Delete app.py

app.py

@@ -1,119 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-from huggingface_hub import hf_hub_download
-import gradio as gr
-from briarmbg import BriaRMBG
-import PIL
-from PIL import Image
-
-net = BriaRMBG()
-model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth')
-if torch.cuda.is_available():
-    net.load_state_dict(torch.load(model_path))
-    net = net.cuda()
-else:
-    net.load_state_dict(torch.load(model_path, map_location="cpu"))
-net.eval()
-
-def resize_image(image):
-    image = image.convert('RGB')
-    model_input_size = (1024, 1024)
-    image = image.resize(model_input_size, Image.BILINEAR)
-    return image
-
-def process(image):
-    orig_image = Image.fromarray(image)
-    w, h = orig_image.size
-    image = resize_image(orig_image)
-    im_np = np.array(image)
-    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
-    im_tensor = torch.unsqueeze(im_tensor, 0)
-    im_tensor = torch.divide(im_tensor, 255.0)
-    im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
-    if torch.cuda.is_available():
-        im_tensor = im_tensor.cuda()
-
-    result = net(im_tensor)
-    result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode='bilinear'), 0)
-    ma = torch.max(result)
-    mi = torch.min(result)
-    result = (result - mi) / (ma - mi)
-    im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
-    pil_im = Image.fromarray(np.squeeze(im_array))
-    new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
-    new_im.paste(orig_image, mask=pil_im)
-
-    return new_im
-
-css = """
-body { 
-    font-family: 'Arial', sans-serif; 
-    margin: 0; 
-    padding: 0; 
-    background-color: #f0f2f5; 
-    color: #333; 
-}
-h1 { 
-    color: #0000ff; 
-}
-p { 
-    color: #000000; 
-}
-.gradio-app, .gradio-content { 
-    background-color: #ffffff; 
-    border-radius: 8px; 
-    border: 1px solid #ccc; 
-    box-shadow: 0 10px 25px 0 rgba(0,0,0,0.1); 
-    padding: 20px; 
-}
-button { 
-    border: none; 
-    color: white; 
-    padding: 10px 20px; 
-    margin: 10px 0; 
-    cursor: pointer; 
-    border-radius: 5px; 
-    background-image: linear-gradient(to right, #6a11cb 0%, #2575fc 100%); 
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2); 
-    transition: all 0.2s ease-in-out; 
-}
-button:hover { 
-    box-shadow: 0 6px 8px rgba(0, 0, 0, 0.3); 
-}
-input, textarea { 
-    border: 2px solid #2575fc; 
-    border-radius: 4px; 
-    padding: 10px; 
-    margin: 10px 0; 
-    width: 100%; 
-    box-sizing: border-box; 
-    box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.1); 
-}
-.gradio-toolbar { 
-    background-color: #f0f2f5; 
-}
-footer { 
-    visibility: hidden; 
-}
-"""
-
-title = "Background Removal"
-description = """
-This is a demo for BRIA RMBG 1.4 using the BRIA RMBG-1.4 image matting model as a backbone.<br>
-Background removal model developed by <a href='https://BRIA.AI' target='_blank'><b>BRIA.AI</b></a>, trained on a carefully selected dataset and is available as an open-source model for non-commercial use.<br> 
-For a test, upload your image and wait. Read more at the model card <a href='https://huggingface.co/briaai/RMBG-1.4' target='_blank'><b>briaai/RMBG-1.4</b></a>.<br>
-"""
-
-demo = gr.Interface(
-    fn=process,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title=title,
-    description=description,
-    css=css
-)
-
-if __name__ == "__main__":
-    demo.launch(share=False)