netcore-technologies/wifi-analyzer-pro

WiFi Analyzer Pro is a dashboard for checking the health of your WiFi network in real time, combining a Python backend that talks to your wireless hardware with a React powered web interface you view in a browser. It is aimed at anyone who wants a detailed, visual picture of what their WiFi network is actually doing, from home users troubleshooting a weak signal to more technical people who want deep diagnostic data. The dashboard scans nearby networks and shows details like signal strength, signal to noise ratio, security type, and which WiFi generation each network supports. It can detect hidden networks, warn about a rogue access point pretending to be your own network, and recommend which channel to use based on how congested the airwaves are. Beyond scanning, it tracks signal quality and connection stability over time with live graphs, runs internet speed tests measuring download, upload, ping, and jitter, and monitors your computer's own system resources like CPU, memory, and temperature. It also lists connected devices on your network and can send alerts through channels like email, Discord, or Slack when something looks wrong, plus it includes a feature that generates a plain language written summary of your network's overall health. The project supports Linux, Raspberry Pi, Windows 11, and macOS, with beta mobile apps for iOS and Android, and it also offers a demo mode that runs without any real WiFi hardware, using realistic fake data so anyone can try the interface first. Getting started differs slightly by platform. On a Raspberry Pi, a single setup script installs everything. On Linux or macOS, you install the frontend's JavaScript dependencies, build it, then separately install the Python backend's requirements and run its server. On Windows, an installer batch file handles setup. Once running, the dashboard is available through a web browser on a specific local port, and the backend also exposes a documented set of API endpoints for scanning, diagnostics, and speed testing. The frontend uses React, Vite, and Tailwind CSS, while the backend uses Python along with several networking libraries. The project is licensed under MIT and currently has 1 star on GitHub.