Upload 2 files

server2.py

@@ -45,6 +45,7 @@ feature_selected     #selected features which are very much correalated
 
 clean_data=data[feature_selected]
 
+from xgboost import XGBClassifier
 from sklearn.tree import DecisionTreeClassifier                  #using sklearn decisiontreeclassifier
 from sklearn.model_selection import train_test_split
 
@@ -63,7 +64,7 @@ x_train=sc.fit_transform(x_train)
 x_test=sc.transform(x_test)
 
 #training our model
-dt=DecisionTreeClassifier(criterion='entropy',max_depth=6)
+dt=XGBClassifier(max_depth=6)
 dt.fit(x_train,y_train)
 #dt.compile(x_trqin)
 
@@ -79,9 +80,9 @@ print("\nThe accuracy of decisiontreelassifier on Heart disease prediction datas
 
 joblib.dump(dt, 'heart_disease_dt_model.pkl')
 
-from concrete.ml.sklearn.tree import DecisionTreeClassifier
+from concrete.ml.sklearn.tree import XGBClassifier as ConcreteXGBClassifier
 
-fhe_compatible = DecisionTreeClassifier.from_sklearn_model(dt, x_train, n_bits = 10)
+fhe_compatible = ConcreteXGBClassifier.from_sklearn_model(dt, x_train, n_bits = 10)
 fhe_compatible.compile(x_train)