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import re
import gradio as gr
import torch
from transformers import DonutProcessor, VisionEncoderDecoderModel
processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def process_document(image, question):
# prepare encoder inputs
pixel_values = processor(image, return_tensors="pt").pixel_values
# 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
# 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
return processor.token2json(sequence)
description = "Neurons Lab Gradio Demo for document visual question answering. To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
demo = gr.Interface(
fn=process_document,
inputs=["image", "text"],
outputs="json",
title="Neurons Lab Demo: DocVQA",
description=description,
enable_queue=True,
examples=[["Tesla_10Q_2023.png", "What is automotive sales revenue in 2022?"], ["Toshiba.png", "What is weight of VN-M150HE?"]],
cache_examples=False)
demo.launch()
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