56 lines
2 KiB
Python
56 lines
2 KiB
Python
"""Composite score and red flag extraction."""
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from __future__ import annotations
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import json
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from typing import Optional
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from app.db.models import Seller, TrustScore
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HARD_FILTER_AGE_DAYS = 7
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HARD_FILTER_BAD_RATIO_MIN_COUNT = 20
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HARD_FILTER_BAD_RATIO_THRESHOLD = 0.80
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class Aggregator:
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def aggregate(
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self,
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signal_scores: dict[str, Optional[int]],
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photo_hash_duplicate: bool,
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seller: Optional[Seller],
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listing_id: int = 0,
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) -> TrustScore:
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is_partial = any(v is None for v in signal_scores.values())
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clean = {k: (v if v is not None else 0) for k, v in signal_scores.items()}
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composite = sum(clean.values())
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red_flags: list[str] = []
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# Hard filters
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if seller and seller.account_age_days < HARD_FILTER_AGE_DAYS:
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red_flags.append("new_account")
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if seller and (
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seller.feedback_ratio < HARD_FILTER_BAD_RATIO_THRESHOLD
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and seller.feedback_count > HARD_FILTER_BAD_RATIO_MIN_COUNT
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):
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red_flags.append("established_bad_actor")
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# Soft flags
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if seller and seller.account_age_days < 30:
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red_flags.append("account_under_30_days")
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if seller and seller.feedback_count < 10:
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red_flags.append("low_feedback_count")
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if clean["price_vs_market"] == 0:
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red_flags.append("suspicious_price")
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if photo_hash_duplicate:
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red_flags.append("duplicate_photo")
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return TrustScore(
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listing_id=listing_id,
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composite_score=composite,
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account_age_score=clean["account_age"],
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feedback_count_score=clean["feedback_count"],
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feedback_ratio_score=clean["feedback_ratio"],
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price_vs_market_score=clean["price_vs_market"],
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category_history_score=clean["category_history"],
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photo_hash_duplicate=photo_hash_duplicate,
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red_flags_json=json.dumps(red_flags),
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score_is_partial=is_partial,
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)
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