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import re
import gradio
import torch
import pandas as pd
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel
processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def process_document(image):
# prepare encoder inputs
pixel_values = processor(image, return_tensors="pt").pixel_values
# prepare decoder inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
# generate answer
outputs = model.generate(
pixel_values.to(device),
decoder_input_ids=decoder_input_ids.to(device),
max_length=model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
num_beams=1,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
# postprocess
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
# js = processor.token2json(sequence)
return {
'text_requirements': 'all_pass',
'symbol_requirements': 'all_pass',
'language_requirements': 'all_pass'
}
demo = gradio.Interface(
fn=process_document,
inputs="image",
outputs="json",
title="Donut Text Parsing",
description=None,
article=None,
examples=None,
cache_examples=False)
demo.launch(enable_queue=True) |