Update app.py

app.py

@@ -3,17 +3,15 @@ import torch
 from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
 from PIL import Image
 
-# Load the model and processor
 model_id = "brucewayne0459/paligemma_derm"
 processor = AutoProcessor.from_pretrained(model_id)
 model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
 model.eval()
 
-# Set device
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
-# Add Hugging Face logo at the top
+# logo (pakai yg huggingface dulu)
 st.markdown(
     """
     <style>
@@ -33,21 +31,19 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-# Streamlit app title and instructions
 st.title("VisionDerm")
 st.write("Upload an image or use your camera to identify the skin condition.")
 
-# Column layout for input and display
 col1, col2 = st.columns([3, 2])
 
 with col1:
-    # File uploader for image
+    # File uploader
     uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    # Camera input for photo capture
+    # Camera input
     camera_photo = st.camera_input("Take a photo")
     prompt = 'Identify the skin condition?'
 
-# Choose input image (either uploaded or taken by camera)
+# Choose input image
 input_image = None
 if camera_photo:
     input_image = Image.open(camera_photo)
@@ -56,7 +52,6 @@ elif uploaded_file:
 
 with col2:
     if input_image:
-        # Display the image
         resized_image = input_image.resize((300, 300))
         st.image(resized_image, caption="Selected Image (300x300)", use_container_width=True)
 
@@ -64,10 +59,8 @@ with col2:
         max_size = (512, 512)
         processed_image = input_image.resize(max_size)
 
-        # Predict automatically when the image is uploaded or captured
         with st.spinner("Processing..."):
             try:
-                # Prepare inputs
                 inputs = processor(
                     text=prompt,
                     images=processed_image,
@@ -75,28 +68,23 @@ with col2:
                     padding="longest"
                 ).to(device)
 
-                # Generate output
                 default_max_tokens = 50  # Set a default value for max tokens
                 with torch.no_grad():
                     outputs = model.generate(**inputs, max_new_tokens=default_max_tokens)
 
-                # Decode output and remove the prompt text
                 decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
                 if prompt in decoded_output:
                     decoded_output = decoded_output.replace(prompt, "").strip()
 
-                # Capitalize the first letter of each word
                 decoded_output = decoded_output.title()
 
-                # Display result
                 st.success("Analysis Complete!")
                 st.write("**Model Output:**", decoded_output)
 
             except Exception as e:
                 st.error(f"Error: {str(e)}")
 
-# Add team information at the bottom
-st.markdown("---")  # Add a horizontal line for separation
+st.markdown("---")
 st.info("""
 ### Team: Mahasigma Berprestasi
 - **Muhammad Karov Ardava Barus** ; 103052300001