref<string[]>(loadExcludeIngredients())
  const hardDayMode = ref(false)
  const maxMissing = ref<number | null>(null)
  const styleId = ref<string | null>(null)
@ -151,8 +135,6 @@ export const useRecipesStore = defineStore('recipes', () => {
  const pantryMatchOnly = ref(false)
  const complexityFilter = ref<string | null>(null)
  const maxTimeMin = ref<number | null>(null)
  const maxTotalMin = ref<number | null>(null)
  const maxActiveMin = ref<number | null>(null)
  const nutritionFilters = ref<NutritionFilters>({
    max_calories: null,
    max_sugar_g: null,
@ -178,19 +160,13 @@ export const useRecipesStore = defineStore('recipes', () => {
  watch(builderFilterMode, (val) => localStorage.setItem(FILTER_MODE_KEY, val))
  watch(constraints, (val) => saveConstraints(val), { deep: true })
  watch(allergies, (val) => saveAllergies(val), { deep: true })
  watch(excludeIngredients, (val) => saveExcludeIngredients(val), { deep: true })
  const dismissedCount = computed(() => dismissedIds.value.size)
-  function _buildRequest(
+  function _buildRequest(pantryItems: string[], extraExcluded: number[] = []): RecipeRequest {
    pantryItems: string[],
    secondaryPantryItems: Record<string, string> = {},
    extraExcluded: number[] = [],
  ): RecipeRequest {
    const excluded = new Set([...dismissedIds.value, ...extraExcluded])
    return {
      pantry_items: pantryItems,
      secondary_pantry_items: secondaryPantryItems,
      level: level.value,
      constraints: constraints.value,
      allergies: allergies.value,
@ -202,13 +178,10 @@ export const useRecipesStore = defineStore('recipes', () => {
      wildcard_confirmed: wildcardConfirmed.value,
      nutrition_filters: nutritionFilters.value,
      excluded_ids: [...excluded],
      exclude_ingredients: excludeIngredients.value,
      shopping_mode: shoppingMode.value,
      pantry_match_only: pantryMatchOnly.value,
      complexity_filter: complexityFilter.value,
      max_time_min: maxTimeMin.value,
      max_total_min: maxTotalMin.value,
      max_active_min: maxActiveMin.value,
    }
  }
@ -218,68 +191,29 @@ export const useRecipesStore = defineStore('recipes', () => {
    }
  }
-  async function suggest(pantryItems: string[], secondaryPantryItems: Record<string, string> = {}) {
+  async function suggest(pantryItems: string[]) {
    loading.value = true
    error.value = null
    jobStatus.value = null
    seenIds.value = new Set()
    try {
-      if (level.value >= 3) {
+      result.value = await recipesAPI.suggest(_buildRequest(pantryItems))
        await _suggestAsync(pantryItems, secondaryPantryItems)
      } else {
        result.value = await recipesAPI.suggest(_buildRequest(pantryItems, secondaryPantryItems))
      _trackSeen(result.value.suggestions)
      }
    } catch (err: unknown) {
      error.value = err instanceof Error ? err.message : 'Failed to get recipe suggestions'
    } finally {
      loading.value = false
      jobStatus.value = null
    }
  }
-  async function _suggestAsync(pantryItems: string[], secondaryPantryItems: Record<string, string>) {
+  async function loadMore(pantryItems: string[]) {
    const queued = await recipesAPI.suggestAsync(_buildRequest(pantryItems, secondaryPantryItems))
    // CLOUD_MODE or future sync fallback: server returned result directly (status 200)
    if ('suggestions' in queued) {
      result.value = queued as unknown as RecipeResult
      _trackSeen(result.value.suggestions)
      return
    }
    jobStatus.value = 'queued'
    const { job_id } = queued
    const deadline = Date.now() + 90_000
    const POLL_MS = 2_500
    while (Date.now() < deadline) {
      await new Promise((resolve) => setTimeout(resolve, POLL_MS))
      const poll = await recipesAPI.pollJob(job_id)
      jobStatus.value = poll.status
      if (poll.status === 'done') {
        result.value = poll.result
        if (result.value) _trackSeen(result.value.suggestions)
        return
      }
      if (poll.status === 'failed') {
        throw new Error(poll.error ?? 'Recipe generation failed')
      }
    }
    throw new Error('Recipe generation timed out — the model may be busy. Try again.')
  }
  async function loadMore(pantryItems: string[], secondaryPantryItems: Record<string, string> = {}) {
    if (!result.value || loading.value) return
    loading.value = true
    error.value = null
    try {
      // Exclude everything already shown (dismissed + all seen this session)
-      const more = await recipesAPI.suggest(_buildRequest(pantryItems, secondaryPantryItems, [...seenIds.value]))
+      const more = await recipesAPI.suggest(_buildRequest(pantryItems, [...seenIds.value]))
      if (more.suggestions.length === 0) {
        error.value = 'No more recipes found — try clearing dismissed or adjusting filters.'
      } else {
@ -320,8 +254,6 @@ export const useRecipesStore = defineStore('recipes', () => {
    localStorage.removeItem(DISMISSED_KEY)
  }
  // Orbital cadence: cookedAt anchors to completion, not to a schedule.
  // Days-since display measures from this timestamp — no debt accumulates.
  function logCook(id: number, title: string) {
    const entry: CookLogEntry = { id, title, cookedAt: Date.now() }
    cookLog.value = [...cookLog.value, entry]
@ -333,13 +265,6 @@ export const useRecipesStore = defineStore('recipes', () => {
    localStorage.removeItem(COOK_LOG_KEY)
  }
  function lastCookedDaysAgo(recipeId: number): number | null {
    const entries = cookLog.value.filter((e) => e.id === recipeId)
    if (entries.length === 0) return null
    const latestMs = Math.max(...entries.map((e) => e.cookedAt))
    return Math.floor((Date.now() - latestMs) / 86_400_000)
  }
  function isBookmarked(id: number): boolean {
    return bookmarks.value.some((b) => b.id === id)
  }
@ -368,37 +293,19 @@ export const useRecipesStore = defineStore('recipes', () => {
    localStorage.removeItem(ALLERGIES_KEY)
  }
  function clearExcludeIngredients() {
    excludeIngredients.value = []
    localStorage.removeItem(EXCLUDE_INGREDIENTS_KEY)
  }
  function clearResult() {
    result.value = null
    error.value = null
    wildcardConfirmed.value = false
  }
  async function streamSuggest(
    pantryItems: string[],
    secondaryPantryItems: Record<string, string>,
    onChunk: (chunk: string) => void,
    onDone: () => void,
    onError: (err: string) => void,
  ): Promise<void> {
    const req = _buildRequest(pantryItems, secondaryPantryItems)
    await recipesAPI.suggestRecipeStream(req, onChunk, onDone, onError)
  }
  return {
    result,
    loading,
    error,
    jobStatus,
    level,
    constraints,
    allergies,
    excludeIngredients,
    hardDayMode,
    maxMissing,
    styleId,
@ -408,26 +315,21 @@ export const useRecipesStore = defineStore('recipes', () => {
    pantryMatchOnly,
    complexityFilter,
    maxTimeMin,
    maxTotalMin,
    maxActiveMin,
    nutritionFilters,
    dismissedIds,
    dismissedCount,
    cookLog,
    logCook,
    clearCookLog,
    lastCookedDaysAgo,
    bookmarks,
    isBookmarked,
    toggleBookmark,
    clearBookmarks,
    clearConstraints,
    clearAllergies,
    clearExcludeIngredients,
    missingIngredientMode,
    builderFilterMode,
    suggest,
    streamSuggest,
    loadMore,
    dismiss,
    undismiss,
--- a/frontend/src/stores/savedRecipes.ts
+++ b/frontend/src/stores/savedRecipes.ts
@ -11,7 +11,7 @@ export const useSavedRecipesStore = defineStore('savedRecipes', () => {
  const saved = ref<SavedRecipe[]>([])
  const collections = ref<RecipeCollection[]>([])
  const loading = ref(false)
-  const sortBy = ref<'saved_at' | 'rating' | 'title' | 'last_cooked'>('saved_at')
+  const sortBy = ref<'saved_at' | 'rating' | 'title'>('saved_at')
  const activeCollectionId = ref<number | null>(null)
  const savedIds = computed(() => new Set(saved.value.map((s) => s.recipe_id)))
@ -27,15 +27,12 @@ export const useSavedRecipesStore = defineStore('savedRecipes', () => {
  async function load() {
    loading.value = true
    try {
-      // Fetch independently — a collections 403 (Free tier) must not prevent
+      const [items, cols] = await Promise.all([
-      // saved recipes from loading. Backend now returns [] for Free, but guard
+        savedRecipesAPI.list({ sort_by: sortBy.value, collection_id: activeCollectionId.value ?? undefined }),
      // here too in case an older API version is deployed.
      const [itemsResult, colsResult] = await Promise.allSettled([
        savedRecipesAPI.list({ sort_by: sortBy.value === 'last_cooked' ? 'saved_at' : sortBy.value, collection_id: activeCollectionId.value ?? undefined }),
        savedRecipesAPI.listCollections(),
      ])
-      if (itemsResult.status === 'fulfilled') saved.value = itemsResult.value
+      saved.value = items
-      if (colsResult.status === 'fulfilled') collections.value = colsResult.value
+      collections.value = cols
    } finally {
      loading.value = false
    }
--- a/frontend/src/stores/settings.ts
+++ b/frontend/src/stores/settings.ts
@ -1,69 +1,28 @@
 /**
 * Settings Store
 *
 * Manages user settings (cooking equipment, preferences) using Pinia.
 */
 import { defineStore } from 'pinia'
-import { ref, watch, nextTick } from 'vue'
+import { ref } from 'vue'
 import { settingsAPI } from '../services/api'
 import type { UnitSystem } from '../utils/units'
 import type { SensoryPreferences } from '../services/api'
 import { DEFAULT_SENSORY_PREFERENCES } from '../services/api'
 export type TimeFirstLayout = 'auto' | 'time_first' | 'normal'
 function debounce(fn: () => void, ms: number): () => void {
  let t: ReturnType<typeof setTimeout>
  return () => { clearTimeout(t); t = setTimeout(fn, ms) }
 }
 export const useSettingsStore = defineStore('settings', () => {
  // State
  const cookingEquipment = ref<string[]>([])
  const unitSystem = ref<UnitSystem>('metric')
  const shoppingLocale = ref<string>('us')
  const sensoryPreferences = ref<SensoryPreferences>({ ...DEFAULT_SENSORY_PREFERENCES })
  const timeFirstLayout = ref<TimeFirstLayout>('auto')
  const loading = ref(false)
  const saved = ref(false)
-  // Prevents autosave watchers from firing during initial load hydration.
+  // Actions
  // Set to true after nextTick() at the end of load() — by that point all
  // watcher jobs queued by the hydration assignments have already flushed.
  let _hydrated = false
  function _flash() {
    saved.value = true
    setTimeout(() => { saved.value = false }, 2000)
  }
  async function _saveKey(key: string, value: string): Promise<void> {
    if (!_hydrated) return
    try {
      await settingsAPI.setSetting(key, value)
      _flash()
    } catch (err: unknown) {
      console.error('Autosave failed for key:', key, err)
    }
  }
  const _autosave = {
    equipment: debounce(() => _saveKey('cooking_equipment', JSON.stringify(cookingEquipment.value)), 600),
    unit:      debounce(() => _saveKey('unit_system', unitSystem.value), 600),
    locale:    debounce(() => _saveKey('shopping_locale', shoppingLocale.value), 600),
    sensory:   debounce(() => _saveKey('sensory_preferences', JSON.stringify(sensoryPreferences.value)), 600),
    layout:    debounce(() => _saveKey('time_first_layout', timeFirstLayout.value), 600),
  }
  watch(cookingEquipment, _autosave.equipment, { deep: true })
  watch(unitSystem, _autosave.unit)
  watch(shoppingLocale, _autosave.locale)
  watch(sensoryPreferences, _autosave.sensory, { deep: true })
  watch(timeFirstLayout, _autosave.layout)
  async function load() {
    loading.value = true
    try {
-      const [rawEquipment, rawUnits, rawLocale, rawSensory, rawTimeFirst] = await Promise.allSettled([
+      const [rawEquipment, rawUnits] = await Promise.allSettled([
        settingsAPI.getSetting('cooking_equipment'),
        settingsAPI.getSetting('unit_system'),
        settingsAPI.getSetting('shopping_locale'),
        settingsAPI.getSetting('sensory_preferences'),
        settingsAPI.getSetting('time_first_layout'),
      ])
      if (rawEquipment.status === 'fulfilled' && rawEquipment.value) {
        cookingEquipment.value = JSON.parse(rawEquipment.value)
@ -71,44 +30,24 @@ export const useSettingsStore = defineStore('settings', () => {
      if (rawUnits.status === 'fulfilled' && rawUnits.value) {
        unitSystem.value = rawUnits.value as UnitSystem
      }
      if (rawLocale.status === 'fulfilled' && rawLocale.value) {
        shoppingLocale.value = rawLocale.value
      }
      if (rawSensory.status === 'fulfilled' && rawSensory.value) {
        try {
          sensoryPreferences.value = JSON.parse(rawSensory.value)
        } catch {
          sensoryPreferences.value = { ...DEFAULT_SENSORY_PREFERENCES }
        }
      }
      if (rawTimeFirst.status === 'fulfilled' && rawTimeFirst.value) {
        timeFirstLayout.value = rawTimeFirst.value as TimeFirstLayout
      }
    } catch (err: unknown) {
      console.error('Failed to load settings:', err)
    } finally {
      loading.value = false
    }
    // Yield past the watcher flush triggered by hydration assignments above.
    // After nextTick, any pending watcher jobs from this load() have already
    // run (and been ignored by _hydrated guard), so user-driven changes from
    // here forward will correctly trigger autosave.
    await nextTick()
    _hydrated = true
  }
  // Kept for explicit full-save scenarios (e.g. fallback, tests).
  async function save() {
    loading.value = true
    try {
      await Promise.all([
        settingsAPI.setSetting('cooking_equipment', JSON.stringify(cookingEquipment.value)),
        settingsAPI.setSetting('unit_system', unitSystem.value),
        settingsAPI.setSetting('shopping_locale', shoppingLocale.value),
        settingsAPI.setSetting('sensory_preferences', JSON.stringify(sensoryPreferences.value)),
        settingsAPI.setSetting('time_first_layout', timeFirstLayout.value),
      ])
-      _flash()
+      saved.value = true
      setTimeout(() => {
        saved.value = false
      }, 2000)
    } catch (err: unknown) {
      console.error('Failed to save settings:', err)
    } finally {
@ -116,26 +55,15 @@ export const useSettingsStore = defineStore('settings', () => {
    }
  }
  // Kept for backward compat; autosave handles sensory changes now.
  async function saveSensory() {
    try {
      await settingsAPI.setSetting('sensory_preferences', JSON.stringify(sensoryPreferences.value))
      _flash()
    } catch (err: unknown) {
      console.error('Failed to save sensory preferences:', err)
    }
  }
  return {
    // State
    cookingEquipment,
    unitSystem,
    shoppingLocale,
    sensoryPreferences,
    timeFirstLayout,
    loading,
    saved,
    // Actions
    load,
    save,
    saveSensory,
  }
 })
--- a/Show more
+++ b/Show more