A general purpose leaderboard using Streamlit.
Can be used for small machine learning competitions, or just for conveniently keeping score of the performance of various methods/models.
This repository requires Python 3.6 or higher.
Run the following commands to clone the repository and install streamlit-leaderboard:
git clone https://github.com/yairf11/streamlit-leaderboard.git
cd streamlit-leaderboard; pip install -r requirements.txt
To run the leaderboard, just run the following command:
streamlit run src/app.py
You can easily configure the leaderboard for your purposes. In the file config.py, you can edit the following variables which control the app's most basic behavior:
EVALUATOR_CLASS: A subclass of the Evaluator class
used for the evaluation of the users' submissions.
This is the most important configuration,
and you must implement your own evaluator class.
See this example implementation
for detailed information.
SUBMISSIONS_DIR: A pathlib.Path object
holding the path to the directory in which the users' submissions will be saved.
PASSWORDS_DB_FILE: A pathlib.Path object
holding the path to the encrypted passwords DB.
ARGON2_KWARGS: A dictionary holding keyword arguments for the
argon2 password hasher.
MAX_NUM_USERS: The maximum number of users allowed in the system. If None, no limitation is enforced.
ALLOWED_SUBMISSION_FILE_EXTENSION: The extension type required for a
submission file (e.g. ".json"). If None, any extension is allowed.
SHOW_TOP_K_ONLY: The number of top competitors to display on the leaderboard. Users ranked below the k'th place will not be visible to other users.
ADMIN_USERNAME: The username of the admin of the leaderboard. The admin cannot submit to the leaderboard, but he can view the entire leaderboard (regardless of the SHOW_TOP_K_ONLY parameter) and view the personal progress of each competitor.
Lastly, if you want to change some of Streamlit's defaults,
you can edit them in the config.toml file.
Currently, the maximum size allowed for a submission file is 1 MB (maxUploadSize = 1).