from datetime import datetime, timedelta
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select, func
from app.models.bet import Bet
from app.models.bankroll import Bankroll

async def calculate_risk_score(user_id, db: AsyncSession) -> dict:
    score = 0
    factors = []
    now = datetime.utcnow()
    
    # 1. Horário noturno (22h-5h) +15
    hour = now.hour
    if hour >= 22 or hour < 5:
        score += 15
        factors.append("Apostando em horário noturno")
    
    # 2. Valor crescente +25
    recent = await db.execute(
        select(Bet).where(Bet.user_id == user_id).order_by(Bet.created_at.desc()).limit(5)
    )
    recent_bets = recent.scalars().all()
    if len(recent_bets) >= 3:
        amounts = [float(b.amount) for b in recent_bets[:3]]
        if amounts[0] > amounts[1] > amounts[2]:
            score += 25
            factors.append("Valores de aposta crescentes")
    
    # 3. Recuperação (aposta logo após loss) +30
    if len(recent_bets) >= 2:
        last = recent_bets[0]
        prev = recent_bets[1]
        if prev.result == "loss" and last.created_at and prev.created_at:
            diff = (last.created_at - prev.created_at).total_seconds()
            if diff < 1800:  # 30 min
                score += 30
                factors.append("Tentativa de recuperação após perda")
    
    # 4. Frequência alta (>5 apostas em 24h) +20
    day_ago = now - timedelta(hours=24)
    count_result = await db.execute(
        select(func.count()).select_from(Bet).where(Bet.user_id == user_id, Bet.created_at >= day_ago)
    )
    bet_count = count_result.scalar() or 0
    if bet_count > 5:
        score += 20
        factors.append(f"Alta frequência: {bet_count} apostas em 24h")
    
    # 5. Limite estourado +25
    br_result = await db.execute(select(Bankroll).where(Bankroll.user_id == user_id))
    bankroll = br_result.scalar_one_or_none()
    if bankroll and float(bankroll.month_spent) > float(bankroll.monthly_budget):
        score += 25
        factors.append("Limite mensal ultrapassado")
    
    # 6. Emoção negativa +15
    if recent_bets and recent_bets[0].emotion in ["😤", "😰", "tilt", "ansioso", "frustrado"]:
        score += 15
        factors.append("Emoção negativa detectada")
    
    # 7. Sequência de perdas +20
    loss_streak = 0
    for b in recent_bets:
        if b.result == "loss":
            loss_streak += 1
        else:
            break
    if loss_streak >= 3:
        score += 20
        factors.append(f"Sequência de {loss_streak} perdas consecutivas")
    
    if score <= 30:
        level = "green"
    elif score <= 60:
        level = "yellow"
    else:
        level = "red"
    
    return {"score": score, "level": level, "factors": factors}