Eel VS streamlit

I gave up a long time ago attempting to write GUIs in pure python. I've come back to them periodically to see if the landscape has changed, but I'm always disappointed.

My current go-to python GUI solution is to use the Eel library. It renders GUIs in a web-browser like window with HTMl/CSS/JS.It lets you expose your python functions to JavaScript, so data can be easily transferred back and forth between the python and the JavaScript. And since it's just web-dev, it's pretty easy to make things look and feel good enough. Any other web-dev libraries like bootstrap and jquery can be used. It works pretty well for writing GUIs that put data analysis tools in front of my colleagues.

https://github.com/python-eel/Eel

Eel was used to implement the app and communication with the web interface.

I am calling a function from Python using eel and taking the return value. Python functions are asynchronous with JavaScript by nature. This code is combined with other variables to determine if the form can continue. allFilled() is called everytime the form field changes.

In the past, I've used QT4 and QT Designer. It was okay. It was a small mountain to climb for myself then - but certainly doable. However, there was a lot of generated code that wasn't the most concise thing. Today, I discovered Eel. Since I used QT4 in the past, I've done some web dev. Certainly, not proficient there. But Eel wasn't as big of a hurdle. In fact, it took me a fraction of the time because it is much like creating a basic website. Where there's an abundance of tutorials and tips out there for everything imaginable. Instead of downloading and adding a font, clicking through a bunch of dialogs. I just linked a Google font and updated the CSS. Stuff like that.

If you don't want a web-app, you can still use JS for GUI, using something like python-eel https://github.com/python-eel/Eel

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.