SuffolkLITLab / RateMyPDF

RateMyPDF is a website that helps paper form authors (particularly for court forms) improve the usability of their forms for self-represented litigants. It uses the FormFyxer library to deliver its insights.
https://ratemypdf.com
MIT License
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RateMyPDF

RateMyPDF is a website that helps paper form authors (particularly for court forms) improve the usability of their forms for self-represented litigants. It uses the FormFyxer library to deliver its insights.

The first version of this website ran on Flask. This repository replaces it with a version on FastAPI.

It has been described in a paper published in the proceedings of ICAIL '23. You can view it here.

Running locally

Install requirements:

Start redis queue to handle incoming jobs

cd ~/RateMyPDF/app
rq worker

Start the fastapi app, setting the redis URL to localhost

cd ~/RateMyPDF/app
REDIS_URL=redis://localhost:6379 python main.py

The site should now be available at http://localhost:8000

Starting in Docker

Copy the .env.example file to .env

DOMAIN=ratemypdf.com
OPEN_AI__org=org-
OPEN_AI__key=sk-
SPOT_TOKEN=
SECRET_KEY=
TOOLS_TOKEN=
IN_DOCKER=TRUE
REDIS_URL=redis://ratemypdf_redis:6379

Fill in the missing values with the appropriate domain name, key, etc.

Access to the spot and tools tokens is available only by contacting suffolklitlab@gmail.com

Preferred citation format

Please cite this repository as follows:

Quinten Steenhuis, Bryce Willey, and David Colarusso. 2023. Beyond Readability with RateMyPDF: A Combined Rule-based and Machine Learning Approach to Improving Court Forms. In Proceedings of International Conference on Artificial Intelligence and Law (ICAIL 2023). ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3594536.3595146

Bibtex format:

@article{Steenhuis_Willey_Colarusso_2023, title={Beyond Readability with RateMyPDF: A Combined Rule-based and Machine Learning Approach to Improving Court Forms}, DOI={https://doi.org/10.1145/3594536.3595146}, journal={Proceedings of International Conference on Artificial Intelligence and Law (ICAIL 2023)}, author={Steenhuis, Quinten and Willey, Bryce and Colarusso, David}, year={2023}, pages={287–296}}