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""" |
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Donut |
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Copyright (c) 2022-present NAVER Corp. |
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MIT License |
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https://github.com/clovaai/donut |
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""" |
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import gradio as gr |
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import torch |
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from PIL import Image |
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from donut import DonutModel |
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def demo_process(input_img): |
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global pretrained_model, task_prompt, task_name |
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output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0] |
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return output |
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task_prompt = f"<s_cord-v2>" |
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image = Image.open("./sample_image_cord_test_receipt_00004.png") |
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image.save("cord_sample_receipt1.png") |
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image = Image.open("./sample_image_cord_test_receipt_00012.png") |
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image.save("cord_sample_receipt2.png") |
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pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") |
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pretrained_model.eval() |
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demo = gr.Interface( |
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fn=demo_process, |
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inputs= gr.inputs.Image(type="pil"), |
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outputs="json", |
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title=f"Donut 🍩 demonstration for `cord-v2` task", |
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description="""This model is trained with 800 Indonesian receipt images of CORD dataset. <br> |
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Demonstrations for other types of documents/tasks are available at https://github.com/clovaai/donut <br> |
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More CORD receipt images are available at https://huggingface.co/datasets/naver-clova-ix/cord-v2 |
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More details are available at: |
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- Paper: https://arxiv.org/abs/2111.15664 |
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- GitHub: https://github.com/clovaai/donut""", |
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examples=[["cord_sample_receipt1.png"], ["cord_sample_receipt2.png"]], |
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cache_examples=False, |
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) |
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demo.launch() |
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