Create app.py

app.py

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+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>
+        """