🚀 Advanced Data Generator

📋 Table of Contents

• Overview
• Features
• Installation
• Usage
• API Documentation
• Web Interface
• Data Models
• Development
• Contributing
• License

🌟 Overview

Advanced Data Generator is a powerful, production-ready tool for generating realistic test data. It's designed to help developers, testers, and data scientists create high-quality synthetic data for various applications.

Key Benefits

• 🎯 Generate realistic, consistent test data
• 🌍 Support for multiple languages and locales
• 📊 Rich data visualization capabilities
• 🔄 Multiple export formats
• 🚀 High performance and scalability
• 🔒 Data validation and integrity checks

✨ Features

Core Features

• Multi-Model Data Generation

  • User profiles with realistic attributes
  • Product catalogs with detailed information
  • Order management with relationships
  • Custom data generation rules

• Internationalization

  • English (en_US) support
  • Persian (fa_IR) support
  • Extensible locale system

• Data Export

  • JSON format
  • CSV format
  • YAML format
  • Custom export configurations

• Data Validation

  • Email format validation
  • Phone number validation
  • Age verification
  • Data integrity checks

Advanced Features

• REST API

  • Swagger documentation
  • Rate limiting
  • Authentication support
  • Error handling

• Web Interface

  • Interactive dashboard
  • Real-time data visualization
  • Data filtering and sorting
  • Export management

• Containerization

  • Docker support
  • Docker Compose orchestration
  • Environment isolation
  • Easy deployment

🛠 Installation

Prerequisites

• Python 3.9 or higher
• pip (Python package manager)
• Git

Quick Start

# Clone the repository
git clone https://github.com/yourusername/data-generator.git
cd data-generator

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Docker Installation

# Build and run with Docker Compose
docker-compose up --build

📖 Usage

Command Line Interface

# Generate data with default settings
python main.py

# Generate specific number of records
python main.py --users 100 --products 50 --orders 200

# Generate data with Persian locale
python main.py --locale fa_IR

# Export data to different formats
python main.py --export json

API Usage

# Start the API server
uvicorn api:app --reload

# Access the API documentation
# Open http://localhost:8000/docs in your browser

Web Interface

# Start the web interface
streamlit run web_interface.py

# Access the web interface
# Open http://localhost:8501 in your browser

📚 API Documentation

The API documentation is available at http://localhost:8000/docs when the server is running.

Available Endpoints

• POST /generate - Generate new data
• POST /export - Export data
• GET /users - Retrieve users
• GET /products - Retrieve products
• GET /orders - Retrieve orders

🖥 Web Interface

The web interface provides:

• Interactive data generation
• Real-time data visualization
• Data filtering and sorting
• Export management
• Statistical analysis

📊 Data Models

User Model

class User:
    id: int
    name: str
    email: str
    address: str
    phone: str
    birth_date: datetime
    is_active: bool
    created_at: datetime

Product Model

class Product:
    id: int
    name: str
    description: str
    price: float
    category: str
    stock_quantity: int
    created_at: datetime

Order Model

class Order:
    id: int
    user_id: int
    product_id: int
    quantity: int
    total_price: float
    status: str
    created_at: datetime

🛠 Development

Setting Up Development Environment

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest

# Format code
black .

# Lint code
flake8

Testing

# Run all tests
pytest

# Run specific test file
pytest tests/test_data_generator.py

# Run with coverage
pytest --cov=.

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Write docstrings
• Add tests for new features

📄 License

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

🙏 Acknowledgments

• Faker for realistic data generation
• SQLAlchemy for database operations
• FastAPI for the REST API
• Streamlit for the web interface

📞 Support

For support, please:

1. Check the documentation
2. Open an issue
3. Contact the maintainers

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Made with ❤️ by ilya nozary

© 2024 Advanced Data Generator