Update app.py

app.py

@@ -6,15 +6,16 @@ from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
 from sklearn.impute import KNNImputer
 
 # Load your saved model
-model = joblib.load("ann_model.joblib")
+# 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):
     features = [age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]
     fixed_features = cleaning_features(features)
-    prediction = model.predict(features)
-    prediction = 1
-    return "Income >50K" if prediction == 1 else "Income <=50K"
+    # prediction = model.predict(features)
+    # prediction = 1
+    # return "Income >50K" if prediction == 1 else "Income <=50K"
+    return fixed_features
 
 def cleaning_features(data):
     le = LabelEncoder()