Update app.py

app.py

@@ -1,66 +1,6 @@
-import os
-import torch
 import gradio as gr
-from PIL import Image
-from process import load_seg_model, get_palette, generate_mask
-
-# Device selection
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-def load_model():
-    """Load model with Hugging Face Spaces compatible paths"""
-    model_dir = 'model'
-    checkpoint_path = os.path.join(model_dir, 'cloth_segm.pth')
-    
-    # Verify model exists (must be pre-uploaded to HF Spaces)
-    if not os.path.exists(checkpoint_path):
-        raise FileNotFoundError(
-            f"Model not found at {checkpoint_path}. "
-            "Please upload the model file to your Space's repository."
-        )
-    
-    try:
-        net = load_seg_model(checkpoint_path, device=device)
-        palette = get_palette(4)
-        return net, palette
-    except Exception as e:
-        raise RuntimeError(f"Model loading failed: {str(e)}")
-
-# Initialize model (will fail fast if there's an issue)
-net, palette = load_model()
-
-def process_image(img: Image.Image) -> Image.Image:
-    """Process input image and return segmentation mask"""
-    if img is None:
-        raise gr.Error("Please upload an image first")
-    
-    try:
-        return generate_mask(img, net=net, palette=palette, device=device)
-    except Exception as e:
-        raise gr.Error(f"Processing failed: {str(e)}")
-
-# Gradio interface
-title = "Cloth Segmentation Demo"
-description = """
-Upload an image to get cloth segmentation using U2NET.
-"""
 
 with gr.Blocks() as demo:
-    gr.Markdown(f"## {title}")
-    gr.Markdown(description)
-    
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(type="pil", label="Input Image")
-            submit_btn = gr.Button("Process", variant="primary")
-        with gr.Column():
-            output_image = gr.Image(type="pil", label="Segmentation Result")
-    
-    submit_btn.click(
-        fn=process_image,
-        inputs=input_image,
-        outputs=output_image
-    )
+    gr.Markdown("# Welcome to Gradio! 🎉")
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()