"""Supplier scoring.

Weighted score (0-100):
    40% price       — cheaper supplier cost vs. the eligible pool
    20% inventory   — stock sufficiency for the requested qty
    15% speed       — shipping_time ('3-5 days' -> avg days)
    10% fulfillment — supplier_performance.fulfillment_rate
    10% returns     — inverse of supplier_performance.return_rate
     5% history     — reliability_score blended with stock_accuracy
"""
import re


def _num(value, default=0.0):
    try:
        return float(value or 0)
    except (TypeError, ValueError):
        return default


def _shipping_days(sp):
    text = (sp.shipping_time or "").strip()
    numbers = re.findall(r"\d+", text)
    if not numbers:
        return 5.0
    return float(sum(int(n) for n in numbers)) / len(numbers)


def score_supplier(db, supplier, sp, qty, price_pool=None):
    perf = supplier.performance

    # 1) price (40%) — cheaper than pool average is better
    cost = _num(sp.supplier_cost)
    if price_pool and price_pool.get("avg_cost"):
        avg = price_pool["avg_cost"]
        price_score = max(0.0, 100.0 - (cost / avg - 1.0) * 100.0)
    else:
        price_score = 50.0

    # 2) inventory (20%) — can we fill the quantity?
    inventory = int(sp.supplier_inventory or 0)
    inv_score = 100.0 if inventory >= qty else max(0.0, (inventory / qty) * 100.0)

    # 3) speed (15%) — fewer shipping days is better
    days = _shipping_days(sp)
    speed_score = max(0.0, 100.0 - days * 12.0)

    # 4) fulfillment (10%)
    fulfillment = 100.0 if perf is None else _num(perf.fulfillment_rate, 100.0)

    # 5) returns (10%) — 0% returns = 100 points, 20% returns = 0 points
    return_rate = 0.0 if perf is None else _num(perf.return_rate, 0.0)
    returns_score = max(0.0, 100.0 - return_rate * 5.0)

    # 6) history (5%) — reliability blended with stock accuracy
    reliability = _num(supplier.reliability_score, 50.0)
    if perf is None:
        history_score = reliability
    else:
        history_score = 0.5 * reliability + 0.5 * _num(perf.stock_accuracy, 100.0)

    total = (
        0.40 * price_score
        + 0.20 * inv_score
        + 0.15 * speed_score
        + 0.10 * fulfillment
        + 0.10 * returns_score
        + 0.05 * history_score
    )
    return round(min(100.0, max(0.0, total)), 2)