Crest quickly tests any HTML web page for a simple way to solve your accessibility testing problems. Provide a website URL to our callable API and expose potential WCAG violations.

Currently supports:

• Keyboard Focus Indicator
• Closed Captioning and Transcript
• Heading Analysis

Note: Some websites might have permission issues to access their session storage.

• Getting Started
• Fine-tune Machine Learning Model
• Support
• Contributors
• Contributing
• License

To get you started quickly let’s dive into the necessary steps needed to set up the environment.

1. Install Crest using python package manager (Make sure pip is installed in your system).

pip install pythoncrest

1. Download and Install Miniconda from conda.io

2. For Windows, launch Miniconda prompt shell. For MacOS, launch the terminal.

3. Clone the Crest repository. More details can be found at docs.github.com

4. Go to "crest" folder in your terminal/prompt shell. Run command.

conda env create -f environment.yml
conda activate crest

5. Install chromedriver. The details can be found in chromium.org.

6. Download and Install google-chrome-stable from ubuntuupdates.org.

7. Once all the required libraries are installed, go to 'src/crest' folder and run the below command in your terminal/prompt shell.

FLASK_APP=server.py FLASK_ENV=development flask run --port 3000 --host 0.0.0.0

1. Clone the repository. More details can be found at docs.github.com.

2. Download and Install docker from docker.com. Note: Make sure your docker server as well as crest image is running.

3. Go to "crest" folder in your terminal/prompt shell. Run command.

docker build -t crest .

4. Once the docker image is created, run it by executing the following command.

docker container run --name crest_container -p 3000:3000 crest 

Note: A machine learning model will be downloaded from huggingface.co when you use the Heading Analysis/ Crest Single API for the first time. It will be saved in your system's cache for future use and could take approximately 2 GB of your system's memory.

Train the machine learning model to a specific domain:

1. Prepare a labelled dataset in the below format.

2. Change the dataset directory path i.e. ['model_params']['training_data_input_file_name'] in the config.py file.
3. Run a python script placed in the src/crest/utils/ directory by executing the following command:

    python train_model.py

Note: Install Apex if you are using fp16 training. Please follow the instructions in github.com/NVIDIA/apex. (Installing Apex from pip has caused issues for several people.)

Crest is released as open source software and comes with no commercial support.
But since we want to ensure success and recognize that Crest consumers might fall into a range of roles - from developers that are steeped in the conventions of open-source to customers that are more accustomed to commercial offerings, we offer several methods of engaging with the Crest team and community.
For the Crest community, feel free to join our Slack channel

Sheri Byrne Haber 💻

Amit Garg 💻

Joyce Oshita 💻

The Crest project team welcomes contributions from the community. Before you start working with Crest, please read our Developer Certificate of Origin. All contributions to this repository must be signed as described on that page. Your signature certifies that you wrote the patch or have the right to pass it on as an open-source patch.

If you wish to contribute code, please look at our CONTRIBUTING.md file.

Crest is comprised of many open source software components, each of which has its own license that is located in the source code of the respective component as well as documented in the open source license file accompanying the Crest distribution.