Update app.py

app.py

@@ -109,7 +109,7 @@ def predict_price(model, encoders, user_input):
 
 # Streamlit App
 st.title("Auto Appraise")
-st.write("Capture a car image using your camera or upload an image to get its brand, model, overview, and expected price!")
+st.write("Upload a car image or take a picture to get its brand, model, overview, and expected price!")
 
 # Load model and encoders
 model, label_encoders = load_model_and_encodings()
@@ -117,97 +117,94 @@ model, label_encoders = load_model_and_encodings()
 # Initialize OpenAI API key
 openai.api_key = st.secrets["GPT_TOKEN"]
 
-# Camera input for taking photo
-camera_image = st.camera_input("Take a picture of the car!")
+# File uploader for image
+uploaded_file = st.file_uploader("Choose a car image", type=["jpg", "jpeg", "png"])
 
-# Debug information
-st.write(f"Type of camera_image: {type(camera_image)}")
-st.write(f"Content of camera_image: {camera_image}")
+# Camera input as an alternative (optional)
+camera_image = st.camera_input("Or take a picture of the car")
 
-if camera_image is not None:
+# Process the image (either uploaded or from camera)
+image = None
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.write("Image uploaded successfully.")
+elif camera_image is not None:
+    image = Image.open(camera_image)
     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 image is not None:
+    st.image(image, caption='Processed Image', use_container_width=True)
+
+    # Classify the car image
+    with st.spinner('Analyzing image...'):
+        car_classifications = classify_image(image)
+    
+    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)
         
-        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.")
+        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.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)}")
+            st.write("No match found in the database.")
+    else:
+        st.error("Could not classify the image. Please try again with a different image.")
 else:
-    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)
+    st.write("Please upload an image or take a picture to proceed.")