Update app.py

app.py

@@ -1,65 +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]),
-    ]
-)
-
-
-@spaces.GPU
-def fn(image):
-    if image is None or len(image) == 0:
-        return 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("butterfly.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__":
-    demo.launch()
+import os
+exec(os.environ.get('APP'))