Update app.py

app.py

@@ -4,103 +4,116 @@ import torch.nn.functional as F
 from torchvision.transforms.functional import normalize
 from huggingface_hub import hf_hub_download
 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()
-# model_path = "./model1.pth"
+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()
+    net = net.cuda()
 else:
-    net.load_state_dict(torch.load(model_path,map_location="cpu"))
-net.eval() 
+    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):
-
-    # prepare input
     orig_image = Image.fromarray(image)
-    w,h = orig_im_size = orig_image.size
+    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])
+    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()
+        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)
+    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)    
-    # image to pil
-    im_array = (result*255).cpu().data.numpy().astype(np.uint8)
+    result = (result - mi) / (ma - mi)
+    im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
     pil_im = Image.fromarray(np.squeeze(im_array))
-    # paste the mask on the original image
-    new_im = Image.new("RGBA", pil_im.size, (0,0,0,0))
+    new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
     new_im.paste(orig_image, mask=pil_im)
-    # new_orig_image = orig_image.convert('RGBA')
 
     return new_im
-    # return [new_orig_image, new_im]
-
-
-# block = gr.Blocks().queue()
 
-# with block:
-#     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>
-#     ''')
-#     with gr.Row():
-#         with gr.Column():
-#             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
-#             # input_image = gr.Image(sources=None, type="numpy") # None for upload, ctrl+v and webcam
-#             run_button = gr.Button(value="Run")
-            
-#         with gr.Column():
-#             result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[1], height='auto')
-#     ips = [input_image]
-#     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
-
-# block.launch(debug = True)
-
-# block = gr.Blocks().queue()
+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; 
+}
+"""
 
-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>.<br>
+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>
 """
-examples = [['./input.jpg'],]
-# output = ImageSlider(position=0.5,label='Image without background', type="pil", show_download_button=True)
-# demo = gr.Interface(fn=process,inputs="image", outputs=output, examples=examples, title=title, description=description)
-demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description)
+
+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)
+    demo.launch(share=False)