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
+)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+birefnet.to(device)
+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(device)
+    # 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)
+    image.save("img.png","PNG")
+    return (image , "img.png")
+
+
+img1 = gr.Image(type= "pil", image_mode="RGBA")
+image = gr.Image(label="Upload an image")
+file = gr.File()
+
+
+chameleon = load_img("chameleon.jpg", output_type="pil")
+
+url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
+demo = gr.Interface(
+    fn, inputs=image, outputs=[img1,file], 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 (WIP 🛠️, works for linux)"
+# )
+
+if __name__ == "__main__":
+    demo.launch()