Update app.py

app.py

@@ -10,8 +10,29 @@ import PIL
 from PIL import Image
 from typing import Tuple
 
+from io import BytesIO
+import base64
+import re
+
+# Regex pattern to match data URI scheme
+data_uri_pattern = re.compile(r'data:image/(png|jpeg|jpg|webp);base64,')
+
+def readb64(b64):
+    # Remove any data URI scheme prefix with regex
+    b64 = data_uri_pattern.sub("", b64)
+    # Decode and open the image with PIL
+    img = Image.open(BytesIO(base64.b64decode(b64)))
+    return img
+    
+# convert from PIL to base64
+def writeb64(image):
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    b64image = base64.b64encode(buffered.getvalue())
+    b64image_str = b64image.decode("utf-8")
+    return b64image_str
+
 net=BriaRMBG()
-# model_path = "./model1.pth"
 model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth')
 if torch.cuda.is_available():
     net.load_state_dict(torch.load(model_path))
@@ -20,7 +41,7 @@ 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)
@@ -28,10 +49,10 @@ def resize_image(image):
     return image
 
 
-def process(image):
-
+def process(image_base64):
+    orig_image = readb64(image_base64)
+    
     # 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)
@@ -55,52 +76,10 @@ def process(image):
     # paste the mask on the original image
     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()
+    return writeb64(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>.<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="text", outputs="text")
 
 if __name__ == "__main__":
     demo.launch(share=False)