In collaboration with the Data Nutrition Project (DNP), we created a platform for ML assisted surveys to investigate improvemets in response quality for the existing survey used to generate the DNPs Data Nutrition Label (DNL)

Using Python 3.9+

Can install most of the env with conda, but need to run the pip install the torch txt file bc it doesn't play nice with the conda env u:admin pw:secret at login

Then create a new application (or embed to the existing one). See example_app.py as for example. Create config similar to example_config.py. It must contain QUESTIONNAIRE_DIR which must point to a directory with questionnaire files and QUESTIONNAIRE_SUBMISSIONS_DIR where submissions will be written. Don't forget to udpate QUESTIONNAIRE_BASIC_AUTH tuple. It is used for authentication to the questionnaire list and results pages.

Site root provides a list of existing questionnaires with links to forms and results.

Look at questionnaires/texteditor.json for a questionnaire example. Each questionnaire is an object with the following keys:

extends
title
comment
template
submit
questions

With extends you can specify a different json file as a base, it should be prefixed with _ so it will not be considered a stand-alone questionnaire. With template you can specify alternative template. If not specified questionnaire.html is used. questions is a list of objects which represent (surprisingly) questions. Each such object must be one of the following types: string (text), text (textarea), radio and checkbox. radio, checkbox questions must have options. To mark option as "other" field (a field with a text input) prefix it with "+" sign.

You can override templates by creating templates with the same names in your application's template directory or you can specify template option for desired questionnaires.