react-virtualized VS streamlit

You may increase the rendering efficiency of tabular and huge list data by using the React Virtualized module. React apps perform better overall when the quantity of requests and DOM elements is limited. React Virtualized is comparable to many other tools; however, what sets it apart from the competition is the sheer volume of features and excellent upkeep.

17.react-virtualized

If you have been using React for awhile, you may have heard of the infamous virtualisation library react-window or it's predecessor react-virtualized

React Virtualized is a React library that helps you work with large lists and tabular data efficiently in React. It has more than 25K stars on GitHub and more than 2.5 million weekly downloads on NPM as of August 2023.

Use virtualization (e.g. react-virtualized) to prevent off-screen components from actually rendering.

Virtualization can be achieved using third-party libraries like react-window or react-virtualized. These libraries provide a way to render only the visible data and load more data as needed, resulting in faster application performance.

You can implement the same pattern on the web when dealing with large amount of data. There are some libraries for React that trivialize this, like https://github.com/bvaughn/react-virtualized

Oh, right. I totally forgot to mention that– but the idea of "less rendering" in this case seems less like a Suspense concern and more like a windowing concern. I've written a few libraries for that stuff (react-window and react-virtualized) although there are others that may fit your needs better. Their main focus is limiting what you're rendering to more or less only what's on the screen at any given point. Combine that with memoized filtering and I would imagine you're set.

I've used the InfiniteLoader from react-virtualized in combination with useInfiniteQuery from @tanstack/react-query and it was relatively painless.

Here’s a react library https://github.com/bvaughn/react-virtualized

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.