<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Streamlit on Jaehyeon Kim</title><link>https://jaehyeon.me/tags/streamlit/</link><description>Recent content in Streamlit on Jaehyeon Kim</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Copyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.</copyright><lastBuildDate>Tue, 25 Feb 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://jaehyeon.me/tags/streamlit/index.xml" rel="self" type="application/rss+xml"/><item><title>Realtime Dashboard with FastAPI, Streamlit and Next.js - Part 2 Streamlit Dashboard</title><link>https://jaehyeon.me/blog/2025-02-25-realtime-dashboard-2/</link><pubDate>Tue, 25 Feb 2025 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2025-02-25-realtime-dashboard-2/</guid><description><![CDATA[<p>In this post, we develop a real-time monitoring dashboard using <a href="https://streamlit.io/" target="_blank" rel="noopener noreferrer">Streamlit<i class="fas fa-external-link-square-alt ms-1"></i></a>, an open-source Python framework that allows data scientists and AI/ML engineers to create interactive data apps. The app connects to the WebSocket server we developed in <a href="/blog/2025-02-18-realtime-dashboard-1">Part 1</a> and continuously fetches data to visualize key metrics such as <strong>order counts</strong>, <strong>sales data</strong>, and <strong>revenue by traffic source and country</strong>. With interactive bar charts and dynamic metrics, users can monitor sales trends and other important business KPIs in real-time.</p>]]></description><enclosure url="https://jaehyeon.me/blog/2025-02-25-realtime-dashboard-2/featured.gif" length="417216" type="image/gif"/></item></channel></rss>