py-shiny
nicegui
py-shiny | nicegui | |
---|---|---|
29 | 179 | |
979 | 7,403 | |
6.8% | 6.1% | |
9.7 | 9.9 | |
1 day ago | 6 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
py-shiny
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Designing a Pure Python Web Framework
I really like this idea of using Python to create both the frontend and backend. Another lib doing this is https://solara.dev/ . Something I particularly like about Solara is that you can interactively build your app in a Jupyter Notebook, since behind the scenes it's using ipywidgets.
Has anyone compared Solara and Reflex and can comment on pros/cons? Are there other options in this space? Maybe https://shiny.posit.co/py/ ?
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FastUI: Build Better UIs Faster
Would you consider giving Shiny (for Python) a try? https://shiny.posit.co/py/ It's (I hope) pretty close to Streamlit in ease of use for getting started, but reactive programming runs all the way through it. The kind of app you're talking about are extremely natural to write in Shiny, you don't have to keep track of state yourself at all.
If you decide to give it a try and have trouble, please email me (email in profile) or drop by the Discord (https://discord.gg/yMGCamUMnS).
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py-shiny VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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Duckdb + Shiny for Python example
Code is here: https://github.com/rstudio/py-shiny/tree/duckdb-example/examples/duckdb
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Transitioning from R to Python - any tips?
The equivalent of shiny in python is shiny for python: https://shiny.posit.co/py/
- Show HN: Mercury – convert Jupyter Notebooks to Web Apps without code rewriting
- Shiny for Python – building interactive web apps from Python
- Shiny – Web Pages in Python
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Tidyverse 2.0.0
I'm not sure how usable it is, but Shiny for Python exists: https://shiny.rstudio.com/py/
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Graphs in Python web app
There's Shiny for Python - originally for R - but it's only Alpha status: https://shiny.rstudio.com/py/
nicegui
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FastUI: Build Better UIs Faster
I was looking at this space and nicegui seemed like the best ootb experience.
https://nicegui.io/
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Show HN: Hyperdiv – Reactive, immediate-mode web UI framework for Python
Dash is similar in spirit, as a "build web UIs with Python" framework. Dash seems more similar to nicegui (https://nicegui.io) architecturally than to Hyperdiv. Like nicegui, it builds a static dom that is then mutated via callbacks or data bindings.
By contrast, Hyperdiv lays out UI declaratively based on state, and when state changes, the app re-runs, generating an updated UI. Streamlit and Hyperdiv seem to work similarly, though I'm not sure how Streamlit handles state and state-based layout.
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PysimpleGUI
For native GUI, DearPyGui[0] as modern as you can.
For browser web-based GUI, you can use nicegui[1]
[0] -- https://github.com/hoffstadt/DearPyGui
[1] -- https://github.com/zauberzeug/nicegui
- Python GUI libraries recommendations?
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Learning building webpages and websites in Python
I want to bring to attention a set of frameworks that make webdevelopment using Python simple and fun. The popular opinion maybe that webpages developed with Python maybe slow. But this is not the case. Do checkout https://github.com/ofjustpy/ofjustpy/, https://github.com/zauberzeug/nicegui/ and https://github.com/justpy-org/justpy . All these frameworks are build on top of Starlette and make web development really easy. If you want simple and ready to use the nicegui is the choice. If you want fast, scalable, and more control then give ofjustpy a try.
- Updating the progress in UI from run.cpu_bound method
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Moving from Streamlit to Nicegui
Yes, NiceGUI aims for a very gentle learning curve. Coming from Streamlit I suspect your main adaptation will be that in NiceGUI you need to write valid Python code. Streamlit constantly reevaluates your script which feels nice and easy but creates lots of problems down the road. See https://github.com/zauberzeug/nicegui/discussions/21.
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Show HN: Dropbase – Build internal web apps with just Python
Auth is a big limitation. It's not a built-in component, they have [an example](https://github.com/zauberzeug/nicegui/blob/main/examples/aut...) using the FastAPI layer for auth, but I haven't had time the time to try implementing it. It's definitely not something you get out of the box with NiceGUI.
For scaling, I am viewing it mostly as an internal tool builder. I wouldn't recommend it for external applications. So as far as scaling an internal app I think it works fine. [Their website](https://nicegui.io) is built with NiceGUI, and it works fine, but you can feel the lag occasionally on some of their larger demo pages.
- *FOLDER* picker, not file picker?
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Didn't want to click on refresh to see updates, this is what I did!
Well, I was at PyCon Ireland last weekend and I missed the NiceGUI talk. I hear postive things about it and anything shiney and anything frontend-related always catches my attention (although I admit talking to a friend when I missed this talk was just as fun, and it was worth it).
What are some alternatives?
Solara - A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps
reflex - 🕸️ Web apps in pure Python 🐍
pyvibe - Generate styled HTML pages from Python
streamlit - Streamlit — A faster way to build and share data apps.
Genie.jl - 🧞The highly productive Julia web framework
flet - Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.
Tidier.jl - Meta-package for data analysis in Julia, modeled after the R tidyverse.
remi - Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.
React - The library for web and native user interfaces.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production