Initial commit

app.py

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+from flask import Flask, render_template, request, jsonify
+import joblib
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+app = Flask(__name__)
+
+# Load the model
+try:
+    model = joblib.load('models/fraud_model.pkl')
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise
+
+
+def preprocess_input(data):
+    # Convert to DataFrame
+    df = pd.DataFrame([data])
+
+    # Convert numeric fields explicitly
+    numeric_fields = ['amt', 'city_pop', 'lat', 'long', 'merch_lat', 'merch_long']
+    for field in numeric_fields:
+        df[field] = pd.to_numeric(df[field], errors='coerce')
+
+    # Convert transaction time to datetime
+    df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])
+
+    # Extract time features
+    df['hour'] = df['trans_date_trans_time'].dt.hour
+    df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek
+    df['month'] = df['trans_date_trans_time'].dt.month
+
+    # Calculate age from dob
+    df['dob'] = pd.to_datetime(df['dob'])
+    df['age'] = (pd.to_datetime('today') - df['dob']).dt.days // 365
+
+    # Calculate distance between user and merchant
+    df['distance'] = np.sqrt(
+        (df['lat'].astype(float) - df['merch_lat'].astype(float))**2 + 
+        (df['long'].astype(float) - df['merch_long'].astype(float))**2
+    )
+
+    # Drop unnecessary columns
+    return df.drop(['trans_date_trans_time', 'dob'], axis=1, errors='ignore')
+
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get data from form
+        data = request.form.to_dict()
+
+        # Preprocess the input
+        processed_data = preprocess_input(data)
+
+        # Make prediction
+        prediction = model.predict(processed_data)
+        probability = model.predict_proba(processed_data)[0][1]
+
+        result = {
+            'prediction': int(prediction[0]),
+            'probability': float(probability),
+            'is_fraud': bool(prediction[0])
+        }
+
+        return render_template('index.html', prediction=result)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+
+if __name__ == '__main__':
+    app.run(debug=True)