45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
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from PIL import Image
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import gradio as gr
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-handwritten')
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model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten')
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def ocr(image):
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pixel_values = processor(images=image, return_tensors="pt").pixel_values
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generated_ids = model.generate(pixel_values)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return generated_text
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>OCR</font></div>
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""")
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with gr.Row():
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with gr.Column():
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box1 = gr.Image(label="图片")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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box2 = gr.Textbox(label="文本")
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button.click(fn=ocr, inputs=box1, outputs=box2)
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examples = gr.Examples(examples=[['handwritten.jpeg']], inputs=[box1], label="例子")
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0")
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