Create main.py

main.py

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+# main.py
+from utils.preprocessing import preprocess_data
+from models.fraud_detection_model import build_model
+from utils.flexflow_integration import FlexFlowIntegration
+from utils.feature_engineering import feature_engineering
+from utils.encryption import encrypt_data, decrypt_data
+from utils.lora_integration import LoRaIntegration
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, roc_auc_score
+
+# Example Usage
+data_path = 'data/dataset.csv'
+X_train, X_test, y_train, y_test = preprocess_data(data_path)
+model = build_model(X_train.shape[1])
+model.fit(X_train, y_train, epochs=10, batch_size=32)
+
+# Save the entire model
+model.save('models/fraud_detection_model.h5')
+
+# Example FlexFlow Integration
+data_dict = {"score": 0.8, "timestamp": "2023-01-01 12:34:56"}
+FlexFlowIntegration.encrypt_and_send(data_dict)
+received_data = FlexFlowIntegration.receive_and_decrypt()
+
+if received_data:
+    result = FlexFlowIntegration.execute_model(received_data)
+    print("Model Result:", result)
+
+# Example Evaluation (assuming y_true and y_pred are defined)
+y_pred = model.predict_classes(X_test)
+evaluate_model(y_test, y_pred)