jupyterlite VS streamlit

The JupyterLite Python-compiled-to-WASM build has NumPy, SciPy, matplotlib, and SymPy installed; so you can do computer algebra with SymPy in a browser tab.

https://JupyterLite.rtfd.io/

https://github.com/jupyterlite/jupyterlite/tree/main/py/jupy... :

> Initial support for interactive visualization libraries such as: altair, bqplot, ipywidgets, matplotlib, and plotly

https://jupyterlite.readthedocs.io/en/latest/howto/extension... :

> We recommend checking out how to create a server extension first

From https://github.com/jupyterlite/jupyterlite/issues/237#issuec... re: 'micropip':

  %pip install $@

https://github.com/emscripten-forge/recipes/tree/main/recipe...

Re: emscripten fs implementations: https://github.com/emscripten-core/emscripten/issues/15041#i... https://github.com/jupyterlite/jupyterlite/issues/315

Agreed that would be a killer feature. Unzip this package and get a functional Python + Jupyter + scientific (numpy, pandas, scipy, matplotlib) environment.

I have been on-and-off teaching some people Python and the initial setup on-ramp is horrible. Ok, so install Python, now ignore-this-for-now-complications: create a "virtualenv", use this thing called "pip", install these half-dozen things to get a basic notebook (Jupyter + scipy things), install these other half-dozen quality of life things, you should probably also have "conda" for the future, etc. That's a lot of nonsense for someone I am trying to show an alternative to Excel.

My shortcut, "You want to try Python?" approach has been to start with JupyterLite[0] where I can immediately get people coding and delay that pain.

[0] https://github.com/jupyterlite/jupyterlite

JupyterLite: a JupyterLab distribution that runs in the browser\ (45 comments)

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.