Update app.py

app.py

@@ -3,7 +3,6 @@ import torch
 from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
 from PIL import Image
 
-# Load model and processor
 model_id = "brucewayne0459/paligemma_derm"
 processor = AutoProcessor.from_pretrained(model_id)
 model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
@@ -59,11 +58,9 @@ elif uploaded_file:
         st.error(f"Error loading image: {str(e)}")
         input_image = None
 
-# Display and process the image
 with col2:
     if input_image:
         try:
-            # Display the uploaded or captured image
             resized_image = input_image.resize((300, 300))
             st.image(resized_image, caption="Selected Image (300x300)", use_container_width=True)
 
@@ -72,7 +69,6 @@ with col2:
             processed_image = input_image.resize(max_size)
 
             with st.spinner("Processing..."):
-                # Prepare inputs for the model
                 inputs = processor(
                     text=prompt,
                     images=processed_image,
@@ -80,12 +76,10 @@ with col2:
                     padding="longest"
                 ).to(device)
 
-                # Generate output from the model
-                default_max_tokens = 50  # Default value for max tokens
+                default_max_tokens = 50
                 with torch.no_grad():
                     outputs = model.generate(**inputs, max_new_tokens=default_max_tokens)
 
-                # Decode and clean the output
                 decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
                 if prompt in decoded_output:
                     decoded_output = decoded_output.replace(prompt, "").strip()