fix

app.py

@@ -8,28 +8,6 @@ from sklearn.tree import DecisionTreeClassifier                  #using sklearn
 # Load the saved model
 dt = joblib.load('heart_disease_dt_model.pkl')
 
-# Load the dataset and select the relevant features
-data = pd.read_csv('data/heart.xls')
-
-# Perform the correlation analysis
-data_corr = data.corr()
-
-# Select features based on correlation with 'output'
-feature_value = np.array(data_corr['output'])
-for i in range(len(feature_value)):
-    if feature_value[i] < 0:
-        feature_value[i] = -feature_value[i]
-
-features_corr = pd.DataFrame(feature_value, index=data_corr['output'].index, columns=['correlation'])
-feature_sorted = features_corr.sort_values(by=['correlation'], ascending=False)
-feature_selected = feature_sorted.index
-
-# Clean the data by selecting the most correlated features
-clean_data = data[feature_selected]
-
-# Extract the first row of feature data for prediction (excluding 'output' column)
-sample_data = clean_data.iloc[0, 1:].values.reshape(1, -1)  # Reshape to 2D array for model input
-
 #fhe_circuit = 
 # Make prediction on the first row of data
 #prediction = dt.predict(sample_data, fhe="execute")

client.py

@@ -1,31 +0,0 @@
-from concrete.ml.deployment import FHEModelDev, FHEModelClient, FHEModelServer
-
-# Setup the client
-client = FHEModelClient(path_dir=fhe_directory, key_dir="/tmp/keys_client")
-serialized_evaluation_keys = client.get_serialized_evaluation_keys()
-
-
-# Load the dataset and select the relevant features
-data = pd.read_csv('data/heart.xls')
-
-# Perform the correlation analysis
-data_corr = data.corr()
-
-# Select features based on correlation with 'output'
-feature_value = np.array(data_corr['output'])
-for i in range(len(feature_value)):
-    if feature_value[i] < 0:
-        feature_value[i] = -feature_value[i]
-
-features_corr = pd.DataFrame(feature_value, index=data_corr['output'].index, columns=['correlation'])
-feature_sorted = features_corr.sort_values(by=['correlation'], ascending=False)
-feature_selected = feature_sorted.index
-
-# Clean the data by selecting the most correlated features
-clean_data = data[feature_selected]
-
-# Extract the first row of feature data for prediction (excluding 'output' column)
-sample_data = clean_data.iloc[0, 1:].values.reshape(1, -1)  # Reshape to 2D array for model input
-
-encrypted_data = client.quantize_encrypt_serialize(sample_data)
-