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import torch
import streamlit as st
from PIL import Image
from transformers import VisionEncoderDecoderModel, VisionEncoderDecoderConfig , DonutProcessor
def run_prediction(sample):
global pretrained_model, processor, task_prompt
if isinstance(sample, dict):
# prepare inputs
pixel_values = torch.tensor(sample["pixel_values"]).unsqueeze(0)
else: # sample is an image
# prepare encoder inputs
pixel_values = processor(image, return_tensors="pt").pixel_values
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
# run inference
outputs = pretrained_model.generate(
pixel_values.to(device),
decoder_input_ids=decoder_input_ids.to(device),
max_length=pretrained_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,
)
# process output
prediction = processor.batch_decode(outputs.sequences)[0]
# post-processing
if "cord" in task_prompt:
prediction = prediction.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
prediction = re.sub(r"<.*?>", "", prediction, count=1).strip() # remove first task start token
prediction = processor.token2json(prediction)
# load reference target
if isinstance(sample, dict):
target = processor.token2json(sample["target_sequence"])
else:
target = "<not_provided>"
return prediction, target
task_prompt = f"<s>"
st.text('''
This is OCR-free Document Understanding Transformer nicknamed 🍩. It was fine-tuned with 1000 receipt images -> SROIE dataset.
The original 🍩 implementation can be found on: https://github.com/clovaai/donut
''')
with st.sidebar:
information = st.radio(
"What information inside the are you interested in?",
('Receipt Summary', 'Receipt Menu Details', 'Extract all!'))
receipt = st.selectbox('Pick one receipt', ['1', '2', '3', '4', '5', '6'], index=5)
st.text(f'{information} mode is ON!\nTarget receipt: {receipt}\n(opening image @:./img/receipt-{receipt}.png)')
image = Image.open(f"./img/receipt-{receipt}.jpg")
st.image(image, caption='Your target receipt')
st.text(f'baking the 🍩...')
processor = DonutProcessor.from_pretrained("unstructuredio/donut-base-sroie")
pretrained_model = VisionEncoderDecoderModel.from_pretrained("unstructuredio/donut-base-sroie")
pretrained_model.encoder.to(torch.bfloat16)
pretrained_model.eval()
st.text(f'parsing receipt..')
parsed_receipt_info = run_prediction(image)
st.text(f'\nRaw output:\n{parsed_receipt_info}') |