Create app.py

app.py

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+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])
+
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+birefnet.to("cpu")
+
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+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 (origin, processed_image)
+
+@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("cpu")
+    # Prediction
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image_size)
+    image.putalpha(mask)
+    return image
+
+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")
+    transparent = process(im)
+    transparent.save(name_path)
+    return name_path
+
+slider1 = gr.ImageSlider(label="Processed Image", type="pil", format="png")
+slider2 = gr.ImageSlider(label="Processed Image from URL", type="pil", format="png")
+image_upload = gr.Image(label="Upload an image")
+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")
+
+tab1 = gr.Interface(fn, inputs=image_upload, outputs=slider1, api_name="image")
+tab2 = gr.Interface(fn, inputs=url_input, outputs=slider2, api_name="text")
+tab3 = gr.Interface(process_file, inputs=image_file_upload, outputs=output_file, api_name="png")
+
+demo = gr.TabbedInterface(
+    [tab1, tab2, tab3], ["Image Upload", "URL Input", "File Output"], title="✂ Image Background Removar ✂"
+)
+
+if __name__ == "__main__":
+    demo.launch(show_error=True, mcp_server=True)