import gradio as gr
import pandas as pd
import numpy as np
import joblib
import os

# Load the saved model pipeline
model_path = 'credit_risk_assessment_model.pkl'
if os.path.exists(model_path):
    model = joblib.load(model_path)
    print(f"✅ Model loaded successfully from {model_path}")
else:
    print(f"⚠️ Model file not found at {model_path}. Upload it to this Space.")
    model = None

# ---- HELPER FUNCTIONS ----
def get_age_group(age):
    if age < 30: return '20-30'
    elif age < 40: return '30-40'
    elif age < 50: return '40-50'
    elif age < 60: return '50-60'
    elif age < 70: return '60-70'
    else: return '70+'

def get_credit_amount_group(amount):
    if amount < 2000: return 'Low'
    elif amount < 5000: return 'Medium'
    elif amount < 10000: return 'High'
    else: return 'Very High'

def get_duration_group(duration):
    if duration <= 12: return 'Short'
    elif duration <= 36: return 'Medium'
    else: return 'Long'

def get_employment_stability(emp):
    return {
        'A71': 'Unstable', 'A72': 'Unstable', 'A73': 'Moderate',
        'A74': 'Stable', 'A75': 'Very Stable'
    }.get(emp, 'Moderate')

def get_savings_status(savings):
    return {
        'A61': 'None/Low', 'A62': 'Moderate', 'A63': 'Moderate',
        'A64': 'High', 'A65': 'None/Low'
    }.get(savings, 'None/Low')

def get_credit_history_simple(history):
    return {
        'A30': 'Poor', 'A31': 'Good', 'A32': 'Good',
        'A33': 'Fair', 'A34': 'Poor'
    }.get(history, 'Fair')

def calculate_risk_flags(age, credit_amount, duration, checking_account):
    return {
        'young_high_credit_flag': int(age < 30 and credit_amount > 5000),
        'high_exposure_flag': int(credit_amount > 7500 and duration > 24),
        'critical_high_amount_flag': int(credit_amount > 10000),
        'no_checking_high_credit_flag': int(checking_account == 'A14' and credit_amount > 5000),
        'checking_risk': int(checking_account in ['A13', 'A14'])
    }

def calculate_additional_risk_flags(credit_history, savings_account):
    history_risk = int(credit_history in ['A30', 'A34'])
    savings_risk = int(savings_account in ['A61', 'A65'])
    combined_account_risk = history_risk + savings_risk
    return {
        'history_risk': history_risk,
        'savings_risk': savings_risk,
        'combined_account_risk': combined_account_risk
    }

# ---- PREDICTION WRAPPER ----
def predict_credit_risk(checking_account, duration, credit_history, purpose, credit_amount, savings_account, employment_since, installment_rate, personal_status_sex, other_debtors, present_residence, property, age, other_installment_plans, housing, number_credits, job, people_liable, telephone, foreign_worker):
    # If model isn't loaded, show error
    if model is None:
        return """
        <div style='padding: 1rem; border-radius: 0.5rem; background-color: #f44336; color: white;'>
            <h2>Error: Model not loaded</h2>
            <p>The credit risk model has not been loaded. Please check the server logs.</p>
        </div>
        """
    
    try:
        # Calculate derived features
        age_group = get_age_group(age)
        credit_amount_group = get_credit_amount_group(credit_amount)
        duration_group = get_duration_group(duration)
        employment_stability = get_employment_stability(employment_since)
        savings_status = get_savings_status(savings_account)
        credit_history_simple = get_credit_history_simple(credit_history)
        credit_per_month = credit_amount / duration if duration > 0 else 0
        age_to_credit_ratio = credit_amount / age if age > 0 else 0
        debt_burden = credit_per_month * 100 / 2000
        credit_to_duration_ratio = credit_amount / duration if duration > 0 else 0
        
        # Calculate risk flags
        risk_flags = calculate_risk_flags(age, credit_amount, duration, checking_account)
        additional_flags = calculate_additional_risk_flags(credit_history, savings_account)
        
        # Create input data dictionary with all features
        input_data = {
            'index': 0,  # Add index column to fix the error
            'checking_account': checking_account,
            'duration': duration,
            'credit_history': credit_history,
            'purpose': purpose,
            'credit_amount': credit_amount,
            'savings_account': savings_account,
            'employment_since': employment_since,
            'installment_rate': installment_rate,
            'personal_status_sex': personal_status_sex,
            'other_debtors': other_debtors,
            'present_residence': present_residence,
            'property': property,
            'age': age,
            'other_installment_plans': other_installment_plans,
            'housing': housing,
            'number_credits': number_credits,
            'job': job,
            'people_liable': people_liable,
            'telephone': telephone,
            'foreign_worker': foreign_worker,
            'age_group': age_group,
            'credit_amount_group': credit_amount_group,
            'duration_group': duration_group,
            'credit_per_month': credit_per_month,
            'employment_stability': employment_stability,
            'savings_status': savings_status,
            'credit_history_simple': credit_history_simple,
            'age_to_credit_ratio': age_to_credit_ratio,
            'debt_burden': debt_burden,
            'credit_to_duration_ratio': credit_to_duration_ratio,
            'duration_history_interaction': int(duration > 24 and credit_history in ['A30', 'A33', 'A34']),
            'amount_checking_interaction': int(credit_amount > 5000 and checking_account in ['A13', 'A14']),
            **risk_flags,
            **additional_flags
        }

        # Convert to DataFrame for prediction
        df = pd.DataFrame([input_data])
        
        # Make prediction using the pipeline
        try:
            # For debugging
            print(f"Input DataFrame shape: {df.shape}")
            print(f"Input DataFrame columns: {df.columns.tolist()}")
            
            y_proba = model.predict_proba(df)[0][1]
            
            # Determine risk level based on probability
            if y_proba > 0.7:
                risk = "High Risk"
                color = "#f44336"  # Red
                approval = "Loan Rejected"
                icon = "❌"
            elif y_proba > 0.4:
                risk = "Medium Risk"
                color = "#ff9800"  # Orange
                approval = "Further Review Required"
                icon = "⚠️"
            else:
                risk = "Low Risk"
                color = "#4caf50"  # Green
                approval = "Loan Approved"
                icon = "✅"
            
            # Format a detailed response
            return f"""
            <div style='padding: 1.5rem; border-radius: 0.5rem; background-color: {color}; color: white;'>
                <h2 style='margin-top: 0;'>{icon} {risk}: {approval}</h2>
                <p style='font-size: 1.2rem;'>Risk Score: {y_proba:.2%}</p>
                <hr style='border-color: rgba(255,255,255,0.3);'>
                <div style='margin-top: 1rem;'>
                    <p><strong>Key Risk Factors:</strong></p>
                    <ul>
                        <li>Credit Amount: £{credit_amount:,.2f} ({credit_amount_group})</li>
                        <li>Loan Duration: {duration} months ({duration_group})</li>
                        <li>Monthly Payment: £{credit_per_month:,.2f}</li>
                        <li>Credit History: {credit_history_simple}</li>
                        <li>Debt Burden: {debt_burden:.2f}%</li>
                    </ul>
                </div>
            </div>
            """
                
        except Exception as inner_e:
            print(f"Prediction error: {inner_e}")
            print(f"DataFrame columns: {df.columns.tolist()}")
            return f"""
            <div style='padding: 1rem; border-radius: 0.5rem; background-color: #f44336; color: white;'>
                <h2>Error in Prediction</h2>
                <p>{str(inner_e)}</p>
                <p>Please check the server logs for details.</p>
            </div>
            """
            
    except Exception as e:
        print(f"Error in processing: {e}")
        return f"""
        <div style='padding: 1rem; border-radius: 0.5rem; background-color: #f44336; color: white;'>
            <h2>Error Processing Request</h2>
            <p>{str(e)}</p>
            <p>Please check the server logs for details.</p>
        </div>
        """