Update app.py

app.py

@@ -9,6 +9,8 @@ from briarmbg import BriaRMBG
 import PIL
 from PIL import Image
 from typing import Tuple
+import requests
+from io import BytesIO
 
 net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -20,97 +22,46 @@ def get_url_im(url):
     response = requests.get(url, headers=user_agent)
     return BytesIO(response.content)
 
-def resize_image(image):
-    image_path = get_url_im(image_path)
-    image = Image.open(image_path)
+def resize_image(image_url):
+    image_data = get_url_im(image_url)
+    image = Image.open(image_data)
     image = image.convert('RGB')
     model_input_size = (1024, 1024)
     image = image.resize(model_input_size, Image.BILINEAR)
     return image
 
-'''def process(image):
-
+def process(image_url):
     # 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])
+    orig_image = resize_image(image_url)
+    w, h = orig_im_size = orig_image.size
+    im_np = np.array(orig_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()
+        im_tensor = im_tensor.cuda()
 
-    #inference
-    result=net(im_tensor)
+    # inference
+    result = net(im_tensor)
     # post process
-    result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
+    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)    
+    result = (result - mi) / (ma - mi)
     # image to pil
-    im_array = (result*255).cpu().data.numpy().astype(np.uint8)
+    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]'''
-
-def process(image):
-
-    # prepare input
-    orig_image = Image.fromarray(image)
-    w,h = orig_im_size = orig_image.size
-
-    return orig_image
-    # 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()
-
-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>
-"""
-#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=gr.Textbox(label="Text or Image URL", interactive=True), outputs="image", title=title, description=description)
+iface = gr.Interface(
+    fn=process,
+    inputs=gr.inputs.Textbox(label="Text or Image URL"),
+    outputs=gr.outputs.Image(type="pil", label="Output Image"),
+)
 
-if __name__ == "__main__":
-    demo.launch(share=False)
+iface.launch()