pyr0ball
d9660093b1
- Rename 002_background_tasks.sql → 007_background_tasks.sql to avoid collision with existing 002_add_listing_format.sql migration - Add CREATE UNIQUE INDEX on trust_scores(listing_id) in same migration so save_trust_scores() can use ON CONFLICT upsert semantics - Add Store.save_trust_scores() — upserts scores keyed by listing_id; preserves photo_analysis_json so runner writes are never clobbered - runner.py: replace raw sqlite3.connect() with get_connection() throughout (timeout=30 + WAL mode); fix connection leak in insert_task via try/finally - _run_trust_photo_analysis: read 'user_db' from params to write results to the correct per-user DB in cloud mode (was silently writing to wrong DB) - main.py lifespan: use _shared_db_path() in cloud mode so background_tasks queue lives in shared DB, not _LOCAL_SNIPE_DB - Add _enqueue_vision_tasks() and call it after score_batch() — this is the missing enqueue call site; gated by features.photo_analysis (Paid tier) - Test fixture: add missing 'stage' column to background_tasks schema
631 lines
25 KiB
Python
631 lines
25 KiB
Python
"""Snipe FastAPI — search endpoint wired to ScrapedEbayAdapter + TrustScorer."""
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from __future__ import annotations
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import dataclasses
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import hashlib
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import logging
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import os
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from concurrent.futures import ThreadPoolExecutor
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from contextlib import asynccontextmanager
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from pathlib import Path
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import csv
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import io
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from fastapi import Depends, FastAPI, HTTPException, UploadFile, File
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from fastapi.middleware.cors import CORSMiddleware
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from circuitforge_core.config import load_env
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from app.db.store import Store
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from app.db.models import SavedSearch as SavedSearchModel, ScammerEntry
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from app.platforms import SearchFilters
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from app.platforms.ebay.scraper import ScrapedEbayAdapter
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from app.platforms.ebay.adapter import EbayAdapter
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from app.platforms.ebay.auth import EbayTokenManager
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from app.platforms.ebay.query_builder import expand_queries, parse_groups
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from app.trust import TrustScorer
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from api.cloud_session import CloudUser, compute_features, get_session
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from api.ebay_webhook import router as ebay_webhook_router
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load_env(Path(".env"))
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log = logging.getLogger(__name__)
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@asynccontextmanager
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async def _lifespan(app: FastAPI):
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# Start vision/LLM background task scheduler.
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# background_tasks queue lives in shared_db (cloud) or local_db (local)
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# so the scheduler has a single stable DB path across all cloud users.
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from app.tasks.scheduler import get_scheduler, reset_scheduler
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from api.cloud_session import CLOUD_MODE, _LOCAL_SNIPE_DB, _shared_db_path
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sched_db = _shared_db_path() if CLOUD_MODE else _LOCAL_SNIPE_DB
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get_scheduler(sched_db)
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log.info("Snipe task scheduler started (db=%s)", sched_db)
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yield
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get_scheduler(sched_db).shutdown(timeout=10.0)
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reset_scheduler()
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log.info("Snipe task scheduler stopped.")
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def _ebay_creds() -> tuple[str, str, str]:
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"""Return (client_id, client_secret, env) from env vars.
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New names: EBAY_APP_ID / EBAY_CERT_ID (sandbox: EBAY_SANDBOX_APP_ID / EBAY_SANDBOX_CERT_ID)
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Legacy fallback: EBAY_CLIENT_ID / EBAY_CLIENT_SECRET
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"""
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env = os.environ.get("EBAY_ENV", "production").strip()
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if env == "sandbox":
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client_id = os.environ.get("EBAY_SANDBOX_APP_ID", "").strip()
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client_secret = os.environ.get("EBAY_SANDBOX_CERT_ID", "").strip()
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else:
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client_id = (os.environ.get("EBAY_APP_ID") or os.environ.get("EBAY_CLIENT_ID", "")).strip()
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client_secret = (os.environ.get("EBAY_CERT_ID") or os.environ.get("EBAY_CLIENT_SECRET", "")).strip()
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return client_id, client_secret, env
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app = FastAPI(title="Snipe API", version="0.1.0", lifespan=_lifespan)
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app.include_router(ebay_webhook_router)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.get("/api/health")
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def health():
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return {"status": "ok"}
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@app.get("/api/session")
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def session_info(session: CloudUser = Depends(get_session)):
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"""Return the current session tier and computed feature flags.
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Used by the Vue frontend to gate UI features (pages slider cap,
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saved search limits, shared DB badges, etc.) without hardcoding
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tier logic client-side.
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"""
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features = compute_features(session.tier)
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return {
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"user_id": session.user_id,
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"tier": session.tier,
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"features": dataclasses.asdict(features),
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}
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def _trigger_scraper_enrichment(
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listings: list,
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shared_store: Store,
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shared_db: Path,
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) -> None:
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"""Fire-and-forget background enrichment for missing seller signals.
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Two enrichment passes run concurrently in the same daemon thread:
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1. BTF (/itm/ pages) — fills account_age_days for sellers where it is None.
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2. _ssn search pages — fills category_history_json for sellers with no history.
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The main response returns immediately; enriched data lands in the DB for
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future searches. Uses ScrapedEbayAdapter's Playwright stack regardless of
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which adapter was used for the main search (Shopping API handles age for
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the API adapter inline; BTF is the fallback for no-creds / scraper mode).
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shared_store: used for pre-flight seller checks (same-thread reads).
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shared_db: path passed to background thread — it creates its own Store
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(sqlite3 connections are not thread-safe).
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"""
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# Caps per search: limits Playwright sessions launched in the background so we
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# don't hammer Kasada or spin up dozens of Xvfb instances after a large search.
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# Remaining sellers get enriched incrementally on subsequent searches.
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_BTF_MAX_PER_SEARCH = 3
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_CAT_MAX_PER_SEARCH = 3
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needs_btf: dict[str, str] = {}
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needs_categories: list[str] = []
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for listing in listings:
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sid = listing.seller_platform_id
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if not sid:
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continue
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seller = shared_store.get_seller("ebay", sid)
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if not seller:
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continue
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if ((seller.account_age_days is None or seller.feedback_count == 0)
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and sid not in needs_btf
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and len(needs_btf) < _BTF_MAX_PER_SEARCH):
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needs_btf[sid] = listing.platform_listing_id
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if (seller.category_history_json in ("{}", "", None)
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and sid not in needs_categories
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and len(needs_categories) < _CAT_MAX_PER_SEARCH):
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needs_categories.append(sid)
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if not needs_btf and not needs_categories:
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return
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log.info(
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"Scraper enrichment: %d BTF age + %d category pages queued",
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len(needs_btf), len(needs_categories),
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)
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def _run():
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try:
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enricher = ScrapedEbayAdapter(Store(shared_db))
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if needs_btf:
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enricher.enrich_sellers_btf(needs_btf, max_workers=2)
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log.info("BTF enrichment complete for %d sellers", len(needs_btf))
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if needs_categories:
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enricher.enrich_sellers_categories(needs_categories, max_workers=2)
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log.info("Category enrichment complete for %d sellers", len(needs_categories))
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except Exception as e:
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log.warning("Scraper enrichment failed: %s", e)
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import threading
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t = threading.Thread(target=_run, daemon=True)
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t.start()
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def _enqueue_vision_tasks(
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listings: list,
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trust_scores_list: list,
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session: "CloudUser",
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) -> None:
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"""Enqueue trust_photo_analysis tasks for listings with photos.
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Runs fire-and-forget: tasks land in the scheduler queue and the response
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returns immediately. Results are written back to trust_scores.photo_analysis_json
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by the runner when the vision LLM completes.
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session.shared_db: where background_tasks lives (scheduler's DB).
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session.user_db: encoded in params so the runner writes to the right
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trust_scores table in cloud mode.
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"""
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import json as _json
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from app.tasks.runner import insert_task
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from app.tasks.scheduler import get_scheduler
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from api.cloud_session import CLOUD_MODE, _shared_db_path, _LOCAL_SNIPE_DB
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sched_db = _shared_db_path() if CLOUD_MODE else _LOCAL_SNIPE_DB
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sched = get_scheduler(sched_db)
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enqueued = 0
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for listing, ts in zip(listings, trust_scores_list):
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if not listing.photo_urls or not listing.id:
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continue
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params = _json.dumps({
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"photo_url": listing.photo_urls[0],
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"listing_title": listing.title,
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"user_db": str(session.user_db),
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})
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task_id, is_new = insert_task(
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sched_db, "trust_photo_analysis", job_id=listing.id, params=params
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)
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if is_new:
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ok = sched.enqueue(task_id, "trust_photo_analysis", listing.id, params)
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if not ok:
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log.warning(
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"Vision task queue full — dropped task for listing %s",
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listing.platform_listing_id,
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)
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else:
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enqueued += 1
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if enqueued:
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log.info("Enqueued %d vision analysis task(s)", enqueued)
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def _parse_terms(raw: str) -> list[str]:
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"""Split a comma-separated keyword string into non-empty, stripped terms."""
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return [t.strip() for t in raw.split(",") if t.strip()]
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def _make_adapter(shared_store: Store, force: str = "auto"):
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"""Return the appropriate adapter.
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force: "auto" | "api" | "scraper"
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auto — API if creds present, else scraper
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api — Browse API (raises if no creds)
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scraper — Playwright scraper regardless of creds
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Adapters receive shared_store because they only read/write sellers and
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market_comps — never listings. Listings are returned and saved by the caller.
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"""
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client_id, client_secret, env = _ebay_creds()
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has_creds = bool(client_id and client_secret)
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if force == "scraper":
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return ScrapedEbayAdapter(shared_store)
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if force == "api":
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if not has_creds:
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raise ValueError("adapter=api requested but no eBay API credentials configured")
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return EbayAdapter(EbayTokenManager(client_id, client_secret, env), shared_store, env=env)
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# auto
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if has_creds:
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return EbayAdapter(EbayTokenManager(client_id, client_secret, env), shared_store, env=env)
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log.debug("No eBay API credentials — using scraper adapter (partial trust scores)")
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return ScrapedEbayAdapter(shared_store)
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def _adapter_name(force: str = "auto") -> str:
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"""Return the name of the adapter that would be used — without creating it."""
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client_id, client_secret, _ = _ebay_creds()
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if force == "scraper":
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return "scraper"
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if force == "api" or (force == "auto" and client_id and client_secret):
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return "api"
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return "scraper"
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@app.get("/api/search")
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def search(
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q: str = "",
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max_price: float = 0,
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min_price: float = 0,
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pages: int = 1,
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must_include: str = "", # raw filter string; client-side always applied
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must_include_mode: str = "all", # "all" | "any" | "groups" — drives eBay expansion
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must_exclude: str = "", # comma-separated; forwarded to eBay -term + client-side
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category_id: str = "", # eBay category ID — forwarded to Browse API / scraper _sacat
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adapter: str = "auto", # "auto" | "api" | "scraper" — override adapter selection
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session: CloudUser = Depends(get_session),
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):
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if not q.strip():
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return {"listings": [], "trust_scores": {}, "sellers": {}, "market_price": None, "adapter_used": _adapter_name(adapter)}
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# Cap pages to the tier's maximum — free cloud users get 1 page, local gets unlimited.
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features = compute_features(session.tier)
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pages = min(max(1, pages), features.max_pages)
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must_exclude_terms = _parse_terms(must_exclude)
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# In Groups mode, expand OR groups into multiple targeted eBay queries to
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# guarantee comprehensive result coverage — eBay relevance won't silently drop variants.
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if must_include_mode == "groups" and must_include.strip():
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or_groups = parse_groups(must_include)
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ebay_queries = expand_queries(q, or_groups)
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else:
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ebay_queries = [q]
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base_filters = SearchFilters(
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max_price=max_price if max_price > 0 else None,
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min_price=min_price if min_price > 0 else None,
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pages=pages,
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must_exclude=must_exclude_terms, # forwarded to eBay -term by the scraper
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category_id=category_id.strip() or None,
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)
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adapter_used = _adapter_name(adapter)
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shared_db = session.shared_db
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user_db = session.user_db
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# Each thread creates its own Store — sqlite3 check_same_thread=True.
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def _run_search(ebay_query: str) -> list:
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return _make_adapter(Store(shared_db), adapter).search(ebay_query, base_filters)
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def _run_comps() -> None:
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try:
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_make_adapter(Store(shared_db), adapter).get_completed_sales(q, pages)
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except Exception:
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log.warning("comps: unhandled exception for %r", q, exc_info=True)
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try:
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# Comps submitted first — guarantees an immediate worker slot even at max concurrency.
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# Seller enrichment runs after the executor exits (background thread), so comps are
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# always prioritised over tracking seller age / category history.
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max_workers = min(len(ebay_queries) + 1, 5)
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with ThreadPoolExecutor(max_workers=max_workers) as ex:
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comps_future = ex.submit(_run_comps)
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search_futures = [ex.submit(_run_search, eq) for eq in ebay_queries]
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# Merge and deduplicate across all search queries
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seen_ids: set[str] = set()
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listings: list = []
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for fut in search_futures:
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for listing in fut.result():
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if listing.platform_listing_id not in seen_ids:
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seen_ids.add(listing.platform_listing_id)
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listings.append(listing)
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comps_future.result() # side-effect: market comp written to shared DB
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except Exception as e:
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log.warning("eBay scrape failed: %s", e)
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raise HTTPException(status_code=502, detail=f"eBay search failed: {e}")
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log.info("Multi-search: %d queries → %d unique listings", len(ebay_queries), len(listings))
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# Main-thread stores — fresh connections, same thread.
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# shared_store: sellers, market_comps (all users share this data)
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# user_store: listings, saved_searches (per-user in cloud mode, same file in local mode)
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shared_store = Store(shared_db)
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user_store = Store(user_db)
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user_store.save_listings(listings)
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# Derive category_history from accumulated listing data — free for API adapter
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# (category_name comes from Browse API response), no-op for scraper listings (category_name=None).
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# Reads listings from user_store, writes seller categories to shared_store.
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seller_ids = list({l.seller_platform_id for l in listings if l.seller_platform_id})
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n_cat = shared_store.refresh_seller_categories("ebay", seller_ids, listing_store=user_store)
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if n_cat:
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log.info("Category history derived for %d sellers from listing data", n_cat)
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# Re-fetch to hydrate staging fields (times_seen, first_seen_at, id, price_at_first_seen)
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# that are only available from the DB after the upsert.
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staged = user_store.get_listings_staged("ebay", [l.platform_listing_id for l in listings])
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listings = [staged.get(l.platform_listing_id, l) for l in listings]
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# BTF enrichment: scrape /itm/ pages for sellers missing account_age_days.
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# Runs in the background so it doesn't delay the response; next search of
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# the same sellers will have full scores.
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_trigger_scraper_enrichment(listings, shared_store, shared_db)
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scorer = TrustScorer(shared_store)
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trust_scores_list = scorer.score_batch(listings, q)
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# Persist trust scores so background vision tasks have a row to UPDATE.
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user_store.save_trust_scores(trust_scores_list)
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# Enqueue vision analysis for listings with photos — Paid tier and above.
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features = compute_features(session.tier)
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if features.photo_analysis:
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_enqueue_vision_tasks(listings, trust_scores_list, session)
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query_hash = hashlib.md5(q.encode()).hexdigest()
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comp = shared_store.get_market_comp("ebay", query_hash)
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market_price = comp.median_price if comp else None
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# Serialize — keyed by platform_listing_id for easy Vue lookup
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trust_map = {
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listing.platform_listing_id: dataclasses.asdict(ts)
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for listing, ts in zip(listings, trust_scores_list)
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if ts is not None
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}
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seller_map = {
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listing.seller_platform_id: dataclasses.asdict(
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shared_store.get_seller("ebay", listing.seller_platform_id)
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)
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for listing in listings
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if listing.seller_platform_id
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and shared_store.get_seller("ebay", listing.seller_platform_id)
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}
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return {
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"listings": [dataclasses.asdict(l) for l in listings],
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"trust_scores": trust_map,
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"sellers": seller_map,
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"market_price": market_price,
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"adapter_used": adapter_used,
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}
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# ── On-demand enrichment ──────────────────────────────────────────────────────
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@app.post("/api/enrich")
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def enrich_seller(
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seller: str,
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listing_id: str,
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query: str = "",
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session: CloudUser = Depends(get_session),
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):
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"""Synchronous on-demand enrichment for a single seller + re-score.
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Runs enrichment paths in parallel:
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- Shopping API GetUserProfile (fast, ~500ms) — account_age_days if API creds present
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- BTF /itm/ Playwright scrape (~20s) — account_age_days fallback
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- _ssn Playwright scrape (~20s) — category_history_json
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BTF and _ssn run concurrently; total wall time ~20s when Playwright needed.
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Returns the updated trust_score and seller so the frontend can patch in-place.
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"""
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import threading
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shared_store = Store(session.shared_db)
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user_store = Store(session.user_db)
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shared_db = session.shared_db
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seller_obj = shared_store.get_seller("ebay", seller)
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if not seller_obj:
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raise HTTPException(status_code=404, detail=f"Seller '{seller}' not found")
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# Fast path: Shopping API for account age (inline, no Playwright)
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try:
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api_adapter = _make_adapter(shared_store, "api")
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if hasattr(api_adapter, "enrich_sellers_shopping_api"):
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api_adapter.enrich_sellers_shopping_api([seller])
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except Exception:
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pass # no API creds — fall through to BTF
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|
|
seller_obj = shared_store.get_seller("ebay", seller)
|
|
needs_btf = seller_obj is not None and (
|
|
seller_obj.account_age_days is None or seller_obj.feedback_count == 0
|
|
)
|
|
needs_categories = seller_obj is None or seller_obj.category_history_json in ("{}", "", None)
|
|
|
|
# Slow path: Playwright for remaining gaps (BTF + _ssn in parallel threads).
|
|
# Each thread creates its own Store — sqlite3 connections are not thread-safe.
|
|
if needs_btf or needs_categories:
|
|
errors: list[Exception] = []
|
|
|
|
def _btf():
|
|
try:
|
|
ScrapedEbayAdapter(Store(shared_db)).enrich_sellers_btf(
|
|
{seller: listing_id}, max_workers=1
|
|
)
|
|
except Exception as e:
|
|
errors.append(e)
|
|
|
|
def _ssn():
|
|
try:
|
|
ScrapedEbayAdapter(Store(shared_db)).enrich_sellers_categories(
|
|
[seller], max_workers=1
|
|
)
|
|
except Exception as e:
|
|
errors.append(e)
|
|
|
|
threads = []
|
|
if needs_btf:
|
|
threads.append(threading.Thread(target=_btf, daemon=True))
|
|
if needs_categories:
|
|
threads.append(threading.Thread(target=_ssn, daemon=True))
|
|
for t in threads:
|
|
t.start()
|
|
for t in threads:
|
|
t.join(timeout=60)
|
|
|
|
if errors:
|
|
log.warning("enrich_seller: %d scrape error(s): %s", len(errors), errors[0])
|
|
|
|
# Re-fetch listing with staging fields, re-score
|
|
staged = user_store.get_listings_staged("ebay", [listing_id])
|
|
listing = staged.get(listing_id)
|
|
if not listing:
|
|
raise HTTPException(status_code=404, detail=f"Listing '{listing_id}' not found")
|
|
|
|
scorer = TrustScorer(shared_store)
|
|
trust_list = scorer.score_batch([listing], query or listing.title)
|
|
trust = trust_list[0] if trust_list else None
|
|
|
|
seller_final = shared_store.get_seller("ebay", seller)
|
|
return {
|
|
"trust_score": dataclasses.asdict(trust) if trust else None,
|
|
"seller": dataclasses.asdict(seller_final) if seller_final else None,
|
|
}
|
|
|
|
|
|
# ── Saved Searches ────────────────────────────────────────────────────────────
|
|
|
|
class SavedSearchCreate(BaseModel):
|
|
name: str
|
|
query: str
|
|
filters_json: str = "{}"
|
|
|
|
|
|
@app.get("/api/saved-searches")
|
|
def list_saved_searches(session: CloudUser = Depends(get_session)):
|
|
user_store = Store(session.user_db)
|
|
return {"saved_searches": [dataclasses.asdict(s) for s in user_store.list_saved_searches()]}
|
|
|
|
|
|
@app.post("/api/saved-searches", status_code=201)
|
|
def create_saved_search(
|
|
body: SavedSearchCreate,
|
|
session: CloudUser = Depends(get_session),
|
|
):
|
|
user_store = Store(session.user_db)
|
|
features = compute_features(session.tier)
|
|
|
|
if features.saved_searches_limit is not None:
|
|
existing = user_store.list_saved_searches()
|
|
if len(existing) >= features.saved_searches_limit:
|
|
raise HTTPException(
|
|
status_code=403,
|
|
detail=f"Free tier allows up to {features.saved_searches_limit} saved searches. Upgrade to save more.",
|
|
)
|
|
|
|
created = user_store.save_saved_search(
|
|
SavedSearchModel(name=body.name, query=body.query, platform="ebay", filters_json=body.filters_json)
|
|
)
|
|
return dataclasses.asdict(created)
|
|
|
|
|
|
@app.delete("/api/saved-searches/{saved_id}", status_code=204)
|
|
def delete_saved_search(saved_id: int, session: CloudUser = Depends(get_session)):
|
|
Store(session.user_db).delete_saved_search(saved_id)
|
|
|
|
|
|
@app.patch("/api/saved-searches/{saved_id}/run")
|
|
def mark_saved_search_run(saved_id: int, session: CloudUser = Depends(get_session)):
|
|
Store(session.user_db).update_saved_search_last_run(saved_id)
|
|
return {"ok": True}
|
|
|
|
|
|
# ── Scammer Blocklist ─────────────────────────────────────────────────────────
|
|
# Blocklist lives in shared_db: all users on a shared cloud instance see the
|
|
# same community blocklist. In local (single-user) mode shared_db == user_db.
|
|
|
|
class BlocklistAdd(BaseModel):
|
|
platform: str = "ebay"
|
|
platform_seller_id: str
|
|
username: str
|
|
reason: str = ""
|
|
|
|
|
|
@app.get("/api/blocklist")
|
|
def list_blocklist(session: CloudUser = Depends(get_session)):
|
|
store = Store(session.shared_db)
|
|
return {"entries": [dataclasses.asdict(e) for e in store.list_blocklist()]}
|
|
|
|
|
|
@app.post("/api/blocklist", status_code=201)
|
|
def add_to_blocklist(body: BlocklistAdd, session: CloudUser = Depends(get_session)):
|
|
store = Store(session.shared_db)
|
|
entry = store.add_to_blocklist(ScammerEntry(
|
|
platform=body.platform,
|
|
platform_seller_id=body.platform_seller_id,
|
|
username=body.username,
|
|
reason=body.reason or None,
|
|
source="manual",
|
|
))
|
|
return dataclasses.asdict(entry)
|
|
|
|
|
|
@app.delete("/api/blocklist/{platform_seller_id}", status_code=204)
|
|
def remove_from_blocklist(platform_seller_id: str, session: CloudUser = Depends(get_session)):
|
|
Store(session.shared_db).remove_from_blocklist("ebay", platform_seller_id)
|
|
|
|
|
|
@app.get("/api/blocklist/export")
|
|
def export_blocklist(session: CloudUser = Depends(get_session)):
|
|
"""Download the blocklist as a CSV file."""
|
|
entries = Store(session.shared_db).list_blocklist()
|
|
buf = io.StringIO()
|
|
writer = csv.writer(buf)
|
|
writer.writerow(["platform", "platform_seller_id", "username", "reason", "source", "created_at"])
|
|
for e in entries:
|
|
writer.writerow([e.platform, e.platform_seller_id, e.username,
|
|
e.reason or "", e.source, e.created_at or ""])
|
|
buf.seek(0)
|
|
return StreamingResponse(
|
|
iter([buf.getvalue()]),
|
|
media_type="text/csv",
|
|
headers={"Content-Disposition": "attachment; filename=snipe-blocklist.csv"},
|
|
)
|
|
|
|
|
|
@app.post("/api/blocklist/import", status_code=201)
|
|
async def import_blocklist(
|
|
file: UploadFile = File(...),
|
|
session: CloudUser = Depends(get_session),
|
|
):
|
|
"""Import a CSV blocklist. Columns: platform_seller_id, username, reason (optional)."""
|
|
content = await file.read()
|
|
try:
|
|
text = content.decode("utf-8-sig") # handle BOM from Excel exports
|
|
except UnicodeDecodeError:
|
|
raise HTTPException(status_code=400, detail="File must be UTF-8 encoded")
|
|
|
|
store = Store(session.shared_db)
|
|
imported = 0
|
|
errors: list[str] = []
|
|
reader = csv.DictReader(io.StringIO(text))
|
|
|
|
# Accept both full-export format (has 'platform' col) and simple format (no 'platform' col).
|
|
for i, row in enumerate(reader, start=2):
|
|
seller_id = (row.get("platform_seller_id") or "").strip()
|
|
username = (row.get("username") or "").strip()
|
|
if not seller_id or not username:
|
|
errors.append(f"Row {i}: missing platform_seller_id or username — skipped")
|
|
continue
|
|
platform = (row.get("platform") or "ebay").strip()
|
|
reason = (row.get("reason") or "").strip() or None
|
|
store.add_to_blocklist(ScammerEntry(
|
|
platform=platform,
|
|
platform_seller_id=seller_id,
|
|
username=username,
|
|
reason=reason,
|
|
source="csv_import",
|
|
))
|
|
imported += 1
|
|
|
|
log.info("Blocklist import: %d added, %d errors", imported, len(errors))
|
|
return {"imported": imported, "errors": errors}
|