Delete app (1).py

app (1).py

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-import numpy as np
-import torch
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-import gradio as gr
-from gradio_imageslider import ImageSlider
-from briarmbg import BriaRMBG
-import PIL
-from PIL import Image
-from typing import Tuple
-
-
-net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net.to(device)
-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):
-
-    # prepare input
-    orig_image = Image.fromarray(image)
-    w,h = orig_im_size = 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()
-
-    #inference
-    result=net(im_tensor)
-    # post process
-    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)    
-    # image to pil
-    result_array = (result*255).cpu().data.numpy().astype(np.uint8)
-    pil_mask = Image.fromarray(np.squeeze(result_array))
-    # add the mask on the original image as alpha channel
-    new_im = orig_image.copy()
-    new_im.putalpha(pil_mask)
-    return new_im
-    # return [new_orig_image, new_im]
-
-
-gr.Markdown("## BRIA RMBG 1.4")
-gr.HTML('''
-  <p style="margin-bottom: 10px; font-size: 94%">
-    This is a demo for BRIA RMBG 1.4 that using
-    <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. 
-  </p>
-''')
-title = "Background Removal"
-description = r"""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 test upload your image and wait. Read more at model card <a href='https://huggingface.co/briaai/RMBG-1.4' target='_blank'><b>briaai/RMBG-1.4</b></a>. To purchase a commercial license, simply click <a href='https://go.bria.ai/3ZCBTLH' target='_blank'><b>Here</b></a>. <br>
-"""
-examples = [['./input.jpg'],]
-demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description)
-
-if __name__ == "__main__":
-    demo.launch(share=False)