Update app.py

app.py

@@ -120,79 +120,94 @@ openai.api_key = st.secrets["GPT_TOKEN"]
 # Camera input for taking photo
 camera_image = st.camera_input("Take a picture of the car!")
 
+# Debug information
+st.write(f"Type of camera_image: {type(camera_image)}")
+st.write(f"Content of camera_image: {camera_image}")
+
 if camera_image is not None:
-    image = load_image(camera_image)
-    st.image(image, caption='Captured Image.', use_container_width=True)
+    st.write("Image captured successfully.")
+    try:
+        image = load_image(camera_image)
+        st.image(image, caption='Captured Image.', use_container_width=True)
 
-    # Classify the car image
-    with st.spinner('Analyzing image...'):
-        car_classifications = classify_image(image)
-    
-    if car_classifications:
-        st.subheader("Car Classification Results:")
-        for classification in car_classifications:
-            st.write(f"Model: {classification['label']}")
-            st.write(f"Confidence: {classification['score']*100:.2f}%")
-        
-        # Use the top prediction for further processing
-        top_prediction = car_classifications[0]['label']
-        brand, model_name = top_prediction.split(' ', 1)
+        # Classify the car image
+        with st.spinner('Analyzing image...'):
+            car_classifications = classify_image(image)
         
-        st.write(f"Identified Car: {brand} {model_name}")
-
-        # Find the closest match in the CSV
-        df = load_datasets()
-        match = find_closest_match(df, brand, model_name)
-        if match is not None:
-            st.write("Closest Match Found:")
-            st.write(f"Make: {match['Make']}")
-            st.write(f"Model: {match['Model']}")
-            st.write(f"Year: {match['Year']}")
-            st.write(f"Price: ${match['Price']}")
-
-            # Get additional information using GPT-3.5-turbo
-            overview = get_car_overview(match)
-            st.write("Car Overview:")
-            st.write(overview)
-
-            # Interactive Price Prediction
-            st.subheader("Price Prediction Over Time")
-            selected_years = st.slider("Select range of years for price prediction", 
-                                         min_value=2000, max_value=2023, value=(2010, 2023))
-
-            years = np.arange(selected_years[0], selected_years[1] + 1)
-            predicted_prices = []
-
-            for year in years:
-                user_input = {
-                    'Make': match['Make'],
-                    'Model': match['Model'],
-                    'Condition': match['Condition'],
-                    'Fuel': match['Fuel'],
-                    'Title_status': match['Title_status'],
-                    'Transmission': match['Transmission'],
-                    'Drive': match['Drive'],
-                    'Size': match['Size'],
-                    'Type': match['Type'],
-                    'Paint_color': match['Paint_color'],
-                    'Year': year
-                }
-                
-                price = predict_price(model, label_encoders, user_input)
-                predicted_prices.append(price)
-
-            # Plotting the results
-            plt.figure(figsize=(10, 5))
-            plt.plot(years, predicted_prices, marker='o')
-            plt.title(f"Predicted Price of {match['Make']} {match['Model']} Over Time")
-            plt.xlabel("Year")
-            plt.ylabel("Predicted Price ($)")
-            plt.grid()
-            st.pyplot(plt)
-
+        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}%")
+            
+            # Use the top prediction for further processing
+            top_prediction = car_classifications[0]['label']
+            brand, model_name = top_prediction.split(' ', 1)
+            
+            st.write(f"Identified Car: {brand} {model_name}")
+
+            # Find the closest match in the CSV
+            df = load_datasets()
+            match = find_closest_match(df, brand, model_name)
+            if match is not None:
+                st.write("Closest Match Found:")
+                st.write(f"Make: {match['Make']}")
+                st.write(f"Model: {match['Model']}")
+                st.write(f"Year: {match['Year']}")
+                st.write(f"Price: ${match['Price']}")
+
+                # Get additional information using GPT-3.5-turbo
+                overview = get_car_overview(match)
+                st.write("Car Overview:")
+                st.write(overview)
+
+                # Interactive Price Prediction
+                st.subheader("Price Prediction Over Time")
+                selected_years = st.slider("Select range of years for price prediction", 
+                                             min_value=2000, max_value=2023, value=(2010, 2023))
+
+                years = np.arange(selected_years[0], selected_years[1] + 1)
+                predicted_prices = []
+
+                for year in years:
+                    user_input = {
+                        'Make': match['Make'],
+                        'Model': match['Model'],
+                        'Condition': match['Condition'],
+                        'Fuel': match['Fuel'],
+                        'Title_status': match['Title_status'],
+                        'Transmission': match['Transmission'],
+                        'Drive': match['Drive'],
+                        'Size': match['Size'],
+                        'Type': match['Type'],
+                        'Paint_color': match['Paint_color'],
+                        'Year': year
+                    }
+                    
+                    price = predict_price(model, label_encoders, user_input)
+                    predicted_prices.append(price)
+
+                # Plotting the results
+                plt.figure(figsize=(10, 5))
+                plt.plot(years, predicted_prices, marker='o')
+                plt.title(f"Predicted Price of {match['Make']} {match['Model']} Over Time")
+                plt.xlabel("Year")
+                plt.ylabel("Predicted Price ($)")
+                plt.grid()
+                st.pyplot(plt)
+
+            else:
+                st.write("No match found in the database.")
         else:
-            st.write("No match found in the database.")
-    else:
-        st.error("Could not classify the image. Please try again with a different image.")
+            st.error("Could not classify the image. Please try again with a different image.")
+    except Exception as e:
+        st.error(f"Error processing image: {str(e)}")
 else:
-    st.write("Please take a picture of the car to proceed.")
+    st.write("Please take a picture of the car to proceed.")
+
+# # Alternative file uploader (for debugging)
+# uploaded_file = st.file_uploader("Or choose a car image", type=["jpg", "jpeg", "png"])
+# if uploaded_file is not None:
+#     image = Image.open(uploaded_file)
+#     st.image(image, caption='Uploaded Image.', use_container_width=True)