from collections import defaultdict
from sqlalchemy.orm import Session
from app.database.models import Subscription

def analyze_subscriptions(db: Session, user_id: str):
    """
    Analyzes subscriptions to detect duplicates, unused memberships, and cancellation opportunities.
    """
    subs = db.query(Subscription).filter(
        Subscription.user_id == user_id,
        Subscription.active == True
    ).all()
    
    if not subs:
        return {
            "subscriptions": [],
            "duplicates": [],
            "unused_subscriptions": [],
            "yearly_savings_potential": 0.0,
            "risk_analysis": []
        }
        
    merchant_map = defaultdict(list)
    unused_list = []
    cancellation_suggestions = []
    yearly_savings = 0.0
    risk_analysis = []
    
    for s in subs:
        # Standardize merchant name to detect duplicates
        clean_merchant = s.merchant.strip().lower()
        merchant_map[clean_merchant].append(s)
        
        # Determine cost
        m_cost = s.amount if s.billing_cycle.lower() == "monthly" else (s.amount / 12)
        y_cost = (s.amount * 12) if s.billing_cycle.lower() == "monthly" else s.amount
        
        # Detect unused (if frequency is 'low' or 'none' in usage detection metadata)
        usage = s.ai_usage_detection or {}
        freq = str(usage.get("usage_frequency", "medium")).lower()
        
        if freq in ["low", "none", "unused"]:
            unused_list.append(s)
            cancellation_suggestions.append({
                "subscription_id": s.id,
                "merchant": s.merchant,
                "amount": s.amount,
                "billing_cycle": s.billing_cycle,
                "reason": f"Usage frequency is flagged as '{freq}'.",
                "yearly_savings": round(y_cost, 2)
            })
            yearly_savings += y_cost
            
    # Detect duplicates
    duplicates = []
    for merchant, items in merchant_map.items():
        if len(items) > 1:
            total_cost = sum(x.amount for x in items)
            duplicates.append({
                "merchant": items[0].merchant,
                "count": len(items),
                "items": [
                    {
                        "id": x.id,
                        "amount": x.amount,
                        "billing_cycle": x.billing_cycle
                    }
                    for x in items
                ],
                "recommendation": f"You have {len(items)} active subscriptions for {items[0].merchant}. Consolidate to a single account to save."
            })
            
    # Risk Analysis (utilities vs entertainment)
    essential_categories = ["electricity", "water", "gas", "internet", "phone", "insurance"]
    for s in subs:
        is_essential = any(k in s.merchant.lower() for k in essential_categories)
        if is_essential:
            risk_analysis.append({
                "merchant": s.merchant,
                "risk_level": "high",
                "consequences": "Utility interruption, account reactivation fees, or legal service contract breaches."
            })
        else:
            risk_analysis.append({
                "merchant": s.merchant,
                "risk_level": "low",
                "consequences": "Loss of entertainment streaming access only. Service can be reactivated instantly."
            })
            
    return {
        "subscriptions": [
            {
                "id": s.id,
                "merchant": s.merchant,
                "amount": s.amount,
                "billing_cycle": s.billing_cycle,
                "usage_frequency": s.ai_usage_detection.get("usage_frequency", "medium") if s.ai_usage_detection else "medium"
            }
            for s in subs
        ],
        "duplicates": duplicates,
        "unused_subscriptions": cancellation_suggestions,
        "yearly_savings_potential": round(yearly_savings, 2),
        "risk_analysis": risk_analysis
    }