pyodide VS streamlit

We implemented an in-browser Python editor/interpreter built on Pyodide over at Comet (our users are data scientists who need to build custom visualizations quite often, and the most familiar language for most of them is Python).

One of the issues you'll run into is that Pyodide only works by default with packages that have pure Python wheels available. The team has developed support for some libraries with C dependencies (like scikit-learn, I believe), but frameworks like PyTorch are particularly thorny (see this issue: https://github.com/pyodide/pyodide/issues/1625 )

We ended up rolling out a new version of our Python visualizations that runs off-browser, in order to support enough libraries/get the performance we need: https://www.comet.com/docs/v2/guides/comet-ui/experiment-man...

Thank you! Yes, one of the items in the Roadmap is support for Pyodide (https://github.com/pyodide/pyodide) for running in-browser python on the results of each of the code blocks! This should allow most ML libs to be usable in-browser! This is pretty high-up on our priority list.

If I understand correctly, WASM only makes sense for compiled languages, you can run the python interpreter in WASM of course[1], but that will be at a significant performance disadvantage to the native javascript interpreter, and it's also something that has to be loaded every time you load the website.

[1]: https://github.com/pyodide/pyodide

If you absolutely need something comparable to Sympy, then one option might be to figure out how to best call Sympy from Rust. e.g. - RustPython, although it seems like Sympy isn't supported yet - Pyodide, and figuring out how to run it outside of a web browser. Probably also not very easy. - PyPy, and having a pretty simple Python binary for every platform - ...

For running programming languages other than JavaScript in the browser there is Emscripten and WebAssembly. There is v86, where a Linux build is compiled to WASM. Folks have written QuickJS into a Linux build compiled to WASM, Node.js into the Linux buildroot https://github.com/cemalgnlts/now, so Python or CPython can be written to the image and loaded into the browser as WASM as well https://github.com/pyodide/pyodide.

There's a Python runtime that runs on WebAssembly (https://github.com/pyodide/pyodide). I have no idea what it's like, I've never used it.

Still in a quest to provide some tooling to quickly compose documentation websites: https://github.com/synw/docdundee . As I have tons of libs to document and was tired of managing restructured language for readthedocs I started with this, and now it has executable Python examples in the frontend via a Pyodide wrapper composable: usePython

This project's brand new and a lil hacky, but I've already reached out to the scikit-learn team, and they recommended that I experiment with using Pyodide as an alternative backend for the Python bridge.

Streamlit on GitHub: Explore the source code and contribute at github.com/streamlit/streamlit.

On the other hand, pure Python libraries like Dash and Streamlit can be great for small projects, but they are limited to a specific use case and don't have the features and performance to build a full web app. As your app grows in features and complexity, you may find yourself hitting the limits of the framework, at which point you either have to limit your idea to fit the framework, or scrap your project and rebuild it using a "real web framework".

Where to check it out: https://github.com/streamlit/streamlit

We are now all set to deploy our app. First upload the codebase in a GitHub repository. Then click here to sign in to your streamlit account and go to My Apps section:

Now, let’s build a real-time application using Streamlit that connects to the WebSocket server and receives live updates.

Deployment and monitoring Once the tests are successful, you can pull the necessary artifacts (commonly a docker image) to the production server and deploy the updates. You can also build a monitoring dashboard using your favorite tools (Grafana, Streamlit, etc.) to gauge application metrics or employ model monitoring for deployed machine learning models. Use the following commands to unpack the code from your remote registry to your deployment server and deploy it.

Real-time Messaging Application Example Let’s create a basic real-time messaging application using FastAPI for the backend and WebSockets for real-time communication. We’ll use Streamlit for the frontend to display messages.

Regarding the implementation, we have chosen Streamlit as the base of our operations, helping us tie up the outputs generated by the API calls to a chat interface. Unfortunately, the larger model asks for a higher VRAM, which I have chosen to fulfill using Tune Studio's API Calls.

Streamlit, an open-source tool, allowed me to create a chatbot interface with less than 10 lines of code. It’s designed to handle user sessions and responses in the backend while providing an attractive frontend user interface.