Delete app.py

app.py

@@ -1,90 +0,0 @@
-import os
-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.backends.mkldnn.enabled = True  # Enable CPU optimizations
-
-# Load model on CPU
-birefnet = AutoModelForImageSegmentation.from_pretrained(
-    "briaai/RMBG-2.0", trust_remote_code=True
-)
-birefnet.to("cpu")
-
-# Reduce image size for efficiency
-transform_image = transforms.Compose(
-    [
-        transforms.Resize((512, 512)),  # Reduced from 1024x1024
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ]
-)
-
-output_folder = 'output_images'
-os.makedirs(output_folder, exist_ok=True)
-
-def fn(image):
-    im = load_img(image, output_type="pil").convert("RGB")
-    origin = im.copy()
-    image = process(im)    
-    image_path = os.path.join(output_folder, "no_bg_image.png")
-    image.save(image_path)
-    return (image, origin), image_path
-
-def process(image):
-    image_size = image.size
-    input_images = transform_image(image).unsqueeze(0).to("cpu")
-    
-    with torch.inference_mode():  # Efficient inference mode
-        preds = birefnet(input_images)[-1].sigmoid()
-    
-    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").convert("RGB")
-    transparent = process(im)
-    transparent.save(name_path)
-    return name_path
-
-slider1 = ImageSlider(label="RMBG-2.0", type="pil")
-slider2 = ImageSlider(label="RMBG-2.0", 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 png file")
-
-chameleon = load_img("giraffe.jpg", output_type="pil")
-url = "http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg"
-
-tab1 = gr.Interface(
-    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[chameleon], api_name="image"
-)
-tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=[url], api_name="text")
-tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png")
-
-demo = gr.TabbedInterface(
-    [tab1, tab2], ["input image", "input url"], title=(
-    "RMBG-2.0 for background removal <br>"
-    "<span style='font-size:16px; font-weight:300;'>"
-    "Background removal model developed by "
-    "<a href='https://bria.ai' target='_blank'>BRIA.AI</a>, trained on a carefully selected dataset,<br> "
-    "and is available as an open-source model for non-commercial use.</span><br>"
-    "<span style='font-size:16px; font-weight:500;'> For testing upload your image and wait.<br>"
-    "<a href='https://go.bria.ai/3ZCBTLH' target='_blank'>Commercial use license</a> | "
-    "<a href='https://huggingface.co/briaai/RMBG-2.0' target='_blank'>Model card</a> | "
-    "<a href='https://blog.bria.ai/brias-new-state-of-the-art-remove-background-2.0-outperforms-the-competition' target='_blank'>Blog</a>"
-    "</span>")
-)
-
-if __name__ == "__main__":
-    demo.launch(show_api=True,show_error=True)
-