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.
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
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
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}}