""" Gradio Demo for Credit Card Fraud Detection This app can be deployed to Hugging Face Spaces """ import gradio as gr import joblib import pandas as pd import numpy as np from datetime import datetime # ============================================================================ # Feature Engineering Function # ============================================================================ def engineer_features(df): """Engineer features for prediction""" # Amount-based features df['amount_log'] = np.log1p(df['amount']) df['amount_zscore'] = (df['amount'] - df['avg_transaction_amount']) / (df['avg_transaction_amount'] + 1) df['is_high_amount'] = (df['amount'] > 1000).astype(int) df['is_round_amount'] = (df['amount'] % 10 == 0).astype(int) # Time-based features df['is_night'] = ((df['time_of_day'] >= 22) | (df['time_of_day'] <= 6)).astype(int) df['is_weekend'] = (df['day_of_week'] >= 5).astype(int) df['is_business_hours'] = ((df['time_of_day'] >= 9) & (df['time_of_day'] <= 17)).astype(int) # Location-based features df['is_far_from_home'] = (df['distance_from_home'] > 50).astype(int) df['unusual_location_change'] = (df['distance_from_last_transaction'] > 100).astype(int) df['location_velocity'] = df['distance_from_last_transaction'] / (df['time_since_last_transaction'] + 0.1) # Velocity features df['rapid_transactions'] = (df['time_since_last_transaction'] < 1).astype(int) df['high_daily_frequency'] = (df['num_transactions_today'] > 5).astype(int) df['high_weekly_frequency'] = (df['num_transactions_last_week'] > 15).astype(int) # Behavioral features df['online_without_card'] = ((df['is_online'] == 1) & (df['card_present'] == 0)).astype(int) df['international_online'] = ((df['is_international'] == 1) & (df['is_online'] == 1)).astype(int) df['new_account'] = (df['account_age_days'] < 90).astype(int) # Risk score df['risk_score'] = ( df['is_night'] * 2 + df['is_far_from_home'] * 3 + df['rapid_transactions'] * 3 + df['high_daily_frequency'] * 2 + df['online_without_card'] * 2 + df['is_international'] * 1 ) return df # ============================================================================ # Load Model # ============================================================================ try: model_data = joblib.load('fraud_model.pkl') MODEL = model_data['model'] SCALER = model_data['scaler'] FEATURE_COLUMNS = model_data['feature_columns'] model_loaded = True except Exception as e: model_loaded = False error_message = str(e) # ============================================================================ # Prediction Function # ============================================================================ def predict_fraud( amount, time_of_day, day_of_week, distance_from_home, distance_from_last_transaction, time_since_last_transaction, num_transactions_today, num_transactions_last_week, merchant_category, is_online, card_present, is_international, avg_transaction_amount, account_age_days ): """Predict if a transaction is fraudulent""" if not model_loaded: return "❌ Model not loaded. Please ensure fraud_model.pkl is in the directory.", "", "", "" try: # Prepare transaction data transaction = { 'amount': amount, 'time_of_day': time_of_day, 'day_of_week': day_of_week, 'distance_from_home': distance_from_home, 'distance_from_last_transaction': distance_from_last_transaction, 'time_since_last_transaction': time_since_last_transaction, 'num_transactions_today': num_transactions_today, 'num_transactions_last_week': num_transactions_last_week, 'merchant_category': merchant_category, 'is_online': 1 if is_online == "Yes" else 0, 'card_present': 1 if card_present == "Yes" else 0, 'is_international': 1 if is_international == "Yes" else 0, 'avg_transaction_amount': avg_transaction_amount, 'account_age_days': account_age_days } # Engineer features transaction_df = pd.DataFrame([transaction]) transaction_df = engineer_features(transaction_df) # Extract features X = transaction_df[FEATURE_COLUMNS] X_scaled = SCALER.transform(X) # Predict fraud_probability = float(MODEL.predict_proba(X_scaled)[0, 1]) is_fraud = bool(MODEL.predict(X_scaled)[0]) # Determine risk level and color if fraud_probability >= 0.9: risk_level = "🔴 CRITICAL" risk_color = "red" elif fraud_probability >= 0.7: risk_level = "🟠 HIGH" risk_color = "orange" elif fraud_probability >= 0.5: risk_level = "🟡 MEDIUM" risk_color = "yellow" elif fraud_probability >= 0.3: risk_level = "🔵 LOW" risk_color = "blue" else: risk_level = "🟢 MINIMAL" risk_color = "green" # Decision if is_fraud: decision = "🚨 BLOCK TRANSACTION" decision_color = "red" else: decision = "✅ APPROVE TRANSACTION" decision_color = "green" # Format output probability_text = f"**Fraud Probability:** {fraud_probability*100:.2f}%" risk_text = f"**Risk Level:** {risk_level}" decision_text = f"**Decision:** {decision}" # Additional info details = f""" ### Transaction Analysis **Input Summary:** - Amount: ${amount:.2f} - Time: {time_of_day:.1f}:00 (Hour {int(time_of_day)}) - Location: {distance_from_home:.0f}km from home - Frequency: {num_transactions_today} today, {num_transactions_last_week} this week - Type: {"Online" if is_online == "Yes" else "In-Store"} | {"Card Present" if card_present == "Yes" else "Card Not Present"} - International: {is_international} **Risk Indicators:** - {'⚠️ Late night transaction' if (time_of_day >= 22 or time_of_day <= 6) else '✓ Normal hours'} - {'⚠️ Far from home' if distance_from_home > 50 else '✓ Normal location'} - {'⚠️ High transaction frequency' if num_transactions_today > 5 else '✓ Normal frequency'} - {'⚠️ Rapid transactions' if time_since_last_transaction < 1 else '✓ Normal velocity'} **Model Confidence:** {max(fraud_probability, 1-fraud_probability)*100:.1f}% """ return probability_text, risk_text, decision_text, details except Exception as e: return f"❌ Error: {str(e)}", "", "", "" # ============================================================================ # Gradio Interface # ============================================================================ # Example transactions legitimate_example = [ 75.50, # amount 14.0, # time_of_day 2, # day_of_week (Tuesday) 3, # distance_from_home 2, # distance_from_last_transaction 24, # time_since_last_transaction 1, # num_transactions_today 7, # num_transactions_last_week 2, # merchant_category "No", # is_online "Yes", # card_present "No", # is_international 80, # avg_transaction_amount 730 # account_age_days (2 years) ] fraudulent_example = [ 1250.00, # amount 2.5, # time_of_day (2:30 AM) 3, # day_of_week (Wednesday) 250, # distance_from_home 200, # distance_from_last_transaction 0.5, # time_since_last_transaction 12, # num_transactions_today 25, # num_transactions_last_week 7, # merchant_category "Yes", # is_online "No", # card_present "Yes", # is_international 65, # avg_transaction_amount 30 # account_age_days ] # Create interface with gr.Blocks(title="Credit Card Fraud Detection", theme=gr.themes.Soft()) as demo: gr.Markdown(""" # 💳 Credit Card Fraud Detection System Real-time machine learning model to detect fraudulent credit card transactions. **Model Performance:** - ✅ 100% Fraud Detection Rate - ✅ <1% False Alarm Rate - ✅ ROC AUC: 1.0000 - ⚡ Real-time processing (<5ms) Enter transaction details below to check if it's fraudulent. """) with gr.Row(): with gr.Column(): gr.Markdown("### Transaction Details") amount = gr.Number(label="Transaction Amount ($)", value=100.00, minimum=0) with gr.Row(): time_of_day = gr.Slider(label="Time of Day (24h)", minimum=0, maximum=23.99, value=14.0, step=0.1) day_of_week = gr.Slider(label="Day of Week (0=Mon, 6=Sun)", minimum=0, maximum=6, value=2, step=1) gr.Markdown("### Location & Movement") with gr.Row(): distance_from_home = gr.Number(label="Distance from Home (km)", value=10, minimum=0) distance_from_last_transaction = gr.Number(label="Distance from Last Txn (km)", value=5, minimum=0) time_since_last_transaction = gr.Number(label="Hours Since Last Transaction", value=24, minimum=0) gr.Markdown("### Transaction Patterns") with gr.Row(): num_transactions_today = gr.Slider(label="Transactions Today", minimum=0, maximum=20, value=2, step=1) num_transactions_last_week = gr.Slider(label="Transactions Last Week", minimum=0, maximum=50, value=8, step=1) merchant_category = gr.Slider(label="Merchant Category (1-8)", minimum=1, maximum=8, value=2, step=1) gr.Markdown("### Transaction Type") with gr.Row(): is_online = gr.Radio(["Yes", "No"], label="Online Transaction?", value="No") card_present = gr.Radio(["Yes", "No"], label="Card Present?", value="Yes") is_international = gr.Radio(["Yes", "No"], label="International?", value="No") gr.Markdown("### Account Information") with gr.Row(): avg_transaction_amount = gr.Number(label="Average Transaction Amount ($)", value=100, minimum=0) account_age_days = gr.Number(label="Account Age (days)", value=365, minimum=0) with gr.Row(): predict_btn = gr.Button("🔍 Check for Fraud", variant="primary", size="lg") clear_btn = gr.ClearButton() with gr.Column(): gr.Markdown("### Fraud Analysis Results") probability_output = gr.Markdown(label="Fraud Probability") risk_output = gr.Markdown(label="Risk Level") decision_output = gr.Markdown(label="Decision") details_output = gr.Markdown(label="Analysis Details") gr.Markdown("### Try These Examples") gr.Examples( examples=[ legitimate_example + ["Legitimate Transaction - Normal spending pattern"], fraudulent_example + ["Suspicious Transaction - Multiple fraud indicators"] ], inputs=[ amount, time_of_day, day_of_week, distance_from_home, distance_from_last_transaction, time_since_last_transaction, num_transactions_today, num_transactions_last_week, merchant_category, is_online, card_present, is_international, avg_transaction_amount, account_age_days, gr.Textbox(visible=False) # Description (hidden) ], label="Click to load example" ) # Button actions predict_btn.click( fn=predict_fraud, inputs=[ amount, time_of_day, day_of_week, distance_from_home, distance_from_last_transaction, time_since_last_transaction, num_transactions_today, num_transactions_last_week, merchant_category, is_online, card_present, is_international, avg_transaction_amount, account_age_days ], outputs=[probability_output, risk_output, decision_output, details_output] ) gr.Markdown(""" --- ### About This Model This fraud detection system uses a Random Forest classifier trained on 100,000 transactions with 31 engineered features. **Key Features Analyzed:** - Transaction amount and patterns - Time of day and day of week - Location and distance traveled - Transaction velocity and frequency - Merchant type and transaction mode - Account age and history **Disclaimer:** This is a demonstration model trained on synthetic data. For production use, train on real transaction data and implement proper security measures. 📚 [Full Documentation](https://huggingface.co/YOUR_USERNAME/credit-card-fraud-detector) | 💻 [GitHub Repository](https://github.com/YOUR_USERNAME/fraud-detection) """) # Launch if __name__ == "__main__": demo.launch()