Build a Web-Based Real-Time Computer Vision App with Streamlit

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • ssl-proxy

    :lock: Simple zero-config SSL reverse proxy with real autogenerated certificates (LetsEncrypt, self-signed, provided)

    If you have to serve the app on hosts inside local networks, you can use some reverse proxy software like nginx. In prototyping phase, https://github.com/suyashkumar/ssl-proxy might be a simple but useful option.

  • streamlit-webrtc

    Real-time video and audio streams over the network, with Streamlit.

    Streamlit is a great framework for data scientists, machine learning researchers and developers, and streamlit-webrtc extends it to be able to deal with real-time video (and audio) streams. It means you can implement your computer vision algorithms only in Python on server-side and users can use them as web apps with real-time video inputs from their webcams or smartphones.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • streamlit-webrtc-example

    Real time video and audio processing examples with Streamlit and streamlit-webrtc

    One of the easiest way to do it is to use some hosting services like Heroku, which provides HTTPS serving by default. Especially for apps using streamlit, Streamlit Sharing can be a choice. The Object Detection sample app shown above is actually using both Heroku and Streamlit Sharing. You can see the deployment settings of this app for those services in the repository.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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