what is autoadsui fr?

This project, autoadsai, is a demonstration app built with Streamlit that shows how to add privacy-friendly web analytics to an interactive data app. The demo specifically visualizes Uber pickup data across New York City, but the real point is showing developers how to track user interactions on their own Streamlit apps using a tool called Umami. Streamlit is a Python framework that lets developers turn data scripts into interactive web applications without needing to build a full website from scratch. Umami is a lightweight, open-source alternative to Google Analytics that focuses on simplicity and privacy. This repo ties the two together, so when someone opens the Streamlit app and clicks around, those page views and interactions get recorded by Umami and displayed on an analytics dashboard. The Uber NYC map is essentially sample content that gives users something to interact with so the analytics can do their thing. The target audience is developers or data scientists who already use Streamlit for prototypes or internal tools and want to understand how people are using those apps. If you have built a dashboard for your team and you want to see which features get clicked most or how many people open it each week, this demo shows you how to wire that up. The linked blog post walks through the integration in more detail. The README itself is quite sparse, pointing to a Medium article for the full explanation. The Uber visualization is not unique to this project, as Streamlit includes a similar example in its official documentation. What makes this repo worth a look is the specific combination of Streamlit and Umami, giving teams a privacy-conscious analytics option without heavy setup or vendor lock-in.