Overview
Attendance workflows
made more reliable.
EduGuard AI is a Flask-based attendance system for teachers. It supports teacher accounts, class and student management, attendance sessions, face-data registration, camera-based detection, and manual corrections.
The project is practical and admin-focused: protected resources are scoped to the owning teacher, absence notifications fail safely when email settings are missing, and attendance records can be exported as PDF or Excel.
Auth
Teacher Accounts
CV
Face Attendance
PDF
Report Export
Tests
Permission Coverage
Problem → Approach
→ Results
01 — Problem
The Challenge
Teachers need a faster way to manage attendance without losing control over corrections, exports, or class ownership. A plain face-recognition demo would not be enough without reliable login boundaries and data-management workflows.
02 — Approach
How It Was Built
The app is structured around Flask routes, SQLAlchemy models, Flask-Login sessions, SQLite storage, Bootstrap templates, and a separate OpenCV/face-recognition workflow that launches detection and writes attendance results back into the app.
03 — Results
What It Achieved
EduGuard AI now covers the full classroom loop: create classes, add students, register faces, start attendance, edit records, notify absentees, and export reports. Tests cover auth, notifications, and permission boundaries.
Screenshots &
Visuals
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Tech
Stack
Python
Language
Flask
Backend
Flask-Login
Auth
SQLAlchemy
ORM
SQLite
Database
OpenCV
Vision
face-recognition
Face Stack
Bootstrap 5
Frontend