# 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)