Update app.py

app.py

@@ -6,6 +6,7 @@ from PIL import Image
 from torchvision import transforms
 from cloth_segmentation.networks.u2net import U2NET
 import matplotlib.colors as mcolors
+import traceback
 
 # Load U²-Net
 model_path = "cloth_segmentation/networks/u2net.pth"
@@ -85,36 +86,50 @@ def recolor_dress(image_np, dress_mask, target_color):
 
     return (image_np * (1 - adaptive_feather[..., None] / 255) + img_recolored * (adaptive_feather[..., None] / 255)).astype(np.uint8)
 
-# Main function
+# Main function with enhanced error handling
 def change_dress_color(img, color_prompt):
     if img is None or not color_prompt:
         return img
 
-    img_np = np.array(img)
-    target_bgr = get_bgr_from_color_name(color_prompt)
-
     try:
+        img_np = np.array(img)
+        target_bgr = get_bgr_from_color_name(color_prompt)
+
         dress_mask = segment_dress(img_np)
         if np.sum(dress_mask) < 1000:
             return img
+            
         dress_mask = apply_grabcut(img_np, dress_mask)
         img_recolored = recolor_dress(img_np, dress_mask, target_bgr)
         return Image.fromarray(img_recolored)
+        
     except Exception as e:
         print(f"Error: {e}")
+        traceback.print_exc()
         return img
 
-# Fixed Gradio UI using Interface instead of Blocks
-demo = gr.Interface(
-    fn=change_dress_color,
+# Create a simple function that wraps the main functionality
+def process_image(image, color_name):
+    if image is None:
+        return None
+    return change_dress_color(image, color_name)
+
+# Create Gradio interface with explicit input/output definitions
+iface = gr.Interface(
+    fn=process_image,
     inputs=[
         gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(label="Enter Dress Color", placeholder="e.g. crimson, lavender, sky blue")
+        gr.Textbox(label="Dress Color", placeholder="Enter color name (e.g. blue, lavender, crimson)")
     ],
-    outputs=gr.Image(type="pil", label="Recolored Result"),
-    title="🎨 AI Dress Recolorer - Prompt Based",
-    description="Upload an image and type a color (e.g., 'lavender', 'light green', 'royal blue')."
+    outputs=gr.Image(type="pil", label="Result"),
+    title="🎨 AI Dress Recolorer",
+    description="Upload an image of a person wearing a dress and enter a color name to recolor the dress",
+    examples=[
+        ["examples/dress1.jpg", "red"],
+        ["examples/dress2.jpg", "blue"]
+    ]
 )
 
+# Launch the application
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch(server_name="0.0.0.0", server_port=7860)