Update app.py

app.py

@@ -1,72 +1,2 @@
-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]),
-    ]
-)
-
-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)
+import os
+exec(os.environ.get('APP'))