TUI-apps VS streamlit

Hello!

I am pleased to announce a new version of my "CLI text processing with GNU sed" ebook. This book heavily leans on examples to present features one by one. In addition to sed commands and options, regular expressions are also discussed in detail.

Links:

* PDF/EPUB versions: https://learnbyexample.gumroad.com/l/gnu_sed (free for a few days)

* Web version: https://learnbyexample.github.io/learn_gnused/ (always free)

* Markdown source, example files, etc: https://github.com/learnbyexample/learn_gnused

* Interactive TUI app for exercises: https://github.com/learnbyexample/TUI-apps/blob/main/SedExercises

I would highly appreciate it if you'd let me know how you felt about this book. It could be anything from a simple thank you, pointing out a typo, mistakes in code snippets, which aspects of the book worked for you (or didn't!) and so on. Reader feedback is essential and especially so for self-published authors.

Happy learning :)

I also wrote an interactive TUI app based on some of the exercises from the ebook. Reference solutions are provided for both GNU grep and ripgrep.

These days I prefer coding TUI apps using Textual (see my repo https://github.com/learnbyexample/TUI-apps for examples) since I find this framework easier to work with and I don't need full GUI features for my projects.

This year I started updating my existing books and I'm also creating interactive apps. Here's one for Python regex: https://github.com/learnbyexample/TUI-apps/blob/main/PyRegexExercises. Next is grep.

Having an interactive program that automatically loads questions and checks the solution is wonderful to have while learning a topic. I wrote a TUI app that has beginner to advanced level exercises for Python regular expressions. There are more than 100 exercises covering both the builtin re and third-party regex modules.

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.