donut.py

import re
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel


def get_result(image_path, question):
    processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
    model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")

    # load document image from the DocVQA dataset
    image = Image.open(image_path).convert('RGB')

    # prepare decoder inputs
    task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
    prompt = task_prompt.replace("{user_input}", question)
    decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids

    pixel_values = processor(image, return_tensors="pt").pixel_values

    outputs = model.generate(
        pixel_values,
        decoder_input_ids=decoder_input_ids,
        max_length=model.decoder.config.max_position_embeddings,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
    )

    sequence = processor.batch_decode(outputs.sequences)[0]
    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
    print(processor.token2json(sequence))

    return processor.token2json(sequence)['answer']