Update app.py

app.py

@@ -12,10 +12,13 @@ from tensorflow.keras.models import load_model
 
 # # 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]
+    # columns = {
+    # "age": [age], "workclass":[workclass], "educational-num":[education], "marital-status":[marital_status], "occupation":[occupation], 
+    # "relationship":[relationship], "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss], 
+    # "hours-per-week":[hours_per_week], "native-country":[native_country]}
     columns = {
-    "age": [age], "workclass":[workclass], "educational-num":[education], "marital-status":[marital_status], "occupation":[occupation], 
-    "relationship":[relationship], "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss], 
+    "age": [age], "workclass":[workclass], "educational-num":[education], 
+    "race":[race], "gender":[gender], "capital-gain":[capital_gain], "capital-loss":[capital_loss], 
     "hours-per-week":[hours_per_week], "native-country":[native_country]}
     df = pd.DataFrame(data=columns)
     fixed_features = cleaning_features(df)