# model.py

def score_opportunity(data):
    # Stage weights for scoring
    stage_weight = {
        "Prospecting": 10,
        "Qualified": 20,
        "Proposal": 30,
        "Proposal/Price Quote": 35,
        "Negotiation": 40,
        "Closed Won": 50,
        "Closed Lost": 0
    }

    lead_score = data.get("lead_score", 0)
    email_score = min(10, data.get("emails_last_7_days", 0)) * 2    # up to 20
    meeting_score = min(5, data.get("meetings_last_30_days", 0)) * 5  # up to 25
    amount_score = min(data.get("amount", 0) / 1000, 25)              # up to 25
    stage_score = stage_weight.get(data.get("stage"), 0)

    # Total Score Calculation
    total_score = lead_score * 0.25 + email_score + meeting_score + amount_score + stage_score
    total_score = round(min(total_score, 100))

    # Confidence (0.0 to 1.0)
    confidence = round(
        min(1.0, (
            (lead_score / 100) * 0.5 +
            min(1, data.get("emails_last_7_days", 0) / 10) * 0.25 +
            min(1, data.get("meetings_last_30_days", 0) / 5) * 0.25
        )),
        2
    )

    # Risk level and AI recommendation
    if total_score >= 80:
        risk = "Low"
        recommendation = "🔥 Strong lead. Schedule final meeting or send proposal."
    elif total_score >= 60:
        risk = "Medium"
        recommendation = "🗓️ Schedule another meeting before sending proposal."
    elif total_score >= 40:
        risk = "High"
        recommendation = "📞 Reconnect with lead. Increase engagement."
    else:
        risk = "Very High"
        recommendation = "⚠️ Low potential. Reassess or de-prioritize."

    return {
        "score": total_score,
        "confidence": confidence,
        "risk": risk,
        "recommendation": recommendation
    }