Update app.py

app.py

@@ -8,6 +8,7 @@ from sklearn.decomposition import PCA
 import pickle
 from tensorflow.keras.models import load_model
 import pickle
+import hdbscan
 
 
 
@@ -27,7 +28,6 @@ def predict_ann(age, workclass, education, marital_status, occupation, relations
     # with open('ann_model.pkl', 'rb') as ann_model_file:
     #     ann_model = pickle.load(ann_model_file)
     scaler = StandardScaler()
-    X = scaler.fit_transform(fixed_features)
     ann_model = load_model('ann_model.h5')
     prediction = ann_model.predict(fixed_features)
     # prediction = 1
@@ -48,7 +48,6 @@ def predict_rf(age, workclass, education, marital_status, occupation, relationsh
     # with open('ann_model.pkl', 'rb') as ann_model_file:
     #     ann_model = pickle.load(ann_model_file)
     scaler = StandardScaler()
-    X = scaler.fit_transform(fixed_features)
     rf_model = pickle.load(open('rf_model.pkl', 'rb'))
     prediction = rf_model.predict(fixed_features)
     # prediction = 1
@@ -71,9 +70,9 @@ def predict_hb(age, workclass, education, marital_status, occupation, relationsh
     scaler = StandardScaler()
     X = scaler.fit_transform(fixed_features)
     hb_model = pickle.load(open('hdbscan_model.pkl', 'rb'))
-    prediction = hb_model.predict(fixed_features)
+    prediction = hdbscan.approximate_predict(hb_model,fixed_features)
     # prediction = 1
-    return "Income >50K" if prediction == 1 else "Income <=50K"
+    return f"Predicted Cluster (HDBSCAN): {prediction}"
 
 
 def cleaning_features(data,race):
@@ -222,8 +221,8 @@ hb_interface = gr.Interface(
 
 
 interface = gr.TabbedInterface(
-    [ann_interface, rf_interface,hb_interface],
-    ["ANN Model", "Random Forest Model","HDBScan Model"]
+    [ann_interface, rf_interface, hb_interface],
+    ["ANN Model", "Random Forest Model", "HDBScan Model"]
 )