a0a7 commited on
Commit
2806aea
·
1 Parent(s): f199650
Files changed (3) hide show
  1. README.md +1 -1
  2. app.py +6 -23
  3. requirements.txt +1 -1
README.md CHANGED
@@ -4,7 +4,7 @@ emoji: 🖋️
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  colorFrom: blue
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  colorTo: purple
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  sdk: gradio
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- sdk_version: 4.36.0
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  app_file: app.py
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  pinned: false
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  license: mit
 
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  colorFrom: blue
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  colorTo: purple
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  sdk: gradio
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+ sdk_version: 4.20.0
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  app_file: app.py
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  pinned: false
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  license: mit
app.py CHANGED
@@ -2,7 +2,6 @@ import gradio as gr
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  import random
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  from PIL import Image
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- # Create simple interface
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  def recognize_image(image):
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  """Main function for the Gradio interface"""
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  if image is None:
@@ -30,36 +29,20 @@ def recognize_image(image):
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  if display_image.size[0] > 600 or display_image.size[1] > 400:
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  display_image.thumbnail((600, 400), Image.Resampling.LANCZOS)
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- formatted_result = f"**Recognized Text:** {result}\n**Confidence:** {confidence:.1%}"
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  return formatted_result, display_image
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  except Exception as e:
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  return f"Error processing image: {str(e)}", image
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  demo = gr.Interface(
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  fn=recognize_image,
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- inputs=gr.Image(type="pil", label="Upload Gregg Shorthand Image"),
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- outputs=[
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- gr.Textbox(label="Recognition Result", lines=3),
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- gr.Image(label="Processed Image")
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- ],
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- title="🖋️ Gregg Shorthand Recognition",
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- description="Upload an image of Gregg shorthand notation to convert it to readable text!",
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- article="""
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- ### About This Demo
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- This is a demonstration of Gregg shorthand recognition using AI.
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- Gregg shorthand was a popular stenographic writing system used for over a century.
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-
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- **Tips for best results:**
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- - Use clear, high-contrast images
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- - Ensure good lighting
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- - Crop to focus on the shorthand text
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-
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- For more information, visit the [GitHub repository](https://github.com/a0a7/GreggRecognition).
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- """,
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- examples=None,
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- cache_examples=False
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  )
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  if __name__ == "__main__":
 
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  import random
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  from PIL import Image
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  def recognize_image(image):
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  """Main function for the Gradio interface"""
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  if image is None:
 
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  if display_image.size[0] > 600 or display_image.size[1] > 400:
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  display_image.thumbnail((600, 400), Image.Resampling.LANCZOS)
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+ formatted_result = f"Recognized Text: {result}\nConfidence: {confidence:.1%}"
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  return formatted_result, display_image
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  except Exception as e:
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  return f"Error processing image: {str(e)}", image
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+ # Create interface with minimal configuration
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  demo = gr.Interface(
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  fn=recognize_image,
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+ inputs=gr.Image(type="pil"),
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+ outputs=[gr.Textbox(), gr.Image()],
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+ title="Gregg Shorthand Recognition",
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+ description="Upload an image of Gregg shorthand notation to convert it to readable text!"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  )
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  if __name__ == "__main__":
requirements.txt CHANGED
@@ -1,2 +1,2 @@
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- gradio==4.36.0
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  Pillow>=8.0.0
 
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+ gradio==4.20.0
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  Pillow>=8.0.0