Update app.py

app.py

@@ -25,8 +25,8 @@ def predict(age, workclass, education, marital_status, occupation, relationship,
     return print(fixed_features)
 
 def cleaning_features(data):
-    with open('race_onehot_encoder.pkl', 'rb') as enc_file:
-        encoder = pickle.load(enc_file)
+    # with open('race_onehot_encoder.pkl', 'rb') as enc_file:
+    #     encoder = pickle.load(enc_file)
     
     with open('label_encoder.pkl', 'rb') as le_file:
         le = pickle.load(le_file)
@@ -52,14 +52,17 @@ def cleaning_features(data):
         "Doctorate": 15,
         "Prof-school": 16
     }
+
+    gender_mapping = {"Male":1,"Female":0}
     
     numeric_cols = ['age', 'educational-num', 'hours-per-week']
     columns_to_encode = ['race','marital-status','relationship']
     
     data['gender'] = le.fit_transform(data['gender'])
-    data['educational-num'] = data['educational-num'].map(education_num_mapping)
     data['workclass'] = le.fit_transform(data['workclass'])
     data['occupation'] = le.fit_transform(data['occupation'])
+    data['gender'] = data['gender'].map(gender_mapping)
+    data['educational-num'] = data['educational-num'].map(education_num_mapping)
     
     data[numeric_cols] = scaler.fit_transform(data[numeric_cols])