Update app.py

app.py

@@ -1,60 +1,30 @@
 import gradio as gr
 from loadimg import load_img
-import spaces
 from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 from typing import Union, Tuple
 from PIL import Image
 
-torch.set_float32_matmul_precision(["high", "highest"][0])
+torch.set_float32_matmul_precision("high")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 birefnet = AutoModelForImageSegmentation.from_pretrained(
     "ZhengPeng7/BiRefNet", trust_remote_code=True
 )
-birefnet.to("cuda")
-
-transform_image = transforms.Compose(
-    [
-        transforms.Resize((1024, 1024)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ]
-)
+birefnet.to(device)
 
-def fn(image: Union[Image.Image, str]) -> Tuple[Image.Image, Image.Image]:
-    """
-    Remove the background from an image and return both the transparent version and the original.
-    This function performs background removal using a BiRefNet segmentation model. It is intended for use
-    with image input (either uploaded or from a URL). The function returns a transparent PNG version of the image
-    with the background removed, along with the original RGB version for comparison.
-    Args:
-        image (PIL.Image or str): The input image, either as a PIL object or a filepath/URL string.
-    Returns:
-        tuple:
-            - processed_image (PIL.Image): The input image with the background removed and transparency applied.
-            - origin (PIL.Image): The original RGB image, unchanged.
-    """
-    im = load_img(image, output_type="pil")
-    im = im.convert("RGB")
-    origin = im.copy()
-    processed_image = process(im)
-    return (processed_image, origin)
+transform_image = transforms.Compose([
+    transforms.Resize((1024, 1024)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225]),
+])
 
-@spaces.GPU
 def process(image: Image.Image) -> Image.Image:
-    """
-    Apply BiRefNet-based image segmentation to remove the background.
-    This function preprocesses the input image, runs it through a BiRefNet segmentation model to obtain a mask,
-    and applies the mask as an alpha (transparency) channel to the original image.
-    Args:
-        image (PIL.Image): The input RGB image.
-    Returns:
-        PIL.Image: The image with the background removed, using the segmentation mask as transparency.
-    """
     image_size = image.size
-    input_images = transform_image(image).unsqueeze(0).to("cuda")
-    # Prediction
+    input_images = transform_image(image).unsqueeze(0).to(device)
     with torch.no_grad():
         preds = birefnet(input_images)[-1].sigmoid().cpu()
     pred = preds[0].squeeze()
@@ -63,17 +33,15 @@ def process(image: Image.Image) -> Image.Image:
     image.putalpha(mask)
     return image
 
+def fn(image: Union[Image.Image, str]) -> Tuple[Image.Image, Image.Image]:
+    im = load_img(image, output_type="pil").convert("RGB")
+    origin = im.copy()
+    processed_image = process(im)
+    return processed_image, origin
+
 def process_file(f: str) -> str:
-    """
-    Load an image file from disk, remove the background, and save the output as a transparent PNG.
-    Args:
-        f (str): Filepath of the image to process.
-    Returns:
-        str: Path to the saved PNG image with background removed.
-    """
     name_path = f.rsplit(".", 1)[0] + ".png"
-    im = load_img(f, output_type="pil")
-    im = im.convert("RGB")
+    im = load_img(f, output_type="pil").convert("RGB")
     transparent = process(im)
     transparent.save(name_path)
     return name_path
@@ -85,17 +53,22 @@ image_file_upload = gr.Image(label="Upload an image", type="filepath")
 url_input = gr.Textbox(label="Paste an image URL")
 output_file = gr.File(label="Output PNG File")
 
-# Example images
 chameleon = load_img("butterfly.jpg", output_type="pil")
 url_example = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
 
-tab1 = gr.Interface(fn, inputs=image_upload, outputs=slider1, examples=[chameleon], api_name="image")
-tab2 = gr.Interface(fn, inputs=url_input, outputs=slider2, examples=[url_example], api_name="text")
-tab3 = gr.Interface(process_file, inputs=image_file_upload, outputs=output_file, examples=["butterfly.jpg"], api_name="png")
+tab1 = gr.Interface(fn, inputs=image_upload, outputs=slider1,
+                   examples=[chameleon], api_name="image")
+tab2 = gr.Interface(fn, inputs=url_input, outputs=slider2,
+                   examples=[url_example], api_name="text")
+tab3 = gr.Interface(process_file, inputs=image_file_upload,
+                   outputs=output_file, examples=["butterfly.jpg"],
+                   api_name="png")
 
 demo = gr.TabbedInterface(
-    [tab1, tab2, tab3], ["Image Upload", "URL Input", "File Output"], title="Background Removal Tool"
+    [tab1, tab2, tab3],
+    ["Image Upload", "URL Input", "File Output"],
+    title="Background Removal Tool"
 )
 
 if __name__ == "__main__":
-    demo.launch(show_error=True, mcp_server=True)
+    demo.launch(show_error=True, server_name="0.0.0.0", server_port=7860)