Update app.py

app.py

@@ -208,109 +208,4 @@ with gr.Blocks(
     )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True)
-```
-
-=== utils.py ===
-```python
-import torch
-import numpy as np
-from PIL import Image
-import matplotlib
-import requests
-from io import BytesIO
-
-def load_image_from_url(url: str) -> Image.Image:
-    """
-    Load an image from a URL.
-    
-    Args:
-        url: Image URL
-        
-    Returns:
-        PIL Image object
-    """
-    try:
-        response = requests.get(url, timeout=10)
-        response.raise_for_status()
-        image = Image.open(BytesIO(response.content))
-        return image.convert("RGB")
-    except Exception as e:
-        raise ValueError(f"Could not load image from URL: {str(e)}")
-
-def validate_image(image: Image.Image) -> bool:
-    """
-    Validate if the image is suitable for processing.
-    
-    Args:
-        image: PIL Image object
-        
-    Returns:
-        True if valid, False otherwise
-    """
-    if image is None:
-        return False
-    
-    if image.size[0] <= 0 or image.size[1] <= 0:
-        return False
-        
-    return True
-
-def resize_for_processing(image: Image.Image, max_size: int = 1024) -> Image.Image:
-    """
-    Resize image for processing while maintaining aspect ratio.
-    
-    Args:
-        image: Input PIL Image
-        max_size: Maximum size for the longer dimension
-        
-    Returns:
-        Resized PIL Image
-    """
-    width, height = image.size
-    if max(width, height) <= max_size:
-        return image
-    
-    if width > height:
-        new_width = max_size
-        new_height = int(height * max_size / width)
-    else:
-        new_height = max_size
-        new_width = int(width * max_size / height)
-    
-    return image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-
-def overlay_masks_advanced(image: Image.Image, masks: torch.Tensor, alpha: float = 0.5) -> Image.Image:
-    """
-    Advanced overlay function with customizable alpha.
-    
-    Args:
-        image: Input PIL Image
-        masks: Segmentation masks tensor
-        alpha: Overlay transparency (0-1)
-        
-    Returns:
-        Overlaid PIL Image
-    """
-    image = image.convert("RGBA")
-    masks = 255 * masks.cpu().numpy().astype(np.uint8)
-    
-    n_masks = masks.shape[0]
-    if n_masks == 0:
-        return image.convert("RGB")
-    
-    # Use a good colormap
-    cmap = matplotlib.colormaps.get_cmap("tab10").resampled(n_masks)
-    colors = [
-        tuple(int(c * 255) for c in cmap(i)[:3])
-        for i in range(n_masks)
-    ]
-
-    for mask, color in zip(masks, colors):
-        mask_img = Image.fromarray(mask)
-        overlay = Image.new("RGBA", image.size, color + (0,))
-        alpha_map = mask_img.point(lambda v: int(v * alpha * 255))
-        overlay.putalpha(alpha_map)
-        image = Image.alpha_composite(image, overlay)
-    
-    return image
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True)