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import torch
import streamlit as st
from PIL import Image
from io import BytesIO
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. set_page_config(layout="wide")
# logo = Image.open("./img/rsz_unstructured_logo.png")
# st.image(logo)
st.markdown('''
### Donut Common Crawl
Experimental OCR-free Document Understanding Vision Transformer nicknamed 🍩, fine-tuned with few samples of the common-crawl with some specific document elements.
''')
with st.sidebar:
information = st.radio(
"Choose one predictor:?",
('Base Common-Crawl 🍩', 'Hierarchical Common-Crawl 🍩'))
image_choice = st.selectbox('Pick one 📑', ['1', '2', '3', '4', '5'], index=1)
st.text(f'{information} mode is ON!\nTarget 📑: {image_choice}') # \n(opening image @:./img/receipt-{receipt}.png)')
col1, col2 = st.columns(2)
image_choice_map = {
'1': 'commoncrawl_amandalacombznewspolice-bust-man-sawed-oal_1.jpg',
'2': 'commoncrawl_canyonhillschroniclecomtagwomens-basketbll_0.png',
'3': 'commoncrawl_celstuttgartdeideaa-different-stort-of-nfe_0.png',
'4': 'commoncrawl_abusoftbplacednetsearchphpsearchidunanswae_0.png',
'5': 'commoncrawl_addxorrolblogspotcomessays-about-managemml_1.png'
}
image = Image.open(f'samples/{image_choice_map[image_choice]}')
with col1:
st.image(image, caption='Your target sample')
if st.button('Parse sample! 🐍'):
image = image.convert('RGB')
image.save('./target_image.jpg')
image = Image.open('./target_image.jpg')
with st.spinner(f'baking the 🍩s...'):
if information == 'Base Common-Crawl 🍩':
processor = DonutProcessor.from_pretrained("laverdes/donut-web") # laverdes/donut-commoncrawl
pretrained_model = VisionEncoderDecoderModel.from_pretrained("laverdes/donut-web") # laverdes/donut-commoncrawl
task_prompt = f"<s>"
device = "cuda" if torch.cuda.is_available() else "cpu"
pretrained_model.to(device)
elif information == 'Hierarchical Common-Crawl 🍩':
st.info("Not implemented yet...")
with col2:
st.info(f'parsing 📑...')
parsed_info, _ = run_prediction(image)
st.text(f'\n{information}')
st.json(parsed_info) |