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47# 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:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
pip install -r requirements.txt
```
1. Rename `input-sample.csv` to `input.csv` in the root directory. Add in data within similar format.
2. Run the analysis script:
```bash
python equalify-uic-analysis.py
```
3. After the analysis completes, start the dashboard:
```bash
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](https://it.uic.edu/accessibility/engineering/) at University of Illinois Chicago (UIC) Technology Solutions.