import os import torch import logging import argparse import streamlit as st import nltk import evaluate from PIL import Image from transformers import AutoProcessor from transformers import VisionEncoderDecoderModel from src.utils import common_utils from nltk import edit_distance as compute_edit_distance from src.utils.common_utils import compute_exprate bleu_func = evaluate.load("bleu") wer_func = evaluate.load("wer") exact_match_func = evaluate.load("exact_match") logging.basicConfig( level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s" ) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) def main(args): @st.cache_resource def init_model(): # Get the device device = common_utils.check_device(logger) # Init model logger.info("Load model & processor from: {}".format(args.ckpt)) model = VisionEncoderDecoderModel.from_pretrained( args.ckpt ).to(device) # Load processor processor = AutoProcessor.from_pretrained(args.ckpt) task_prompt = processor.tokenizer.bos_token decoder_input_ids = processor.tokenizer( task_prompt, add_special_tokens=False, return_tensors="pt" ).input_ids return model, processor, decoder_input_ids, device model, processor, decoder_input_ids, device = init_model() @st.cache_data def inference(input_image): # Load image logger.info("\nLoad image from: {}".format(input_image)) image = Image.open(input_image) if not image.mode == "RGB": image = image.convert('RGB') pixel_values = processor.image_processor( image, return_tensors="pt", data_format="channels_first", ).pixel_values # Generate LaTeX expression with torch.no_grad(): outputs = model.generate( pixel_values.to(device), decoder_input_ids=decoder_input_ids.to(device), max_length=model.decoder.config.max_length, pad_token_id=processor.tokenizer.pad_token_id, eos_token_id=processor.tokenizer.eos_token_id, use_cache=True, num_beams=4, bad_words_ids=[[processor.tokenizer.unk_token_id]], return_dict_in_generate=True, ) sequence = processor.tokenizer.batch_decode(outputs.sequences)[0] sequence = sequence.replace( processor.tokenizer.eos_token, "" ).replace( processor.tokenizer.pad_token, "" ).replace(processor.tokenizer.bos_token,"") logger.info("Output: {}".format(sequence)) return sequence @st.cache_data def compute_crohme_metrics(label_str, pred_str): wer = wer_func.compute(predictions=[pred_str], references=[label_str]) # Compute expression rate score exprate, error_1, error_2, error_3 = compute_exprate( predictions=[pred_str], references=[label_str] ) return round(wer*100, 2), round(exprate*100, 2), round(error_1*100, 2), round(error_2*100, 2), round(error_3*100, 2) @st.cache_data def compute_img2latex100k_metrics(label_str, pred_str): # Compute edit distance score edit_distance = compute_edit_distance( pred_str, label_str )/max(len(pred_str),len(label_str)) # Convert minimun edit distance score to maximun edit distance score edit_distance = round((1 - edit_distance)*100, 2) # Compute bleu score bleu = bleu_func.compute( predictions=[pred_str], references=[label_str], max_order=4 # Maximum n-gram order to use when computing BLEU score ) bleu = round(bleu['bleu']*100, 2) exact_match = exact_match_func.compute( predictions=[pred_str], references=[label_str] ) exact_match = round(exact_match['exact_match']*100, 2) return bleu, edit_distance, exact_match # --------------------------------- Sreamlit code --------------------------------- st.markdown("

Math Formula Images To LaTeX Code Based On End-to-End Approach With Attention Mechanism

", unsafe_allow_html=True) st.write('') st.write('') st.write('') st.header('Input', divider='blue') uploaded_file = st.file_uploader( "Upload an image", type = ['png', 'jpg'], ) if uploaded_file is not None: bytes_data = uploaded_file.read() st.image( bytes_data, width = 700, channels = 'RGB', output_format = 'PNG' ) on = st.toggle('Enable testing with label') if on: with st.container(border=True): option = st.selectbox( 'Benchmark ?', ('Im2latex-100k', 'CROHME')) label = st.text_input('Label', None) run = st.button("Run") if run is True and uploaded_file is not None and label is not None and option == 'Im2latex-100k': pred_str = inference(uploaded_file) st.header('Output', divider='blue') st.latex(pred_str) st.write(':orange[Latex sequences:]', pred_str) bleu, edit_distance, exact_match = compute_img2latex100k_metrics(label, pred_str) with st.container(border=True): col1, col2, col3 = st.columns(3) col1.metric("Bleu", bleu) col2.metric("Edit Distance", edit_distance) col3.metric("Exact Match", exact_match) if run is True and uploaded_file is not None and label is not None and option == 'CROHME': pred_str = inference(uploaded_file) st.header('Output', divider='blue') st.latex(pred_str) st.write(':orange[Latex sequences:]', pred_str) wer, exprate, error_1, error_2, error_3 = compute_crohme_metrics(label, pred_str) with st.container(border=True): col1, col2, col3, col4, col5 = st.columns(5) col1.metric("ExpRate", exprate) col2.metric("ExpRate 1", error_1) col3.metric("ExpRate 2", error_2) col4.metric("ExpRate 3", error_3) col5.metric("WER", wer) else: run = st.button("Run") if run is True and uploaded_file is not None: pred_str = inference(uploaded_file) st.write('') st.header('Output', divider='blue') st.latex(pred_str) st.write(':orange[Latex sequences:]', pred_str) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Sumen Latex OCR") parser.add_argument( "--ckpt", type=str, default="checkpoints", help="Path to the checkpoint", ) args = parser.parse_args() main(args)