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import os
import joblib
import numpy as np
from concrete.ml.deployment import FHEModelClient, FHEModelServer
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
# Paths to required files
SCALER_PATH = os.path.join("models", "scaler.pkl")
FHE_FILES_PATH = os.path.join("models", "fhe_files")
# Load the scaler
try:
scaler = joblib.load(SCALER_PATH)
logging.info("Scaler loaded successfully.")
except FileNotFoundError:
logging.error(f"Error: The file scaler.pkl is missing at {SCALER_PATH}.")
raise
# Initialize the FHE client and server
try:
client = FHEModelClient(path_dir=FHE_FILES_PATH, key_dir=FHE_FILES_PATH)
server = FHEModelServer(path_dir=FHE_FILES_PATH)
server.load()
logging.info("FHE Client and Server initialized successfully.")
except FileNotFoundError:
logging.error(f"Error: The FHE files (client.zip, server.zip) are missing in {FHE_FILES_PATH}.")
raise
# Load evaluation keys
evaluation_keys = client.get_serialized_evaluation_keys()
def predict(input_data):
"""
Perform a local prediction using the compiled FHE model.
Args:
input_data (dict): User input data as a dictionary.
Returns:
str: Prediction result ("Fraudulent" or "Non-fraudulent").
"""
try:
logging.info(f"Input Data: {input_data}")
# Scale the input data
scaled_data = scaler.transform([list(input_data.values())])
logging.info(f"Scaled Data: {scaled_data}")
# Encrypt the scaled data
encrypted_data = client.quantize_encrypt_serialize(scaled_data)
logging.info("Data encrypted successfully.")
# Execute the model locally on encrypted data
encrypted_prediction = server.run(
encrypted_data, serialized_evaluation_keys=evaluation_keys
)
logging.info(f"Encrypted Prediction: {encrypted_prediction}")
# Decrypt the prediction result
decrypted_prediction = client.deserialize_decrypt_dequantize(encrypted_prediction)
logging.info(f"Decrypted Prediction: {decrypted_prediction}")
# Interpret the prediction
binary_prediction = int(np.argmax(decrypted_prediction))
return "Fraudulent" if binary_prediction == 1 else "Non-fraudulent"
except Exception as e:
logging.error(f"Error during prediction: {e}")
return "Error during prediction"
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