click VS dash

Context: "click" - "Command Line Interface Creation Kit" - easily create CLIs from Python code, via adding decorators: https://github.com/pallets/click

"click-web" in turn turns the click CLI app into a web app with one line of code.

there is also click, it is more straight forward and also nice to keep the relevant code where the code is. https://github.com/pallets/click/

I don't have huge experience with Python, but I used async code with C#/Typescript and lately I had to use some asyncio magic.

I found this article: https://blog.dalibo.com/2022/09/12/monitoring-python-subproc... and while async/await syntax is the same, it's not entirely clear for me, why there's some event loop and what exactly happens, when I pass function to asyncio.run(), like here: https://github.com/pallets/click/issues/85#issuecomment-5034...

So, you can use it and it's not that hard, but there are some parts that are vague for me, no matter which language implements async support.

Hmm… did you try such approaches, as [click](https://github.com/pallets/click) or[tap](https://github.com/swansonk14/typed-argument-parser)?

This app leverages these powerful libraries: - yt-dlp: video downloader - moviepy: video trimmer/merger - click: CLI app creator - rich / rich-click: CLI app styler

I've used click [1] a lot to build Python tooling scripts the past few years. Click usage is "sort of" similar to the author's proposed solution. There's also a small section here [2] that describes some of the issues covered in the article (in context of argparse).

[1] - https://github.com/pallets/click

I think the success of Arduino in the hardware world can be explained in a similar way, as the relative success of "command line app frameworks" like Click[1], or even much lighter-weight libraries like argparse[2]. You absolutely can get away with using just getopt[3] (and people experienced with it will likely strongly prefer it). However certain factors such as a more declarative API, a nice logo, the existence of an ecosystem (even if you're not actively drawing from it), an official "branded" forum, etc can all play into picking a more complex solution, with more baggage you don't need, certain oddities that may throw users off, etc.

[1]: https://click.palletsprojects.com/

[2]: https://docs.python.org/3/library/argparse.html

[3]: https://man.openbsd.org/getopt.3, https://linux.die.net/man/3/getopt

Exactly! You can also use a library like click (https://github.com/pallets/click) to help take care of the command line side, while you focus on the 'business logic' of your application :)

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.