init

.gitignore

@@ -0,0 +1 @@
+venv

app.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pandas as pd
+import joblib   # for loading the saved model
+from sklearn.tree import DecisionTreeClassifier                  #using sklearn decisiontreeclassifier
+
+#from concrete.ml.sklearn.xgb import DecisionTreeClassifier
+
+# 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")
+prediction = dt.predict(sample_data) # clair
+
+# Display the prediction result
+print(prediction)
+if prediction == 1:
+    print("Prediction: The patient is likely to have heart disease.")
+else:
+    print("Prediction: The patient is unlikely to have heart disease.")
+

client.py

@@ -0,0 +1,31 @@
+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)
+

data/heart.xls

@@ -0,0 +1,304 @@
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requirements.txt

@@ -0,0 +1,7 @@
+numpy
+pandas
+seaborn
+scikit-learn
+setuptools
+concrete-ml
+concrete

server.py

@@ -0,0 +1,149 @@
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import joblib
+
+import os
+import shutil
+
+# Define the directory for FHE client/server files
+fhe_directory = '/tmp/fhe_client_server_files/'
+
+# Create the directory if it does not exist
+if not os.path.exists(fhe_directory):
+    os.makedirs(fhe_directory)
+else:
+    # If it exists, delete its contents
+    shutil.rmtree(fhe_directory)
+    os.makedirs(fhe_directory)
+
+data=pd.read_csv('data/heart.xls')
+
+data.info()   #checking the info
+
+data_corr=data.corr()
+
+plt.figure(figsize=(20,20))
+sns.heatmap(data=data_corr,annot=True)
+#Heatmap for data
+
+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]
+
+print(feature_value)
+
+features_corr=pd.DataFrame(feature_value,index=data_corr['output'].index,columns=['correalation'])
+
+feature_sorted=features_corr.sort_values(by=['correalation'],ascending=False)
+
+feature_selected=feature_sorted.index
+
+feature_selected     #selected features which are very much correalated
+
+clean_data=data[feature_selected]
+
+from sklearn.tree import DecisionTreeClassifier                  #using sklearn decisiontreeclassifier
+from sklearn.model_selection import train_test_split
+
+#making input and output dataset
+X=clean_data.iloc[:,1:]
+Y=clean_data['output']
+
+x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.25,random_state=0)
+
+print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)     #data is splited in traing and testing dataset
+
+# feature scaling
+from sklearn.preprocessing import StandardScaler
+sc=StandardScaler()
+x_train=sc.fit_transform(x_train)
+x_test=sc.transform(x_test)
+
+#training our model
+dt=DecisionTreeClassifier(criterion='entropy',max_depth=6)
+dt.fit(x_train,y_train)
+#dt.compile(x_trqin)
+
+#predicting the value on testing data
+y_pred=dt.predict(x_test)
+
+#ploting the data
+from sklearn.metrics import confusion_matrix
+conf_mat=confusion_matrix(y_test,y_pred)
+print(conf_mat)
+accuracy=dt.score(x_test,y_test)
+print("\nThe accuracy of decisiontreelassifier on Heart disease prediction dataset is "+str(round(accuracy*100,2))+"%")
+
+joblib.dump(dt, 'heart_disease_dt_model.pkl')
+
+from concrete.ml.sklearn.tree import DecisionTreeClassifier
+
+fhe_compatible = DecisionTreeClassifier.from_sklearn_model(dt, x_train, n_bits = 10)
+fhe_compatible.compile(x_train)
+
+
+
+
+
+
+#### server
+from concrete.ml.deployment import FHEModelDev, FHEModelClient, FHEModelServer
+
+# Setup the development environment
+dev = FHEModelDev(path_dir=fhe_directory, model=fhe_compatible)
+dev.save()
+
+# Setup the server
+server = FHEModelServer(path_dir=fhe_directory)
+server.load()
+
+
+
+
+
+
+
+####### client
+
+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)
+
+
+
+##### end client
+
+encrypted_result = server.run(encrypted_data, serialized_evaluation_keys)
+
+result = client.deserialize_decrypt_dequantize(encrypted_result)
+print(result)