gradio VS streamlit

There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.

It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.

What is the business model for https://www.taipy.io/, https://streamlit.io/, or https://www.gradio.app/? These are nice tools - but how will the sponsoring businesses support themselves? I didn't see any mention of enterprise plans, etc. Is the answer simply that "we've not announced our revenue model yet"? What should one expect?

Repo : https://github.com/gradio-app/gradio

There's TensorFlow.js for running machine learning on JavaScript, but personally, I'd prefer using the Python Gradio package, which is designed for creating UIs for machine learning inference demos.

I found an issue on gradio github but looks like it's closed so I am not sure if it's still a common issue or only I am facing it due to certain settings/absence of a fix. ( https://github.com/gradio-app/gradio/issues/3060 )

I disagree hard with this – for instance I've recently needed to dig into the code for the Gradio library, and when PRs are like https://github.com/gradio-app/gradio/pull/3300 (and the merge commit's message is what it is) it's hard to understand why some decisions have been made when doing `git annotate` later on.

Gradio for a ui that your commanderGPT can visit and use

gradio is documented, it doesn't seem very complex, it would be something like moving this block under the other one. i think it's ui_extra_networks.py, the file you are looking to edit. (if you do it make a copy to restore when you go to update)

Gradio did have an issue for a while where your URL was guessable, so unless you had a password it was pretty easy to find, but as far as I know they've increased the complexity so much that it's no longer an issue.

My script wouldn't work on the langchain because it was literally made within the gradio "docs builder" script that afaik was made specifically for their website (repo)

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.