Create app.py

app.py

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+import numpy as np
+import torch
+import torch.nn as nn
+from flask import Flask, request, jsonify
+from sklearn.preprocessing import MinMaxScaler
+import joblib
+
+# Define the autoencoder model
+class Autoencoder(nn.Module):
+    def __init__(self, input_dim):
+        super(Autoencoder, self).__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(input_dim, 64),
+            nn.BatchNorm1d(64),
+            nn.ReLU(),
+            nn.Dropout(0.2)
+        )
+        self.decoder = nn.Sequential(
+            nn.Linear(64, input_dim),
+            nn.BatchNorm1d(input_dim),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        encoded = self.encoder(x)
+        decoded = self.decoder(encoded)
+        return decoded
+
+# Load trained model
+input_dim = 29  # Assuming 29 features (V1-V28 + Amount)
+model = Autoencoder(input_dim)
+model.load_state_dict(torch.load("trained_autoencoder.pth"))
+model.eval()
+
+# Load the MinMaxScaler
+scaler = joblib.load("scaler.pkl")
+
+# Create Flask app
+app = Flask(__name__)
+
+@app.route('/')
+def home():
+    return "Credit Card Fraud Detection API is Running!"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get JSON input
+        data = request.get_json()
+        X_input = np.array(data['features']).reshape(1, -1)  # Ensure it's in the right shape
+
+        # Scale input data
+        X_scaled = scaler.transform(X_input)
+
+        # Convert to PyTorch tensor
+        X_tensor = torch.tensor(X_scaled, dtype=torch.float32)
+
+        # Get reconstruction error
+        recon = model(X_tensor).detach().numpy()
+        recon_error = np.mean((recon - X_scaled) ** 2)
+
+        # Use threshold to classify as fraud (1) or normal (0)
+        threshold = 0.01  # Adjust this based on previous experiments
+        prediction = 1 if recon_error > threshold else 0
+
+        return jsonify({'fraud_probability': float(recon_error), 'is_fraud': prediction})
+    
+    except Exception as e:
+        return jsonify({'error': str(e)})
+
+if __name__ == '__main__':
+    app.run(debug=True)