Jeeva-987

Jeeva-987 / flaw-detector

Public

AI-powered monitoring platform that detects user presence, identifies mobile phone distractions, and analyzes emotions in real-time using YOLOv8 and HSEmotion models.

43
0
69% credibility
Found Feb 10, 2026 at 20 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A webcam-based web application that detects human presence, mobile phone usage, and facial emotions to track engagement and distractions during coding or learning activities.

How It Works

1
🔍 Find the Attention Monitor

You come across this handy tool online that helps track if someone is focused during study or coding sessions by watching their webcam feed.

2
💻 Get It Set Up

Download the files to your computer and follow the simple guide to prepare everything, making sure your webcam works.

3
🚀 Turn On the Monitor

Start the tool with an easy launch so it can begin watching for people, phones, and feelings.

4
🌐 Open the Web Viewer

Go to the web page in your browser where you see the live view from your camera, feeling excited to start tracking.

5
📹 Point Your Webcam

Allow the tool to use your camera and point it at the person or area you want to monitor during a session.

6
😊 See Real-Time Insights

Watch as it instantly shows if someone is there, using a phone, or what emotions like happy or focused they show, with scores for engagement.

7
📊 Review the Results

Get detailed reports on attention levels, distractions, and feelings to understand how the session went.

✅ Better Sessions Ahead

Now you can improve focus and engagement in learning or work by knowing exactly what's happening.

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Star Growth

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AI-Generated Review

What is flaw-detector?

Flaw-detector is a Python-based AI-powered monitoring system that watches webcam feeds or uploaded images to detect user presence, spot mobile phone distractions, and gauge emotions in real-time. Built with FastAPI for the backend API and React for the frontend, it delivers engagement scores, valence labels, and debug visuals via a simple /predict endpoint or live webcam demo. Developers get an ready-to-run tool for tracking focus in coding sessions, exams, or remote work, solving the pain of manual proctoring or distraction logging.

Why is it gaining traction?

It stands out with multi-person support, phone-person interaction checks, and derived metrics like engagement percentages that go beyond basic detection—perfect for ai powered employee monitoring or online learning analytics. The quick setup (pip install, uvicorn run, npm dev) and interactive API docs hook devs prototyping ai powered monitoring tools, unlike heavier alternatives needing custom model training. Low-latency inference on CPU makes it practical for edge demos without GPU hassle.

Who should use this?

Edtech builders adding proctoring to LMS platforms, HR teams implementing ai powered employee monitoring for remote focus tracking, or researchers analyzing emotions in user studies. It's ideal for indie devs spiking ai powered monitoring systems for virtual classrooms or productivity dashboards, not production-scale surveillance.

Verdict

Grab it for proofs-of-concept in ai powered monitoring—solid docs and webcam-ready frontend make it dev-friendly despite 19 stars and a 0.7% credibility score signaling early maturity. Polish tests and scale models before deploying; great flaw detector starter for niche GitHub projects.

(198 words)

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