Adds full docstrings for your main functions, MCP server ready !

#21
by fffiloni - opened
Files changed (1) hide show
  1. app.py +37 -1
app.py CHANGED
@@ -21,6 +21,21 @@ transform_image = transforms.Compose(
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  )
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  def fn(image):
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  im = load_img(image, output_type="pil")
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  im = im.convert("RGB")
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  origin = im.copy()
@@ -29,6 +44,18 @@ def fn(image):
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  @spaces.GPU
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  def process(image):
 
 
 
 
 
 
 
 
 
 
 
 
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  image_size = image.size
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  input_images = transform_image(image).unsqueeze(0).to("cuda")
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  # Prediction
@@ -41,6 +68,15 @@ def process(image):
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  return image
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  def process_file(f):
 
 
 
 
 
 
 
 
 
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  name_path = f.rsplit(".", 1)[0] + ".png"
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  im = load_img(f, output_type="pil")
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  im = im.convert("RGB")
@@ -68,4 +104,4 @@ demo = gr.TabbedInterface(
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  )
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  if __name__ == "__main__":
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- demo.launch(show_error=True)
 
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  )
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  def fn(image):
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+ """
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+ Remove the background from an image and return both the transparent version and the original.
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+
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+ This function performs background removal using a BiRefNet segmentation model. It is intended for use
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+ with image input (either uploaded or from a URL). The function returns a transparent PNG version of the image
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+ with the background removed, along with the original RGB version for comparison.
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+
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+ Args:
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+ image (PIL.Image or str): The input image, either as a PIL object or a filepath/URL string.
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+
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+ Returns:
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+ tuple:
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+ - processed_image (PIL.Image): The input image with the background removed and transparency applied.
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+ - origin (PIL.Image): The original RGB image, unchanged.
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+ """
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  im = load_img(image, output_type="pil")
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  im = im.convert("RGB")
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  origin = im.copy()
 
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  @spaces.GPU
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  def process(image):
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+ """
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+ Apply BiRefNet-based image segmentation to remove the background.
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+
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+ This function preprocesses the input image, runs it through a BiRefNet segmentation model to obtain a mask,
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+ and applies the mask as an alpha (transparency) channel to the original image.
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+
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+ Args:
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+ image (PIL.Image): The input RGB image.
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+
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+ Returns:
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+ PIL.Image: The image with the background removed, using the segmentation mask as transparency.
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+ """
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  image_size = image.size
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  input_images = transform_image(image).unsqueeze(0).to("cuda")
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  # Prediction
 
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  return image
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  def process_file(f):
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+ """
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+ Load an image file from disk, remove the background, and save the output as a transparent PNG.
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+
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+ Args:
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+ f (str): Filepath of the image to process.
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+
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+ Returns:
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+ str: Path to the saved PNG image with background removed.
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+ """
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  name_path = f.rsplit(".", 1)[0] + ".png"
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  im = load_img(f, output_type="pil")
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  im = im.convert("RGB")
 
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  )
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  if __name__ == "__main__":
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+ demo.launch(show_error=True, mcp_server=True)