py-shiny VS streamlit

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/ ?

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).

Code is here: https://github.com/rstudio/py-shiny/tree/duckdb-example/examples/duckdb

The equivalent of shiny in python is shiny for python: https://shiny.posit.co/py/

I'm not sure how usable it is, but Shiny for Python exists: https://shiny.rstudio.com/py/

There's Shiny for Python - originally for R - but it's only Alpha status: https://shiny.rstudio.com/py/

2.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.

Developed using Langchain and Streamlit technologies for enhanced performance.

Hello,

Have you ever seen the https://streamlit.io/ ? I think this is what you are looking for.

While building dashboards in Streamlit, I found myself really missing Buefy's (Bulma) modern web components.

Specially due to the inability to add new values to Streamlit's multiselect [1], some missing controls like a polished image carousel [2] or a highly customizable data table.

Long story short, we put together streamfy (Streamlit + Buefy) as an MIT licensed project in GitHub to bring Buefy to Streamlit.

Demo: https://streamfy.streamlit.app

All the form components are implemented, missing half of other non-form UX components. There is plenty of room for PRs, testing, feedback, documentation, example, etc.

Please send issues and contributions to GitHub project [3] and general feedback to X / Twitter [4]

Thanks!

[1] https://github.com/streamlit/streamlit/issues/5348

Here's your savior, let's welcome Streamlit.

Looks cool. How do you see this differing from streamlit? https://streamlit.io/

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

In this tutorial, you'll construct a fully functional Streamlit application from the ground up. Streamlit lets you turn simple data scripts into web applications without traditional front-end tools. This application will be capable of downloading audio from any YouTube video, transcribing it using Deepgram, and then summarizing the content with the assistance of Mistral 7B, all streamlined through the capabilities of Langchain.

Install the Stable Diffusion tools and checkpoints, and run it all with Streamlit.