An AI-powered analytics system for smart malls using computer vision and machine learning.

• Person detection using YOLOv8
• Face analysis (age, gender, emotion) using DeepFace
• Customer behavior prediction using RandomForest
• Real-time analytics dashboard
• MongoDB integration for data storage
• Automated slot allocation system

1. Clone the repository:

git clone https://github.com/yourusername/SmartMallAI.git
cd SmartMallAI

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

• Copy .env.example to .env
• Update the MongoDB connection string and other settings

5. Run the application:

python app.py

The application will be available at http://localhost:5000

SmartMallAI/
├── app.py                     # Main Flask app
├── yolo_detector.py          # YOLOv8 person detection
├── face_analysis.py          # DeepFace analysis
├── ml_model.py              # RandomForest model
├── db_manager.py            # MongoDB operations
├── utils.py                 # Utilities
├── models/                  # Trained models
├── data/                    # Training data
├── static/                  # Static files
│   ├── uploads/            # Image uploads
│   ├── css/               # Stylesheets
│   └── js/                # JavaScript
├── templates/              # HTML templates
└── logs/                  # Application logs

• GET / - Main dashboard
• POST /upload - Upload and analyze image
• GET /results/<result_id> - View analysis results
• GET /dashboard/stats - Get real-time analytics

• Flask - Web framework
• YOLOv8 - Object detection
• DeepFace - Face analysis
• scikit-learn - Machine learning
• MongoDB - Database
• Chart.js - Analytics visualization

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.