import os
from datetime import datetime, timedelta
from sqlalchemy.orm import Session
from app.database.models import User, Account, Transaction, Goal, Investment, Subscription
from app.ai.forecasting import get_cashflow_metrics
from app.ai.ollama_integration import get_ai_response

def calculate_financial_health_score(db: Session, user_id: str):
    """
    Computes a multi-dimensional Financial Health Score (0-100) based on real database records.
    """
    accounts = db.query(Account).filter(Account.user_id == user_id).all()
    total_balance = sum(acc.balance for acc in accounts)
    savings_balance = sum(acc.balance for acc in accounts if acc.type.lower() == "savings")
    
    # Cashflow
    current_balance, daily_income, daily_spending = get_cashflow_metrics(db, user_id)
    monthly_income = max(1000.0, daily_income * 30.4)
    monthly_spending = daily_spending * 30.4
    
    # 1. Savings Consistency (20 pts)
    # Check frequency of saving transactions or goal additions
    txns = db.query(Transaction).join(Account).filter(
        Account.user_id == user_id,
        Transaction.type == "credit",
        Transaction.category == "Income"
    ).count()
    # Let's say if they have active goals with current_amount > 0, they get higher points
    goals = db.query(Goal).filter(Goal.user_id == user_id).all()
    goal_savings = sum(g.current_amount for g in goals)
    
    savings_score = 10.0
    if goal_savings > 1000:
        savings_score += 10.0
    elif goal_savings > 0:
        savings_score += 5.0
        
    # 2. Debt Ratio (20 pts)
    # Estimate EMIs or goals with "debt"
    debt_goals = sum(g.target_amount - g.current_amount for g in goals if "debt" in g.title.lower() or "loan" in g.title.lower())
    # Standard monthly debt service (estimate 10% of debt or $150 minimum if debt exists)
    est_monthly_debt = max(0.0, debt_goals * 0.05)
    debt_to_income = est_monthly_debt / monthly_income
    
    debt_score = 20.0
    if debt_to_income > 0.40:
        debt_score = 5.0
    elif debt_to_income > 0.20:
        debt_score = 12.0
    elif debt_to_income > 0.05:
        debt_score = 18.0
        
    # 3. Spending Discipline (20 pts)
    # Ratio of monthly spending to monthly income
    savings_rate = (monthly_income - monthly_spending) / monthly_income if monthly_income > 0 else 0
    discipline_score = 10.0
    if savings_rate >= 0.30:
        discipline_score = 20.0
    elif savings_rate >= 0.15:
        discipline_score = 16.0
    elif savings_rate >= 0.0:
        discipline_score = 12.0
        
    # 4. Emergency Fund (20 pts)
    # Do they have 3-6 months of expenses in savings?
    monthly_expenses = max(500.0, monthly_spending)
    months_buffer = savings_balance / monthly_expenses
    
    emergency_score = 0.0
    if months_buffer >= 6.0:
        emergency_score = 20.0
    elif months_buffer >= 3.0:
        emergency_score = 15.0
    elif months_buffer >= 1.0:
        emergency_score = 8.0
        
    # 5. Investment Index (10 pts)
    investments = db.query(Investment).filter(Investment.user_id == user_id).all()
    inv_total = sum(i.current_value for i in investments)
    
    investment_score = 0.0
    if inv_total > 5000:
        investment_score = 10.0
    elif inv_total > 0:
        investment_score = 6.0
        
    # 6. Subscription Efficiency (10 pts)
    subs = db.query(Subscription).filter(Subscription.user_id == user_id, Subscription.active == True).all()
    sub_cost = sum(s.amount if s.billing_cycle.lower() == "monthly" else (s.amount / 12) for s in subs)
    sub_ratio = sub_cost / monthly_income
    
    sub_score = 10.0
    if sub_ratio > 0.10:  # More than 10% of income on subscriptions
        sub_score = 3.0
    elif sub_ratio > 0.05: # More than 5%
        sub_score = 7.0
        
    # Calculate Overall Score
    overall_score = savings_score + debt_score + discipline_score + emergency_score + investment_score + sub_score
    overall_score = min(100.0, max(0.0, overall_score))
    
    # Actionable improvements list
    improvements = []
    if savings_score < 15:
        improvements.append("Set up automated transfers to your Savings account right after payday.")
    if debt_score < 15:
        improvements.append("Prioritize high-interest debt payoffs using the debt avalanche method.")
    if discipline_score < 15:
        improvements.append("Discretionary spending (shopping & dining) is high. Try implementing a $50 weekly limit.")
    if emergency_score < 15:
        improvements.append(f"Build savings buffer. Try to accumulate at least ${monthly_expenses * 3:,.2f} (3 months of expenses).")
    if investment_score < 6:
        improvements.append("Start a low-cost stock index fund SIP to counter inflation.")
    if sub_score < 8:
        improvements.append("Conduct an audit of active subscriptions. Cancel duplicate/unused memberships.")
        
    if not improvements:
        improvements.append("Maintain your current financial habits; your portfolio is highly optimized!")
        
    # AI Explanation
    user = db.query(User).filter(User.id == user_id).first()
    persona = user.financial_personality if user else "Saver"
    
    ai_prompt = f"""
    The user is a '{persona}' with a Financial Health Score of {overall_score:.0f}/100.
    Sub-scores:
    - Savings Consistency: {savings_score:.0f}/20
    - Debt Management: {debt_score:.0f}/20
    - Spending Discipline: {discipline_score:.0f}/20
    - Emergency Fund: {emergency_score:.0f}/20
    - Investment Allocation: {investment_score:.0f}/10
    - Subscription Management: {sub_score:.0f}/10
    
    Write a concise, professional financial analyst explanation of this score. Detail the primary strengths and key weaknesses.
    Do NOT write a generic chatbot reply. Keep it to 3-4 sentences. Format like a Bloomberg analyst report.
    """
    
    from app.ai.ollama_integration import has_active_ai_backend
    
    explanation = None
    if has_active_ai_backend():
        try:
            # Hard 8-second timeout so the health score endpoint never hangs
            import threading
            result = [None]
            def _call():
                result[0] = get_ai_response(ai_prompt)
            t = threading.Thread(target=_call, daemon=True)
            t.start()
            t.join(timeout=8)
            explanation = result[0]
        except Exception:
            pass
            
    if not explanation:
        explanation = f"As a {persona}, your financial health score of {overall_score:.0f} reflects solid fundamentals with opportunities to optimize emergency allocations and subscription efficiencies. Focus on automating savings and expanding investments."
        
    return {
        "overall_score": round(overall_score, 0),
        "categories": {
            "savings_consistency": {"score": round(savings_score, 0), "max": 20},
            "debt_ratio": {"score": round(debt_score, 0), "max": 20},
            "spending_discipline": {"score": round(discipline_score, 0), "max": 20},
            "emergency_funds": {"score": round(emergency_score, 0), "max": 20},
            "investments": {"score": round(investment_score, 0), "max": 10},
            "subscription_management": {"score": round(sub_score, 0), "max": 10}
        },
        "explanation": explanation,
        "actionable_improvements": improvements
    }

def generate_daily_briefing(db: Session, user_id: str):
    """
    Pulls complete financial context and generates a personalized daily financial briefing.
    """
    user = db.query(User).filter(User.id == user_id).first()
    if not user:
        return {"briefing": "User not found."}
        
    # Collect data
    accounts = db.query(Account).filter(Account.user_id == user_id).all()
    total_balance = sum(acc.balance for acc in accounts)
    
    goals = db.query(Goal).filter(Goal.user_id == user_id).all()
    goals_summary = [f"{g.title}: {g.current_amount}/{g.target_amount}" for g in goals]
    
    investments = db.query(Investment).filter(Investment.user_id == user_id).all()
    inv_summary = [f"{i.asset_name} ({i.type}): Current Value ${i.current_value:,.2f}" for i in investments]
    
    # Cashflow
    current_balance, daily_income, daily_spending = get_cashflow_metrics(db, user_id)
    monthly_income = daily_income * 30.4
    monthly_spending = daily_spending * 30.4
    
    # Format AI Prompt
    ai_prompt = f"""
    You are an AI Wealth Advisor and Predictive Banking Engine. Generate a personalized daily financial briefing for {user.profile_data.get('name', 'User')}.
    
    Financial Summary:
    - User Personality: {user.financial_personality}
    - Total Account Balance: ${total_balance:,.2f}
    - Estimated Monthly Income: ${monthly_income:,.2f}
    - Estimated Monthly Spending: ${monthly_spending:,.2f}
    - Active Goals: {', '.join(goals_summary) if goals_summary else 'None'}
    - Investments: {', '.join(inv_summary) if inv_summary else 'None'}
    
    Generate a 3-paragraph daily briefing.
    Paragraph 1: Summary of their current liquidity and portfolio health.
    Paragraph 2: One specific recommendation regarding their savings goals or investment potential.
    Paragraph 3: A behavioral spending insight warning based on their spending velocity.
    
    Style: Bloomberg Terminal style, highly intelligent, concise, financially meaningful, human-like.
    Avoid boilerplate generic remarks (e.g. 'You should try saving more money'). Use exact figures.
    """
    
    from app.ai.ollama_integration import has_active_ai_backend
    
    briefing = None
    if has_active_ai_backend():
        try:
            import threading
            result = [None]
            def _call():
                result[0] = get_ai_response(ai_prompt)
            t = threading.Thread(target=_call, daemon=True)
            t.start()
            t.join(timeout=10)
            briefing = result[0]
        except Exception:
            pass
            
    if not briefing:
        briefing = f"DAILY BRIEFING:\n\nYour liquid capital stands at ${total_balance:,.2f}. Portfolio indicators suggest regular cashflow velocity. Based on your {user.financial_personality} profile, we advise dedicating a portion of your net surplus to your active goals to optimize compound growth. Avoid non-essential weekend dining and retail spikes to maintain your target trajectory."
        
    return {
        "date": datetime.utcnow().strftime("%Y-%m-%d"),
        "user_name": user.profile_data.get('name', 'User'),
        "briefing": briefing,
        "metrics": {
            "total_liquid_capital": round(total_balance, 2),
            "monthly_income_projection": round(monthly_income, 2),
            "monthly_burn_rate": round(monthly_spending, 2)
        }
    }