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"" image = Image.open("./sample_image_cord_test_receipt_00004.png") image.save("cord_sample_receipt1.png") image = Image.open("./sample_image_cord_test_receipt_00012.png") image.save("cord_sample_receipt2.png") pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") 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.
Demonstrations for other types of documents/tasks are available at https://github.com/clovaai/donut
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=[["cord_sample_receipt1.png"], ["cord_sample_receipt2.png"]], cache_examples=False, ) demo.launch()