feat: Set up complete Python testing infrastructure with Poetry

[llbbl]

Set Up Python Testing Infrastructure

Summary

This PR establishes a complete testing infrastructure for the AI image generation project, migrating from a basic requirements.txt setup to a modern Poetry-based package management system with comprehensive pytest configuration.

Changes Made

Package Management

• Migrated to Poetry: Converted all 211 dependencies from requirements.txt to pyproject.toml
• Organized dependencies: Separated development dependencies into a dedicated group
• Fixed version conflicts: Adjusted numpy version constraint to resolve dependency conflicts

Testing Framework Setup

• Added testing dependencies:
  • pytest - Core testing framework
  • pytest-cov - Coverage reporting
  • pytest-mock - Mocking utilities
• Configured pytest in pyproject.toml with:
  • Test discovery patterns
  • Coverage settings (80% threshold)
  • Multiple coverage report formats (terminal, HTML, XML)
  • Custom markers for test organization
  • Strict configuration options

Directory Structure

tests/
├── __init__.py
├── conftest.py          # Shared fixtures
├── test_setup_validation.py  # Infrastructure validation
├── unit/
│   └── __init__.py
└── integration/
    └── __init__.py

Test Configuration

• Coverage configuration:
  • Source directories included
  • Exclusion patterns for non-application code
  • 80% coverage threshold with branch coverage
  • HTML reports in htmlcov/, XML in coverage.xml
• Test markers:
  • @pytest.mark.unit - Unit tests
  • @pytest.mark.integration - Integration tests
  • @pytest.mark.slow - Slow-running tests

Shared Fixtures (conftest.py)

Created comprehensive fixtures for common testing needs:

• temp_dir - Temporary directory management
• temp_file - Temporary file creation
• mock_config - Mock configuration dictionary
• mock_env_vars - Environment variable mocking
• sample_image_path - Sample image file creation
• sample_json_data - Sample JSON structures
• reset_environment - Auto-cleanup of test environment
• mock_model_weights - Mock ML model files
• capture_logs - Log capture and assertion

Poetry Commands

Configured Poetry scripts for easy test execution:

• poetry run test - Run all tests
• poetry run tests - Alternative command (both work)
• Standard pytest options remain available

Additional Files

• .gitignore: Updated with comprehensive Python testing entries
• CLAUDE.md: Project memory file with commands and configuration details
• Validation tests: Created test_setup_validation.py to verify the infrastructure

How to Use

1. Install Poetry (if not already installed):

   curl -sSL https://install.python-poetry.org | python3 -
   

2. Install dependencies:

   poetry install  # Install all dependencies
   # or
   poetry install --only dev  # Install only dev dependencies
   

3. Run tests:

   poetry run test           # Run all tests
   poetry run pytest -v      # Run with verbose output
   poetry run pytest -m unit # Run only unit tests
   

4. View coverage reports:

   • Terminal: Automatically shown after test run
   • HTML: Open htmlcov/index.html in a browser
   • XML: Available at coverage.xml for CI integration

Notes

• The numpy dependency was adjusted to >=1.24.3,<2.0 to resolve version conflicts with other packages
• Poetry lock file is intentionally not gitignored to ensure reproducible builds
• The infrastructure is ready for immediate test development - no actual application tests were written as per requirements
• All validation tests pass, confirming the infrastructure is properly configured

Next Steps

Developers can now:

1. Write unit tests in tests/unit/
2. Write integration tests in tests/integration/
3. Use the provided fixtures from conftest.py
4. Run tests with coverage reporting
5. Use markers to organize and selectively run tests