A Streamlit component for real-time video processing web apps, streamlit-webrtc | Hacker News Reader

A Streamlit component for real-time video processing web apps, streamlit-webrtc

discuss.streamlit.io

44 pointswhitphx7 comments

Since its inception, I have see streamlit as a jupyter-notebook killer. You have the good of jupyter notebook (lazy execution of python code, nice componnent for displaying information), but can keep code in a decent ide like vs code, and have versionning, linting, etc. But it may be in an alternate reality that didn't happend. An this demo shows even streamlit does not see my way ...

Since a year or so ago, I use streamlit in almost every situation I previously used jupyter and enjoy working with versioning, a real ide, etc.

I dont see how this project breaks from that though. It looks pretty useful for quickly visualizing information common in computer vision. What "does not see your way"?

Maybe you belong to that alternate reality.

Jupyter supports many more languages and GPU compute until Streamlit can do that I don’t think it’s even a contender for JNB for many users.

The variety of supported languages is one aspect of comparison.

GPU support is not. You can use GPUs with Streamlit, since its app scripts are normal Python code just executed through Streamlit framework.

Jupyter and Streamlit are not competitors, but they covers the needs the other lacks.

(Note, I'm Head of Developer Relations for Streamlit, so clearly not a non-biased observer!)

You're absolutely right that Streamlit isn't a Jupyter replacement (we're not trying to be!), in the same way that Spyder isn't a "replacement" for the JetBrains set of IDEs. Maybe Streamlit will one day move to multiple language support, but for now, we're trying to be the best Python library we can be for creating web apps.

As far as GPU-support goes, as mentioned above, Streamlit "supports" GPU in as much as Python does. It's important to separate out front-end/UI from backend. If something is supported with GPU as a backend, "data science" (however defined) functionality, Streamlit supports that in the same way a React app calling a GPU API endpoint or other workflow is "GPU-enabled"

Streamlit and Juypter can often be compared, but I think their subjects are different.

Jupyter is a great lab environment. It's nice for experiments and reporting. Streamlit is a great framework to prototype web apps without frontend knowledge. It's useful especially for ML/CV developers who want to create demo apps, but want to focus on Python code.

Regarding to streamlit-webrtc, which is linked at this post, enhances the advantage of Streamlit stated above, especially in the field of Computer Vision, and its equivalent does not exist in Jupyter world for now.

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