Mold Cost Calculator

A Flask-based web application for calculating mold costs and managing quotations.

Features

• Cost Calculation

  • Mold type-based calculations
  • Complexity factors (sidewall, slider, cavity)
  • Texture options (standard, custom, laser)
  • Tax rate support for different countries

• User Management

  • Secure authentication system
  • Role-based access control
  • Admin dashboard for user management
  • Email-based registration with allowlist

• Quotation System

  • Create and manage quotations
  • Export functionality
  • Historical record keeping
  • Admin review and management

Tech Stack

• Backend: Flask, SQLAlchemy
• Frontend: HTML, CSS, JavaScript
• Database: PostgreSQL (production/development), SQLite in-memory (testing)
• Server: Gunicorn
• Proxy: Nginx
• Authentication: Flask-Login
• Forms: Flask-WTF
• Database Migrations: Flask-Migrate
• Containerization: Podman/Docker

Project Structure

mold_cost_online_tool/
├── app/                    # Application package
│   ├── forms/             # Form definitions
│   ├── models/            # Database models
│   ├── static/            # Static files (CSS, JS)
│   └── templates/         # HTML templates
├── instance/              # Instance-specific files
├── logs/                  # Application logs
├── migrations/            # Database migrations
├── tests/                 # Test files
├── deploy/                # Deployment configuration
├── db_data/               # Database data (development)
├── app.py                 # Main application file
├── config.py              # Configuration
├── run.py                 # Application entry point
├── requirements.txt       # Python dependencies
├── gunicorn_config.py     # Gunicorn configuration
├── podman-compose.yml     # Production container setup
└── podman-compose.development.yml  # Development container setup

Setup and Installation

Option 1: Using Containers (Recommended)

1. Clone the repository

   git clone <repository-url>
   cd mold_cost_online_tool
   

2. Set up environment variables Create .env.development file for development:

   POSTGRES_USER=your_db_user
   POSTGRES_PASSWORD=your_db_password
   POSTGRES_DB=mold_cost
   DATABASE_HOST=db_development
   SECRET_KEY=your-secret-key-here
   MAIL_SERVER=smtp.example.com
   MAIL_USERNAME=your-email@example.com
   MAIL_PASSWORD=your-mail-password
   MAIL_DEFAULT_SENDER=your-email@example.com
   ADMIN_EMAIL=admin@example.com
   ADMIN_PASSWORD=your-admin-password
   

3. Start the development environment

   podman-compose -f podman-compose.development.yml up --build
   

4. Initialize the database

   flask db upgrade
   

Option 2: Local Development

1. Clone the repository

   git clone <repository-url>
   cd mold_cost_online_tool
   

2. Create and activate 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 PostgreSQL database

   • Install PostgreSQL
   • Create database and user
   • Set environment variables for database connection

5. Set up environment variables

   export FLASK_APP=run.py
   export FLASK_ENV=development
   export SECRET_KEY=your-secret-key-here
   export DATABASE_URL=postgresql://your_user:your_password@localhost/your_dbname
   

6. Initialize the database

   flask db upgrade
   

Development

1. Run development server

   python run.py
   

2. Run tests

   python -m pytest tests/
   

Deployment

1. Production server setup

   • Configure Nginx as reverse proxy
   • Set up Gunicorn with provided configuration
   • Configure SSL certificates
   • Set up PostgreSQL database

2. Environment variables for production

   FLASK_APP=run.py
   FLASK_ENV=production
   SECRET_KEY=your-secure-secret-key-here
   DATABASE_URL=postgresql://your_user:your_password@your_host/your_dbname
   

3. Start the application

   gunicorn -c gunicorn_config.py run:app
   

Database Management

• Backups: Automated daily backups using pg_dump
• Migrations: Use Flask-Migrate for database schema changes
• Development: Uses PostgreSQL container with persistent data
• Testing: Uses SQLite in-memory database for fast, isolated tests

Security Features

• CSRF protection
• Secure password hashing
• Session management
• Input validation
• Security headers
• Rate limiting
• SQL injection prevention

Maintenance

• Regular database backups
• Log rotation
• Security updates
• Performance monitoring

Contributing

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

License

PCT & TOOLING app, all rights reserved.

Support

For support and inquiries, please contact: jimmy.gan@adidas.com