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
+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):
+    im = load_img(image, output_type="pil")
+    im = im.convert("RGB")
+    origin = im.copy()
+    image = process(im)
+    return image
+
+def process(image):
+    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)
+
+    white_background = Image.new("RGBA", image_size, (255, 255, 255, 255))
+    image.putalpha(mask)
+    combined = Image.alpha_composite(white_background, image)
+
+    return combined
+  
+def process_file(f):
+    name_path = f.rsplit(".",1)[0]+".jpeg"
+    im = load_img(f, output_type="pil")
+    im = im.convert("RGB")
+    transparent = process(im)
+    rgb_image = transparent.convert("RGB")  # Ensure the final image is in RGB mode for JPEG
+    rgb_image.save(name_path)
+    return name_path
+
+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")
+text = gr.Textbox(label="Paste an image URL")
+png_file = gr.File(label="output jpeg file")
+
+
+chameleon = load_img("butterfly.jpg", output_type="pil")
+
+url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
+
+tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["butterfly.jpg"], api_name="png")
+
+
+demo = gr.TabbedInterface(
+    [tab3], ["jpeg"], title="Na Na"
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)