scoring_engine.py

#!/usr/bin/env python3
import json
import math
import sys
from datetime import datetime


def read_payload():
    raw = sys.stdin.read().strip()
    if not raw:
        raise ValueError("Missing scoring payload.")
    return json.loads(raw)


def safe_parse_date(value):
    if not value:
        return None
    return datetime.strptime(value, "%Y-%m-%d")


def build_score(payload):
    ventes = payload.get("ventes", [])
    boutique = payload.get("commercant", {})

    count = len(ventes)
    volume = sum(float(vente.get("montant", 0) or 0) for vente in ventes)
    average = volume / count if count else 0.0

    dates = [safe_parse_date(vente.get("date_vente")) for vente in ventes if vente.get("date_vente")]
    dates = [date for date in dates if date is not None]
    activity_span = max((dates[-1] - dates[0]).days, 0) if len(dates) >= 2 else 0
    consistency_bonus = min(10.0, activity_span / 3.0)

    article_diversity = len({(vente.get("article") or "").strip().lower() for vente in ventes if vente.get("article")})
    diversity_bonus = min(8.0, article_diversity * 1.5)

    profile_bonus = 4.0 if len((boutique.get("boutique_info") or "").strip()) >= 40 else 0.0

    score = (
        38.0
        + min(25.0, count * 3.5)
        + min(18.0, volume / 220.0)
        + min(12.0, average / 90.0)
        + consistency_bonus
        + diversity_bonus
        + profile_bonus
    )

    score = round(max(0.0, min(96.0, score)), 1)
    mention = "Eligible" if score >= 70 else "A consolider"

    return {
        "score": score,
        "mention": mention,
        "metrics": {
            "sales_count": count,
            "volume": round(volume, 2),
            "average_ticket": round(average, 2),
            "activity_span_days": activity_span,
            "article_diversity": article_diversity,
        },
    }


def main():
    payload = read_payload()
    result = build_score(payload)
    sys.stdout.write(json.dumps(result))


if __name__ == "__main__":
    main()