Update gradio_app.py

gradio_app.py

@@ -190,17 +190,11 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
         rgb_image = generated_image.convert('RGB')
         
         print("[debug] removing the background by calling bg_remover.process(rgb_image)")
+        # The key change: Process returns RGBA with transparent background
         no_bg_image = bg_remover.process(rgb_image)
         
-        print("[debug] converting to numpy array to extract the mask")
-        no_bg_array = np.array(no_bg_image)
-        
-        # Create mask based on RGB values
-        mask = ((no_bg_array > 0).any(axis=2)).astype(np.float32)
-        mask = np.expand_dims(mask, axis=2)  # Add channel dimension
-        
-        print("[debug] creating the RGBA image using create_rgba_image(rgb_image, mask)")
-        rgba_image = create_rgba_image(rgb_image, mask)
+        # Convert the no_bg_image directly to RGBA (it should already be RGBA)
+        rgba_image = Image.fromarray(no_bg_image).convert('RGBA')
         
         print("[debug] auto-cropping the rgba_image using spar3d_utils.foreground_crop(...)")
         processed_image = spar3d_utils.foreground_crop(
@@ -209,6 +203,10 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
             newsize=(COND_WIDTH, COND_HEIGHT),
             no_crop=False
         )
+        
+        # Show the processed image alpha channel for debugging
+        alpha = np.array(processed_image)[:, :, 3]
+        print(f"[debug] Alpha channel stats - min: {alpha.min()}, max: {alpha.max()}, unique: {np.unique(alpha)}")
 
         # Prepare batch for processing
         print("[debug] preparing the batch by calling create_batch(processed_image)")
@@ -253,7 +251,7 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
         import traceback
         traceback.print_exc()
         return None, None
-
+        
 # Create Gradio interface
 demo = gr.Interface(
     fn=generate_and_process_3d,