Reorganize layout

app.py

@@ -67,13 +67,9 @@ data
 #st.divider()
 
 # header: predictors
-numeric_features = ["Duration", "Credit_amount", "Installment_rate", "Resident_since", "Age", "Existing_credits", "People_maintenance_for"]
-categorical_features = ["Account_status", "Credit_history", "Purpose", "Savings_bonds", "Present_employment_since", "Other_debtors_guarantors", "Property", "Other_installment_plans", "Housing", "Job", "Telephone", "Foreign_worker", "Gender"]
-#categorical_features = []
-
 st.header('Predictors')
 st.write('Please select up to 3 predictors:')
-selected = st.multiselect('', numeric_features+categorical_features, max_selections=3)
+selected = st.multiselect('', vars, max_selections=3)
 #st.divider()
 
 # header: model
@@ -81,11 +77,13 @@ st.header('Model')
 
 X_train_f = X_train.loc[:, selected]
 
+numeric_features = ["Duration", "Credit_amount", "Installment_rate", "Resident_since", "Age", "Existing_credits", "People_maintenance_for"]
 numeric_features_selected = list(set(selected) & set(numeric_features))
 numeric_transformer = Pipeline(
     steps=[("imputer", SimpleImputer())]
 )
 
+categorical_features = ["Account_status", "Credit_history", "Purpose", "Savings_bonds", "Present_employment_since", "Other_debtors_guarantors", "Property", "Other_installment_plans", "Housing", "Job", "Telephone", "Foreign_worker", "Gender"]
 categorical_features_selected = list(set(selected) & set(categorical_features))
 categorical_transformer = Pipeline(
     steps=[