Da Vinci Teacher Allocation System

A sophisticated automation solution for managing teacher allocations in the Da Vinci program. This system streamlines the process of matching students with teachers, handling multi-subject allocations, and managing communications.

Features

1. Intelligent Teacher Matching

• Sophisticated matching algorithm considering:
  • Subject expertise and qualifications
  • Current workload and availability
  • Student preferences and schedule compatibility
  • Historical performance metrics
• Real-time workload monitoring
• Automated teacher invitations

2. Web Interface

• Real-time dashboard for tracking allocations
• Detailed views of student-teacher matches
• Easy-to-use forms for creating new allocations
• Status tracking and progress monitoring
• Clean, intuitive design

3. Crimson API Integration

• Seamless integration with Crimson platform
• Automated teacher invitations
• Real-time workload monitoring
• Student subject management
• Status updates and confirmations

4. Data Processing

• Efficient handling of multi-subject allocations
• Data validation and transformation
• Status tracking and updates
• Performance analytics
• Automated data splitting for multiple subjects

5. Communication System

• Automated email notifications
• Personalized communications
• Multi-party notification management
• Templated messages for consistency
• Delivery and engagement tracking

Setup

Prerequisites

• Python 3.8 or newer
• pip (Python package manager)
• Git (for cloning the repository)

Installation

1. Clone the repository:

git clone https://github.com/none34829/cga-assignment
cd davinci-allocation

2. Create a .env file in the root directory with the following settings:

CRIMSON_API_KEY=your_api_key_here
CRIMSON_API_URL=https://api.crimsoneducation.org/v1
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
SMTP_USERNAME=your_email@gmail.com
SMTP_PASSWORD=your_app_password
SENDER_EMAIL=your_email@gmail.com

3. Run the application:

On Windows:

run.bat

On Linux/Mac:

./run.sh

The setup script will:

• Check for Python and pip installation
• Install required packages
• Create necessary data folders
• Start the application

Project Structure

davinci_allocation/
├── app/
│   ├── __init__.py
│   ├── app.py              # Main Flask application
│   ├── models.py           # Data models
│   ├── data_processor.py   # Data processing logic
│   ├── teacher_matcher.py  # Teacher matching algorithm
│   ├── crimson_api.py      # Crimson API integration
│   └── email_service.py    # Email notification system
├── templates/
│   ├── dashboard.html      # Main dashboard
│   ├── allocation_details.html
│   ├── new_allocation.html
│   └── email_templates.py
├── data/                   # Application data
├── test_data/             # Test data
├── run.bat                # Windows startup script
├── run.sh                 # Unix startup script
└── requirements.txt       # Python dependencies

Development

Running Tests

python -m pytest tests/

Mock Data

The system includes mock data for development and testing. To use mock data:

1. Set USE_MOCK=true in your .env file
2. The system will use mock data instead of making real API calls

Contributing

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