change to white background

app.py

@@ -5,6 +5,7 @@ import spaces
 from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
+from PIL import Image
 
 torch.set_float32_matmul_precision(["high", "highest"][0])
 
@@ -25,9 +26,8 @@ def fn(image):
     im = im.convert("RGB")
     origin = im.copy()
     image = process(im)
-    return (image, origin)
+    return image
 
-@spaces.GPU
 def process(image):
     image_size = image.size
     input_images = transform_image(image).unsqueeze(0).to("cuda")
@@ -37,8 +37,12 @@ def process(image):
     pred = preds[0].squeeze()
     pred_pil = transforms.ToPILImage()(pred)
     mask = pred_pil.resize(image_size)
+
+    white_background = Image.new("RGBA", image_size, (255, 255, 255, 255))
     image.putalpha(mask)
-    return image
+    combined = Image.alpha_composite(white_background, image)
+
+    return combined
   
 def process_file(f):
     name_path = f.rsplit(".",1)[0]+".png"
@@ -48,7 +52,7 @@ def process_file(f):
     transparent.save(name_path)
     return name_path
 
-slider1 = ImageSlider(label="birefnet", type="pil")
+slider1 = gr.Image()
 slider2 = ImageSlider(label="birefnet", type="pil")
 image = gr.Image(label="Upload an image")
 image2 = gr.Image(label="Upload an image",type="filepath")
@@ -68,8 +72,8 @@ tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["bu
 
 
 demo = gr.TabbedInterface(
-    [tab1, tab2,tab3], ["image", "text","png"], title="birefnet for background removal"
+    [tab3], ["png"], title="Na Na"
 )
 
 if __name__ == "__main__":
-    demo.launch(show_error=True)
+    demo.launch(auth=("nana", "2024"), share=True)