Update app.py

app.py

@@ -1,2 +1,72 @@
-import os
-exec(os.environ.get('APP’))
+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")
+
+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]),
+    ]
+)
+
+def fn(image):
+    im = load_img(image, output_type="pil")
+    im = im.convert("RGB")
+    origin = im.copy()
+    processed_image = process(im)
+    return (processed_image, origin)
+
+@spaces.GPU
+def process(image):
+    image_size = image.size
+    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
+
+def process_file(f):
+    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 = ImageSlider(label="Processed Image", type="pil")
+slider2 = ImageSlider(label="Processed Image from URL", type="pil")
+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")
+
+# 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")
+
+demo = gr.TabbedInterface(
+    [tab1, tab2, tab3], ["Image Upload", "URL Input", "File Output"], title="Background Removal Tool"
+)
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)