Update app.py

app.py

@@ -143,16 +143,19 @@ if st.session_state.image is not None:
     if car_classifications:
         st.write("Image classification successful.")
         st.subheader("Car Classification Results:")
-        for classification in car_classifications:
-            st.write(f"Model: {classification['label']}")
-            st.write(f"Confidence: {classification['score'] * 100:.2f}%")
+        # for classification in car_classifications:
+        #     st.write(f"Model: {classification['label']}")
+            # st.write(f"Confidence: {classification['score'] * 100:.2f}%")
         
         # Separate make and model from the classification result
         top_prediction = car_classifications[0]['label']
         make_name, model_name = top_prediction.split(' ', 1)
-        
-        st.write(f"Identified Car Make: {make_name}")
-        st.write(f"Identified Car Model: {model_name}")
+
+        col1, col2= st.columns(2)
+        col1.metric("Identified Car Make", make_name)
+        col2.metric("Identified Car Model", model_name)
+        # st.write(f"Identified Car Model: {make_name}")
+        # st.write(f"Identified Car Model: {model_name}")
 
         # Find the closest match in the CSV based on the classification
         car_data = load_car_data()
@@ -162,8 +165,8 @@ if st.session_state.image is not None:
             
             # 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]}")
+            # 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]}")
             st.write(f"Price: ${closest_car['price']}")
             st.write(f"Condition: {closest_car['condition']}")
             st.write(f"Fuel: {closest_car['fuel']}")