| import os | |
| from PIL import Image | |
| import streamlit | |
| from pix2tex.cli import LatexOCR | |
| from munch import Munch | |
| args = Munch({'config': 'settings/config.yaml', | |
| 'checkpoint': os.path.realpath(os.path.join(os.path.dirname(__file__), 'checkpoints/weights.pth')), | |
| 'no_resize': False}) | |
| model = LatexOCR(args) | |
| if __name__ == '__main__': | |
| streamlit.set_page_config(page_title='LaTeX-OCR') | |
| streamlit.title('LaTeX OCR') | |
| streamlit.markdown( | |
| 'Convert images of equations to corresponding LaTeX code.\n\nThis is based on the `pix2tex` module. Check it out [](https://github.com/lukas-blecher/LaTeX-OCR)') | |
| uploaded_file = streamlit.file_uploader( | |
| 'Upload an image an equation', | |
| type=['png', 'jpg'], | |
| ) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| streamlit.image(image) | |
| else: | |
| streamlit.text('\n') | |
| if streamlit.button('Convert'): | |
| if uploaded_file is not None and image is not None: | |
| with streamlit.spinner('Computing'): | |
| try: | |
| latex_code = model(image) | |
| streamlit.code(latex_code, language='latex') | |
| streamlit.markdown(f'$\\displaystyle {latex_code}$') | |
| except Exception as e: | |
| streamlit.error(e) | |
| else: | |
| streamlit.error('Please upload an image.') | |