ELDonationTracker VS dash

https://github.com/djotaku/ELDonationTracker depends on

http://djotaku.github.io/ELDonationTracker/ - for tracking donations to Extra Life and displaying when Live Streaming

Python - it’s my main language and the one in which I’m most proficient. I have written some programs used by many others (like my Extra Life Donation Tracker) and utilities that solve some problem I have (like my btrfs snapshot and backup program). By solving each problem in Python first, I allow myself to focus on the problem first instead of a syntax I’m unfamiliar with.

If you want to move to GUI programs, I recommend : Qt5 Python GUI Programming Cookbook. Using QT Designer you can GUI programs pretty quickly. I used this book to create the GUI for my Extra Life project: http://djotaku.github.io/ELDonationTracker/

not sure if this is going beyond automation, but my most complex Python program which is actually used by other folks is for generating Donation Alerts while Live-Streaming to Twitch (or wherever) - http://djotaku.github.io/ELDonationTracker/

I use JSON and a dataclass (not a literal dataclass w/ the decorator and all). See ParticipantConf class starting line 103 https://github.com/djotaku/ELDonationTracker/blob/master/eldonationtracker/utils/extralife_io.py

In my project, here are the files that QT Designer saves: https://github.com/djotaku/ELDonationTracker/tree/master/QTdesignerfiles

I wanted to be able to use the Donation API when doing the Extra Life fundraiser, so I made: https://github.com/djotaku/ELDonationTracker

I'm using it for my program as well. It's really awesome, especially if you use qt designer. https://github.com/djotaku/ELDonationTracker

Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.

You could use something like Streamlit or Dash. In any case you will be accessing your app through the browser.

Useful list. Dash & bokeh as two more in the space

https://github.com/plotly/dash

I also heard of Dash which serves the same purpose I guess, but I think it has more to offer.

Plotly is a plotting library, and Dash is their open-source framework for building data apps with Python, R or Julia. (Dash also has an Enterprise version, but we'll focus on the open-source library here.)

Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.

looks like you can get it manually (albeit with a loss of interactivity) https://github.com/plotly/dash/issues/145

Shiny is a framework that makes it easy to build interactive web applications. Shiny was introduced 10 years ago as an R package. In his 10th anniversary keynote speech, Joe Cheng announced Shiny for Python at the 2022 RStudio Conference. Python programmers can now try out Shiny to create interactive data-driven web applications. Shiny comes as an alternative to other frameworks, like Dash, or Streamlit.