pyr0ball
98695b00f0
Core trust scoring: - Five metadata signals (account age, feedback count/ratio, price vs market, category history), composited 0–100 - CV-based price signal suppression for heterogeneous search results (e.g. mixed laptop generations won't false-positive suspicious_price) - Expanded scratch/dent title detection: evasive redirects, functional problem phrases, DIY/repair indicators - Hard filters: new_account, established_bad_actor - Soft flags: low_feedback, suspicious_price, duplicate_photo, scratch_dent, long_on_market, significant_price_drop Search & filtering: - Browse API adapter (up to 200 items/page) + Playwright scraper fallback - OR-group query expansion for comprehensive variant coverage - Must-include (AND/ANY/groups), must-exclude, category, price range filters - Saved searches with full filter round-trip via URL params Seller enrichment: - Background BTF /itm/ scraping for account age (Kasada-safe headed Chromium) - On-demand enrichment: POST /api/enrich + ListingCard ↻ button - Category history derived from Browse API categories field (free, no extra calls) - Shopping API GetUserProfile inline enrichment for API adapter Market comps: - eBay Marketplace Insights API with Browse API fallback (catches 403 + 404) - Comps prioritised in ThreadPoolExecutor (submitted first) Infrastructure: - Staging DB fields: times_seen, first_seen_at, price_at_first_seen, category_name - Migrations 004 (staging tracking) + 005 (listing category) - eBay webhook handler stub - Cloud compose stack (compose.cloud.yml) - Vue frontend: search store, saved searches store, ListingCard, filter sidebar Docs: - README fully rewritten to reflect MVP status + full feature documentation - Roadmap table linked to all 13 Forgejo issues
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
"""Convert raw eBay API responses into Snipe domain objects."""
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from __future__ import annotations
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import json
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from datetime import datetime, timezone
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from typing import Optional
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from app.db.models import Listing, Seller
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def normalise_listing(raw: dict) -> Listing:
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price_data = raw.get("price", {})
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photos = []
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if "image" in raw:
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photos.append(raw["image"].get("imageUrl", ""))
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for img in raw.get("additionalImages", []):
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url = img.get("imageUrl", "")
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if url and url not in photos:
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photos.append(url)
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photos = [p for p in photos if p]
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listing_age_days = 0
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created_raw = raw.get("itemCreationDate", "")
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if created_raw:
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try:
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created = datetime.fromisoformat(created_raw.replace("Z", "+00:00"))
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listing_age_days = (datetime.now(timezone.utc) - created).days
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except ValueError:
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pass
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options = raw.get("buyingOptions", [])
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if "AUCTION" in options:
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buying_format = "auction"
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elif "BEST_OFFER" in options:
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buying_format = "best_offer"
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else:
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buying_format = "fixed_price"
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ends_at = None
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end_raw = raw.get("itemEndDate", "")
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if end_raw:
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try:
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ends_at = datetime.fromisoformat(end_raw.replace("Z", "+00:00")).isoformat()
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except ValueError:
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pass
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# Leaf category is categories[0] (most specific); parent path follows.
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categories = raw.get("categories", [])
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category_name: Optional[str] = categories[0]["categoryName"] if categories else None
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seller = raw.get("seller", {})
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return Listing(
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platform="ebay",
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platform_listing_id=raw["itemId"],
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title=raw.get("title", ""),
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price=float(price_data.get("value", 0)),
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currency=price_data.get("currency", "USD"),
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condition=raw.get("condition", "").lower(),
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seller_platform_id=seller.get("username", ""),
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url=raw.get("itemWebUrl", ""),
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photo_urls=photos,
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listing_age_days=listing_age_days,
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buying_format=buying_format,
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ends_at=ends_at,
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category_name=category_name,
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)
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def normalise_seller(raw: dict) -> Seller:
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feedback_pct = float(raw.get("feedbackPercentage", "0").strip("%")) / 100.0
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account_age_days: Optional[int] = None # None = registrationDate not in API response
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reg_date_raw = raw.get("registrationDate", "")
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if reg_date_raw:
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try:
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reg_date = datetime.fromisoformat(reg_date_raw.replace("Z", "+00:00"))
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account_age_days = (datetime.now(timezone.utc) - reg_date).days
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except ValueError:
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pass
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category_history = {}
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summary = raw.get("sellerFeedbackSummary", {})
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for entry in summary.get("feedbackByCategory", []):
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category_history[entry.get("categorySite", "")] = int(entry.get("count", 0))
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return Seller(
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platform="ebay",
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platform_seller_id=raw["username"],
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username=raw["username"],
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account_age_days=account_age_days,
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feedback_count=int(raw.get("feedbackScore", 0)),
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feedback_ratio=feedback_pct,
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category_history_json=json.dumps(category_history),
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)
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