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| """ | |
| Donut | |
| Copyright (c) 2022-present NAVER Corp. | |
| MIT License | |
| https://github.com/clovaai/donut | |
| """ | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from donut import DonutModel | |
| def demo_process(input_img): | |
| global pretrained_model, task_prompt, task_name | |
| # input_img = Image.fromarray(input_img) | |
| output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0] | |
| return output | |
| # task_prompt = f"<s_cord-v2>" | |
| task_prompt = f"<s_receipts>" | |
| device = 'cpu' # 'cuda' if torch.cuda.is_available() else 'cpu' | |
| pretrained_model: DonutModel = DonutModel.from_pretrained("result", local_files_only=True) | |
| pretrained_model.to(device) | |
| pretrained_model.eval() | |
| demo = gr.Interface( | |
| fn=demo_process, | |
| inputs= gr.inputs.Image(type="pil"), | |
| outputs="json", | |
| title=f"Donut 🍩 demonstration for `cord-v2` task", | |
| description="""This model is trained with 800 Indonesian receipt images of CORD dataset. <br> | |
| Demonstrations for other types of documents/tasks are available at https://github.com/clovaai/donut <br> | |
| More CORD receipt images are available at https://huggingface.co/datasets/naver-clova-ix/cord-v2 | |
| More details are available at: | |
| - Paper: https://arxiv.org/abs/2111.15664 | |
| - GitHub: https://github.com/clovaai/donut""", | |
| examples=[["sample_image_cord_test_receipt_00004.png"], ["sample_image_cord_test_receipt_00012.png"]], | |
| cache_examples=False, | |
| ) | |
| demo.launch(server_name="0.0.0.0") | |