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import gradio as gr
import re
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
from PIL import Image
def process_filename(filename, question):
print(f"Image file: {filename}")
print(f"Question: {question}")
image = Image.open(filename).convert("RGB")
return process_image(image)
def process_image(image, question):
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)
# prepare decoder inputs
prompt = f"<s_docvqa><s_question>{question}</s_question><s_answer>"
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.to(device),
decoder_input_ids=decoder_input_ids.to(device),
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=False,
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 [True, processor.token2json(sequence)['answer'], ""]
def process_document(image, question):
ret = process_image(image, question)
return ret[1]
description = "DocVQA (document visual question answering)"
demo = gr.Interface(
fn=process_document,
inputs=["image", gr.Textbox(label = "Question" )],
outputs=gr.Textbox(label = "Response" ),
title="Extract data from image",
description=description,
cache_examples=True)
demo.launch()
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