Food Mess Management & Attendance System

A production-ready, Streamlit-based food mess management system powered by AI face recognition.

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Quick Start (Local Setup)

1. Prerequisites (macOS)

# Install cmake (required by face_recognition/dlib)
brew install cmake

2. Clone / navigate to the project

cd /Users/harshit/Desktop/FaceRecAtd

3. Create & activate virtual environment

python3 -m venv venv
source venv/bin/activate

4. Install dependencies

pip install --upgrade pip
pip install -r requirements.txt

5. Run the app

streamlit run app.py

Open http://localhost:8501 (or whichever port Streamlit specifies) in your browser.

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Project Structure

FaceRecAtd/
├── app.py                          # Unified Dashboard (Scanner, Registration, Records)
├── core/
│   ├── database.py                 # SQLite Database Management
│   └── face_engine.py              # Face detection and encoding logic
├── utils/
│   └── helpers.py                  # Utility functions
├── data/
│   └── attendance.db               # SQLite DB (auto-generated)
└── requirements.txt                # Python dependencies

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Features

• Food Program Management: Easily track which students/staff are enrolled in the Food Mess program.
• Meal Phases: System automatically tracks Breakfast, Lunch, and Dinner based on the time of day.
• Smart Face Scanning: Input methods include Webcam Snapshot, File Upload, and an optimized Live CCTV Scanner that scans continuously and logs attendees instantly in a timeline.
• Records & Reporting: View daily logs, filter by user/department, and export everything directly to a CSV file.
• Database Control: Built-in SQLite database with one-click full data wipes for clearing history between sessions.
• Clean Interface: Beautiful, dark-mode Streamlit dashboard with no clutter.