Update app.py

app.py

@@ -7,8 +7,7 @@ from sklearn.impute import KNNImputer
 from sklearn.decomposition import PCA
 import pickle
 
-# Load your saved model
-# model = joblib.load("ann_model.joblib")
+
 
 # # Define the prediction function
 def predict(age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
@@ -19,14 +18,13 @@ def predict(age, workclass, education, marital_status, occupation, relationship,
     "hours-per-week":[hours_per_week], "native-country":[native_country]}
     df = pd.DataFrame(data=columns)
     fixed_features = cleaning_features(df)
-    # prediction = model.predict(features)
+     with open('ann_model.pkl', 'rb') as ann_model_file:
+        ann_model = pickle.load(ann_model_file)
+    prediction = ann_model.predict(fixed_features)
     # prediction = 1
-    # return "Income >50K" if prediction == 1 else "Income <=50K"
-    return print(fixed_features)
+    return "Income >50K" if prediction == 1 else "Income <=50K"
 
 def cleaning_features(data):
-    # with open('race_onehot_encoder.pkl', 'rb') as enc_file:
-    #     encoder = pickle.load(enc_file)
     
     with open('label_encoder_work.pkl', 'rb') as le_file:
         le_work = pickle.load(le_file)
@@ -69,7 +67,7 @@ def cleaning_features(data):
     
     data[numeric_cols] = scaler.transform(data[numeric_cols])
 
-    #data = pca(data)
+    data = pca(data)
     return data
 
 # def pca(data):
@@ -155,7 +153,7 @@ interface = gr.Interface(
             ["Male", "Female"], 
             label="Gender"
         ),
-        gr.Slider(1, 90, step=1, label="Hours Per Week"),
+        gr.Slider(1, 60, step=1, label="Hours Per Week"),
         gr.Slider(0, 100000, step=100, label="Capital Gain"),
         gr.Slider(0, 5000, step=50, label="Capital Loss"),
         gr.Dropdown(