Update app.py

app.py

@@ -152,19 +152,13 @@ if st.session_state.image is not None:
         st.write(f"Identified Car Make: {make_name}")
         st.write(f"Identified Car Model: {model_name}")
 
-        # Get additional information using GPT-3.5-turbo
-        current_year = datetime.now().year
-        overview = get_car_overview(make_name, model_name, current_year)
-        st.write("Car Overview:")
-        st.write(overview)
-
         # Find the closest match in the CSV based on the classification
         car_data = load_car_data()
         if car_data is not None:
             processed_car_data, label_encoders = preprocess_car_data(car_data)
             closest_car = find_closest_car(processed_car_data, label_encoders, make_name, model_name, current_year)
             
-            st.write(f"Closest match in database:")
+            # st.write(f"Closest match in database:")
             st.write(f"Year: {closest_car['year']}")
             st.write(f"Make: {label_encoders['make'].inverse_transform([closest_car['make']])[0]}")
             st.write(f"Model: {label_encoders['model'].inverse_transform([closest_car['model']])[0]}")
@@ -174,6 +168,14 @@ if st.session_state.image is not None:
             st.write(f"Transmission: {closest_car['transmission']}")
             st.write(f"Drive: {closest_car['drive']}")
             st.write(f"Type: {closest_car['type']}")
+
+        st.divider()
+
+        # Get additional information using GPT-3.5-turbo
+        current_year = datetime.now().year
+        overview = get_car_overview(make_name, model_name, current_year)
+        st.write("Car Overview:")
+        st.write(overview)
     else:
         st.error("Could not classify the image. Please try again with a different image.")
 else: