Spaces:
Runtime error
Runtime error
File size: 2,890 Bytes
994b44c fb6be5e 994b44c 6a94555 fb6be5e 6a94555 fb6be5e 6a94555 994b44c fb6be5e 994b44c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
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)
demo = gr.Interface(
fn=process_document,
inputs= gr.inputs.Image(type="pil"),
outputs="json",
title=f"Demo: Donut 🍩 for DocVQA",
description = "Gradio Demo for Donut, an instance of `VisionEncoderDecoderModel` fine-tuned on DocVQA (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."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>"
image = gr.inputs.Image(type="pil")
question = gr.inputs.Textbox(label="Question")
interface = gr.Interface(fn=answer_question,
inputs=[image, question],
outputs="json",
examples=examples,
title=title,
description=description,
article=article,
enable_queue=True)
interface.launch(debug=True)
examples=[["example_1.png", "When is the coffee break?"]],
cache_examples=False,
)
demo.launch() |