Update app.py

app.py

@@ -23,9 +23,6 @@ else:
 column_trans = ColumnTransformer([('ohe', ohe_new, [0, 1, 4])], remainder='passthrough')
 column_trans.fit(X_train)
 
-if not hasattr(column_trans, '_name_to_fitted_passthrough'):
-    column_trans.fit(X_train)
-
 # save the fitted ColumnTransformer object
 with open('column_trans.pkl', 'wb') as f:
     pickle.dump(column_trans, f)
@@ -52,11 +49,14 @@ if st.button('Click for car price'):
     input_df = pd.DataFrame({'name': model_name, 'company':company_name, 'year': year,
                                'kms_driven':kms_driven, 'fuel_type': fuel_type}, index=[0])
     
+    # fit the ColumnTransformer object again on your data before using it to transform your input data
+    column_trans.fit(X_train)
+    
     # transform the input DataFrame using the fitted ColumnTransformer object
-    # input_transformed = column_trans.transform(input_df)
+    input_transformed = column_trans.transform(input_df)
     
     # make the prediction
-    prediction = pipe.predict(input_df)
+    prediction = pipe.predict(input_transformed)
     
     if prediction <= 0:
         st.write('The car is a scrap')