mathtext 0.0.17

title: MathText app_file: app.py sdk: gradio sdk_version: 3.15.0 license: agpl-3.0

MathText NLU

Natural Language Understanding for math symbols, digits, and words with a Gradio user interface and REST API.

Setup your Python environment

Launch a terminal on linux (or the git-bash application on Windows). Then create a virtualenv with whatever python version you have available on your system.

Any python version greater than 3.7 should work. Most of us on Linux systems use Python 3.9:

git clone git@gitlab.com:tangibleai/community/mathtext
cd mathtext
pip install --upgrade virtualenv poetry
python -m virtualenv .venv
ls -hal

You should see a new .venv/ directory. It will contain your python interpreter and a few site-packages like pip and distutils.

Now activate your new virtual environment by sourcing .venv/bin/activate (on Linux) or .venv/scripts/activate (on Windows).

source .venv/bin/activate || source .venv/scripts/activate

Developer installation

Once you have a shiny new virtual environment activated you can install the mathtext in --editable mode. This way, when you edit the files and have the package change immediately.

Make sure you are already within your cloned mathtext project directory. And makes sure your virtual environment is activated. You should see the name of your virtual environment in parentheses within your command line prompt, like (.venv) $. Then when you install MathText it will be available to any other application within that environment.

pip install --editable .

User installation

If you don't want to contribute to the MathText source code and you just want to import and run the MathText modules, you can install it from a binary wheel on PyPi.

pip install mathtext

File notes

mathtext
    mathtext: mathtext code
        data: training and test sets for various tasks
        api_gradio.py: gradio api
        api_scaling.py: makes async http requests to the local api
        nlutils_vish.py: various NLP utils
        nlutils.py: various NLP utils
        plot_calls.py: Functions for plotting data
        readme.md: other readme?
        sentiment.py: sets up huggingface sentiment analysis pipeline for the api (gradio or FastAPI?)
        tag_numbers.py: Number and word POS tagger
        text2int.py: text2int function
    scripts: setup scripts
        build.sh
        pyproject.template
    tests: various tests
        __init.py
        test_text2int.py
    .git*: various git files
    api_scaling.sh: makes calls to local api
    app.py: ties all of the api components together for huggingface
    LICENSE.md: license
    pyproject.toml: pyproject file
    README.md: this
    requirements.txt: project dependencies