pyr0ball
eb05be0612
- ci.yml: API lint (ruff F+I) + pytest, web vue-tsc + vitest + build - mirror.yml: push to GitHub (CircuitForgeLLC) + Codeberg (CircuitForge) on main/tags - release.yml: Docker build → Forgejo registry + release via API; GHCR deferred pending BSL policy (cf-agents#3) - .cliff.toml: git-cliff changelog config for semver releases - pyproject.toml: add [dev] extras (pytest, ruff), ruff config - Fix 45 ruff violations across codebase (import sorting, unused vars, unused imports)
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
import hashlib
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import math
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from app.db.models import Listing, TrustScore
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from app.db.models import Seller as Seller
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from app.db.store import Store
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from .aggregator import Aggregator
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from .metadata import MetadataScorer
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from .photo import PhotoScorer
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class TrustScorer:
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"""Orchestrates metadata + photo scoring for a batch of listings."""
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def __init__(self, shared_store: Store):
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self._store = shared_store
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self._meta = MetadataScorer()
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self._photo = PhotoScorer()
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self._agg = Aggregator()
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def score_batch(
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self,
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listings: list[Listing],
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query: str,
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) -> list[TrustScore]:
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query_hash = hashlib.md5(query.encode()).hexdigest()
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comp = self._store.get_market_comp("ebay", query_hash)
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market_median = comp.median_price if comp else None
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# Coefficient of variation: stddev/mean across batch prices.
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# None when fewer than 2 priced listings (can't compute variance).
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_prices = [l.price for l in listings if l.price > 0]
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if len(_prices) >= 2:
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_mean = sum(_prices) / len(_prices)
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_stddev = math.sqrt(sum((p - _mean) ** 2 for p in _prices) / len(_prices))
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price_cv: float | None = _stddev / _mean if _mean > 0 else None
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else:
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price_cv = None
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photo_url_sets = [l.photo_urls for l in listings]
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duplicates = self._photo.check_duplicates(photo_url_sets)
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scores = []
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for listing, is_dup in zip(listings, duplicates):
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seller = self._store.get_seller("ebay", listing.seller_platform_id)
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blocklisted = self._store.is_blocklisted("ebay", listing.seller_platform_id)
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if seller:
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signal_scores = self._meta.score(seller, market_median, listing.price, price_cv)
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else:
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signal_scores = {k: None for k in
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["account_age", "feedback_count", "feedback_ratio",
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"price_vs_market", "category_history"]}
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trust = self._agg.aggregate(
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signal_scores, is_dup, seller,
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listing_id=listing.id or 0,
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listing_title=listing.title,
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listing_condition=listing.condition,
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times_seen=listing.times_seen,
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first_seen_at=listing.first_seen_at,
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price=listing.price,
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price_at_first_seen=listing.price_at_first_seen,
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is_blocklisted=blocklisted,
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)
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scores.append(trust)
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return scores
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