luma-diagnostics 0.1.0

A diagnostic tool for troubleshooting LUMA Dream Machine API issues

LUMA API Image Diagnostics

An automated diagnostic suite for troubleshooting image processing issues with the LUMA API. This tool provides a user-friendly wizard interface to run comprehensive tests and generate detailed reports.

Features

• 🧙‍♂️ Interactive CLI wizard
• 🔍 Comprehensive image tests
• 📊 Detailed test reports
• 📁 Case management
• 🔑 Secure API key handling
• 📝 Human-readable outputs
• 🤖 JSON outputs for automation

Installation

# Clone the repository
git clone https://github.com/caseyfenton/luma-diagnostics.git
cd luma-diagnostics

# Install dependencies
pip install -e .

Quick Start

# Run the diagnostic wizard
luma-diagnostics --wizard

# Or run specific tests
luma-diagnostics --image-url https://example.com/image.jpg --test-type basic

Configuration

1. Set your LUMA API key:

   # Option 1: Environment variable
   export LUMA_API_KEY=your_api_key
   
   # Option 2: Add to ~/.env file
   echo "LUMA_API_KEY=your_api_key" >> ~/.env
   
   # Option 3: Let the wizard guide you
   luma-diagnostics --wizard
   

2. Optional: Configure default test parameters in ~/.config/luma-diagnostics/settings.json

Available Tests

Basic Tests

• URL accessibility
• Certificate validation
• Redirect handling
• MIME type verification
• Image format validation

Advanced Tests (requires API key)

• Text-to-Image generation
• Image-to-Image generation
• Image-to-Video generation

Case Management

The tool includes a case management system to track issues and test results:

cases/
├── active/          # Active case files
│   └── customer-case-20250122/
│       ├── README.md
│       ├── test_20250122_105752.json
│       └── test_20250122_105752.txt
└── archived/        # Archived cases

Development

Setup Development Environment

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install development dependencies
pip install -e ".[dev]"

Running Tests

pytest

Contributing

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

License

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

Support

For issues and feature requests, please use the GitHub issue tracker.