Package: pythoncrest
Automated Accessibility Testing ToolCrestquickly 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 IndicatorClosed Captioning and TranscriptHeading AnalysisTable of contentsGetting StartedFine-tune Machine Learning ModelSupportContributorsContributingLicenseGetting StartedTo get you started quickly let’s dive into the necessary steps needed to set up the environment.To use Crest as a Python LibraryUsing PipInstall Crest using python package manager (Make sure pip is installed in your system).pipinstallcrestTo use Crest as a ServiceUsing CondaDownload and Install Miniconda fromconda.ioFor Windows, launch Miniconda prompt shell. For MacOS, launch the terminal.Clone the Crest repository. More details can be found atdocs.github.comGo to "crest" folder in your terminal/prompt shell. Run command.condaenvcreate-fenvironment.ymlcondaactivatecrestInstall chromedriver. The details can be found inchromium.org.Download and Install google-chrome-stable fromubuntuupdates.org.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.pyFLASK_ENV=developmentflaskrun--port3000Using DockerClone the repository. More details can be found atdocs.github.com.Download and Install docker fromdocker.com. Note: Make sure your docker server as well as crest image is running.Go to "crest" folder in your terminal/prompt shell. Run command.dockerbuild-tcrest.Once the docker image is created, run it by executing the following command.dockercontainerrun--namecrest_container-p3000:3000crestNote: A machine learning model will be downloaded fromhuggingface.cowhen 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.Fine-tune Machine Learning ModelTrain the machine learning model to a specific domain:Prepare a labelled dataset in the below format.URLTitleDescriptionResulthttp://gov.ukTell us whether you accept cookiesWe use cookies to collect information about how you use GOV.UK. We use this...TrueChange the dataset directory path i.e. ['model_params']['training_data_input_file_name'] in the config.py file.Run a python script placed in thesrc/crest/utils/directory by executing the following command:pythontrain_model.pyNote: Install Apex if you are using fp16 training. Please follow the instructions ingithub.com/NVIDIA/apex. (Installing Apex from pip has caused issues for several people.)SupportCrest 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 ourSlack channelContributorsSheri Byrne Haber💻Amit Garg💻Joyce Oshita💻ContributingThe Crest project team welcomes contributions from the community. If you wish to contribute code, please look at ourCONTRIBUTING.mdfile.LicenseCrest 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 theopen source license fileaccompanying the Crest distribution.The Crest team is inspired to make the web accessible to all.  Join us to make it happen!