update

pages/Image_prediction.py

@@ -66,7 +66,9 @@ def save_predictions_to_history(uploaded_files, predictions, model_name):
             'prediction': actual
         })
 
-    st.session_state.saved_predictions.extend(prediction_data)
+    # Check if predictions exist and extend saved_predictions list
+    if isinstance(st.session_state.saved_predictions, list):
+        st.session_state.saved_predictions.extend(prediction_data)
 
     with open('prediction_history.json', 'w') as f:
         json.dump(st.session_state.saved_predictions, f, indent=4)
@@ -85,21 +87,29 @@ def download_and_load_model(model_url):
     if st.session_state.model_temp_file is None:
         with tempfile.NamedTemporaryFile(suffix='.keras', delete=False) as tmp:
             st.session_state.model_temp_file = tmp.name
+            # Show toast message for downloading model
             st.toast("📥 Downloading model... Please wait.")
+            # Spinner for downloading the model
             with st.spinner("Downloading the model..."):
                 gdown.download(model_url, st.session_state.model_temp_file, quiet=False)
+
+            # Show toast message for download completion
             st.toast("✅ Model download completed!")
 
+    # Load the model from the temp file
     model = load_model(st.session_state.model_temp_file)
     return model
 
 def show_image_prediction():
     # Streamlit UI
     st.title('Oral Cancer Detection Model Evaluation')
 
+    # Model selection
     model_selection = st.selectbox("Select a model", list(model_links.keys()))
 
-    uploaded_files = st.file_uploader("Upload images", type=['jpg', 'jpeg', 'png'], accept_multiple_files=True)
+    # Upload images
+    uploaded_files = st.file_uploader(
+        "Upload images", type=['jpg', 'jpeg', 'png'], accept_multiple_files=True)
 
     if uploaded_files:
         target_size = model_links[model_selection]['target_size']
@@ -114,24 +124,30 @@ def load_uploaded_images(uploaded_files, target_size):
 
         X_test = load_uploaded_images(uploaded_files, target_size)
 
+        # Function to evaluate the model on uploaded images
         def evaluate_model(model, images):
             predictions = model.predict(images)
             predicted_classes = (predictions > 0.5).astype(int)
             return predicted_classes
 
+        # Add a button to trigger predictions
         if st.button('Predict'):
             st.info("Downloading and loading the model. This may take a few moments...")
 
+            # Download and load the model
             model_url = model_links[model_selection]['url']
             with st.spinner("Loading model..."):
                 model_to_use = download_and_load_model(model_url)
 
+            # Evaluate the model
             with st.spinner("Evaluating images..."):
                 st.session_state.predictions = evaluate_model(model_to_use, X_test)
                 st.session_state.uploaded_images = uploaded_files
 
+            # Show toast message for image prediction
             st.toast("✨ Images predicted successfully!")
 
+            # Display predictions
             st.subheader('Predictions:')
             for i, uploaded_file in enumerate(uploaded_files):
                 actual = 'Cancer' if st.session_state.predictions[i][0] == 0 else 'Non Cancer'
@@ -148,20 +164,17 @@ def evaluate_model(model, images):
         if st.button('Clear'):
             st.session_state.predictions = []
             st.session_state.uploaded_images = []
-            st.session_state.model_temp_file = None
+            st.session_state.model_temp_file = None  # Reset the temp file
             st.success("🗑️ Cleared all predictions and uploaded images.")
 
     with col2:
-        # Ensure that both predictions and uploaded_images are initialized before accessing them
-        if hasattr(st.session_state, 'predictions') and len(st.session_state.predictions) > 0 and \
-           hasattr(st.session_state, 'uploaded_images') and len(st.session_state.uploaded_images) > 0:
+        if len(st.session_state.predictions) > 0 and len(st.session_state.uploaded_images) > 0:
             if st.button('Save Predictions'):
                 save_predictions_to_history(
                     st.session_state.uploaded_images, st.session_state.predictions, model_selection)
 
     # Download predictions functionality
-    if hasattr(st.session_state, 'predictions') and len(st.session_state.predictions) > 0 and \
-       hasattr(st.session_state, 'uploaded_images') and len(st.session_state.uploaded_images) > 0:
+    if len(st.session_state.predictions) > 0 and len(st.session_state.uploaded_images) > 0:
         prediction_images = []
         for i, uploaded_file in enumerate(st.session_state.uploaded_images):
             actual = 'Cancer' if st.session_state.predictions[i][0] == 0 else 'Non Cancer'
@@ -176,6 +189,7 @@ def evaluate_model(model, images):
             buf.seek(0)
             prediction_images.append((buf, f'prediction_{i + 1}.png'))
 
+        # Create zip file for download
         zip_buffer = io.BytesIO()
         with zipfile.ZipFile(zip_buffer, 'w') as zf:
             for image_buf, filename in prediction_images:
@@ -189,22 +203,27 @@ def evaluate_model(model, images):
             mime='application/zip'
         )
 
+    # Utility function to convert an image to base64 for display
     def image_to_base64(image: Image.Image) -> str:
         buffered = io.BytesIO()
         image.save(buffered, format="PNG")
         return base64.b64encode(buffered.getvalue()).decode()
 
+    # Display logo
     logo_path = "./assets/logo.png"  # Update with your logo file path
     logo_image = Image.open(logo_path)
+
+    # Convert the logo image to base64
     logo_base64 = image_to_base64(logo_image)
 
+    # Display the logo with custom CSS styles
     st.sidebar.markdown(
         f"""
         <img src="data:image/jpeg;base64,{logo_base64}"
             style="border-radius: 30px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); width: 90%; height: auto;" />
         """, unsafe_allow_html=True
     )
 
-# Call the function to show image prediction
-if __name__ == "__main__":
+# Streamlit application runner
+if __name__ == '__main__':
     show_image_prediction()