Update streamlit_app.py

streamlit_app.py

@@ -9,8 +9,8 @@ import os
 from PIL import Image # Needed to display image in Streamlit
 
 # --- Configuration ---
-# Ensure this matches the IMG_SIZE used during training
-IMG_SIZE = 260
+
+IMG_SIZE = 224
 # Define the expected model filename
 MODEL_FILENAME = 'skin_lesion_model.keras'
 # Define class names based on the training script output
@@ -110,19 +110,10 @@ if model is not None:
                         # Display result
                         st.success(f'Prediction: **{label}**')
                         st.metric(label="Confidence", value=f"{confidence:.2%}")
-                        # Optional: Display confidence breakdown
-                        # st.write("Confidence Scores:")
-                        # st.write({name: f"{pred:.2%}" for name, pred in zip(CLASS_NAMES, model.predict(processed_image)[0])})
                     else:
                         st.error("Prediction failed. Please check the logs or try a different image.")
                 else:
                     st.error("Image preprocessing failed. Please ensure the image is valid.")
 else:
-    # Message if model loading failed (already handled in load_skin_model, but good practice)
-    st.warning("Model could not be loaded. Please check the setup.")
-
-# --- How to Run ---
-# Save this code as a Python file (e.g., app.py)
-# Ensure 'skin_lesion_model.keras' is in the same directory.
-# Install libraries: pip install streamlit numpy tensorflow Pillow
-# Run from terminal: streamlit run app.py
+
+    st.warning("Model could not be loaded. Please check the setup.")