In the past few years there has been an explosion of Dashboard frameworks. Plotly Dash and Streamlit are examples that are focused more on Data Science analysis whereas Tableau and Qlik represent more of a BI (Business Intelligence) minimal coding approach. But all of them provide frameworks for building complete interactive web-based applications with event-driven inter-component messaging and often utilities for back-end data access (though this is often easiest to do with discipline-specific Python packages).
We have chosen Plotly Dash for reasons that will be outlined in the background documentation and coded the interface itself in Python because of the breadth of data access and processing tools available in that language.
The Background document covers the background for the decision in general and makes a more detailed argument for the choice of Dash.
Everything needed for running the system is captured in installable Python modules and associated Jupyter Notebooks. This is covered in the Installation instructions.
All of our code is open-source and relatively-easy to modify. The source code for anyone that wishes to extend or customize the interface is available through GitHub.
The first example interface / notebook was developed for the Keck Observatory Archive, which is using it operationally. Other, more generic examples are being added.