pyr0ball
11f2a3c2b3
- Two sidebar fields: 'Must include' and 'Must exclude' (comma-separated) - Must-exclude terms forwarded to eBay _nkw as -term prefixes (native eBay support) so exclusions reduce the eBay result set at the source — improves market comp quality as a side effect - Must-include applied client-side only (substring, case-insensitive) - Both applied client-side via passesFilter() for instant response without re-fetching (cache-friendly) - Exclude input has subtle red border tint (color-mix) to signal intent - Hint text: 're-search to apply to eBay' reminds user negatives need a new search to take effect at the eBay level
116 lines
3.9 KiB
Python
116 lines
3.9 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 pathlib import Path
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from fastapi import FastAPI, HTTPException
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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.platforms import SearchFilters
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from app.platforms.ebay.scraper import ScrapedEbayAdapter
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from app.trust import TrustScorer
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load_env(Path(".env"))
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log = logging.getLogger(__name__)
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_DB_PATH = Path(os.environ.get("SNIPE_DB", "data/snipe.db"))
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_DB_PATH.parent.mkdir(exist_ok=True)
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app = FastAPI(title="Snipe API", version="0.1.0")
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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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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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@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 = "", # comma-separated; applied client-side only
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must_exclude: str = "", # comma-separated; forwarded to eBay AND applied client-side
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):
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if not q.strip():
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return {"listings": [], "trust_scores": {}, "sellers": {}, "market_price": None}
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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=max(1, pages),
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must_include=_parse_terms(must_include),
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must_exclude=_parse_terms(must_exclude),
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)
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# Each adapter gets its own Store (SQLite connection) — required for thread safety.
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# search() and get_completed_sales() run concurrently; they write to different tables
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# so SQLite file-level locking is the only contention point.
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search_adapter = ScrapedEbayAdapter(Store(_DB_PATH))
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comps_adapter = ScrapedEbayAdapter(Store(_DB_PATH))
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try:
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with ThreadPoolExecutor(max_workers=2) as ex:
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listings_future = ex.submit(search_adapter.search, q, filters)
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comps_future = ex.submit(comps_adapter.get_completed_sales, q, pages)
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listings = listings_future.result()
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comps_future.result() # wait; side-effect is saving market comp to 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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# Use search_adapter's store for post-processing — it has the sellers already written
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store = search_adapter._store
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store.save_listings(listings)
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scorer = TrustScorer(store)
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trust_scores_list = scorer.score_batch(listings, q)
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# Market comp written by comps_adapter — read from a fresh connection to avoid
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# cross-thread connection reuse
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comp_store = Store(_DB_PATH)
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query_hash = hashlib.md5(q.encode()).hexdigest()
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comp = comp_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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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 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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}
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