Equalify UIC PDF Analysis

This project includes two key tools for analyzing PDF and HTML pages with the Equalify accessibility scan.

Components

1. Equalify UIC Analysis

This script (equalify-uic-analysis.py) performs automated checks on PDF and HTML files. It:

• Analyzes each PDF's size, page count, text content, and tag structure.
• Supports PDFs hosted on direct links, or PDFs on Box.com if you have specfied Box.com API creds.
• Submits eligible PDFs for advanced accessibility analysis via Equalify’s scan service.
• Submits eligble HTML pages via Equalify's scan service.
• Outputs results to output.csv.

2. Equalify UIC Dashboard

This Streamlit dashboard (equalify-uic-dashboard.py) provides a visual summary of the analysis results. It:

• Displays key metrics like total checks passed/failed and number of files.
• Includes a pie chart and table of failure reasons by description.
• Reads Equalify result JSONs from the results/ folder.

Getting Started

Setup Python Environment

It's recommended to use a Python virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
pip install -r requirements.txt

• Rename input-sample.csv to input.csv in the root directory. Add in data within similar format.
• Run the analysis script:

python equalify-uic-analysis.py

• After the analysis completes, start the dashboard:

streamlit run equalify-uic-dashboard.py

Make sure to install required dependencies (see requirements.txt) and set your Box API credentials in a .env file.

Maintainers

This project is maintained by the Accessibility Engineering team at University of Illinois Chicago (UIC) Technology Solutions.