Create app.py

app.py

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+import gradio as gr
+from gradio_imageslider import ImageSlider
+from loadimg import load_img
+import spaces
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+
+torch.set_float32_matmul_precision(["high", "highest"][0])
+
+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]),
+    ]
+)
+
+
+@spaces.GPU
+def fn(image):
+    im = load_img(image, output_type="pil")
+    im = im.convert("RGB")
+    image_size = im.size
+    origin = im.copy()
+    image = load_img(im)
+    input_images = transform_image(image).unsqueeze(0).to("cuda")
+    # 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, origin)
+
+
+slider1 = ImageSlider(label="birefnet", type="pil")
+slider2 = ImageSlider(label="birefnet", type="pil")
+image = gr.Image(label="Upload an image")
+text = gr.Textbox(label="Paste an image URL")
+
+
+chameleon = load_img("chameleon.jpg", output_type="pil")
+
+url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
+tab1 = gr.Interface(
+    fn, inputs=image, outputs=slider1, examples=[chameleon], api_name="image"
+)
+
+tab2 = gr.Interface(fn, inputs=text, outputs=slider2, examples=[url], api_name="text")
+
+
+demo = gr.TabbedInterface(
+    [tab1, tab2], ["image", "text"], title="birefnet for background removal"
+)
+
+if __name__ == "__main__":