remi VS streamlit

Have you considered a remote use case, using something like https://github.com/rawpython/remi ? My SD machine is a headless Linux box, so I'd love to be able to run this on the server where it's local to the images (and perhaps have a multi-image / gallery view)?

How does it compare with remi? https://github.com/rawpython/remi

Looking at the examples, for quick UIs, REMI seems simpler. And PySimpleGUI (https://github.com/PySimpleGUI/PySimpleGUI) offers REMI as a backend to deploy on web too (PySimpleGUI is pretty simple to learn).

Making GUIs is always a fair amount of work/overhead on whatever you are working on, and I like to stay in Python when possible (I've used REMI (https://github.com/rawpython/remi) to create web apps before in Python). Thanks for creating this. I look forward to updates!

This is cool. How does it compare to Remi? I haven’t used it but I am curious

remi works by pre-defining js and html elements

I'd like to point to the excellent REMI [1] library that provides similar capabilities to Lona. You can create web apps by only writing Python. They can also be executed as GUI programs. You can even choose REMI to be the output backend of PySimpleGUI [2].

Not sure what the differences are, but good to know there are similar approaches :) Maybe every project can learn from each other. Thanks for sharing!

[1] https://github.com/dddomodossola/remi

I have installed the last version of REMI (from https://github.com/dddomodossola/remi). When I launch the examples, like hello_world, all is perfectly displayed but the obtained UI is not reactive: no effect when I click on buttons, etc. I tried with Python 2 and 3.

Check this out: https://github.com/dddomodossola/remi

I never used mpld3, but you can see an example of matplotlib here remi/matplotlib_app.py at master · dddomodossola/remi (github.com) .

python3 -m pip install git+https://github.com/dddomodossola/remi.git

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.