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 = "" return prediction, target task_prompt = f"" # 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'], 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' } 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"" 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)