feat: Set up comprehensive Python testing infrastructure

[llbbl]

Set Up Complete Python Testing Infrastructure

Summary

This PR establishes a comprehensive testing infrastructure for the IndicCrop project, providing a robust foundation for test-driven development and continuous integration.

Changes Made

Package Management & Dependencies

• Created pyproject.toml with Poetry configuration
• Migrated all dependencies from requirements.txt to Poetry format with flexible version constraints
• Added testing dependencies: pytest, pytest-cov, pytest-mock as development dependencies
• Configured Poetry scripts: Both poetry run test and poetry run tests commands available

Testing Configuration

• Comprehensive pytest configuration in pyproject.toml:
  • Test discovery patterns for test_*.py and *_test.py
  • Coverage reporting with 80% threshold
  • HTML and XML coverage report generation
  • Custom markers: unit, integration, slow
  • Strict configuration options for better test reliability

Coverage Configuration

• Source tracking for the entire project
• Smart exclusions for test files, virtual environments, build artifacts
• Multiple report formats: terminal, HTML (htmlcov/), XML (coverage.xml)
• Configured thresholds and missing line reporting

Directory Structure

• Created testing directories:

  tests/
  ├── __init__.py
  ├── conftest.py
  ├── unit/
  │   └── __init__.py
  ├── integration/
  │   └── __init__.py
  └── test_setup_validation.py
  

Test Fixtures & Utilities

• Comprehensive conftest.py with shared fixtures:
  • temp_dir: Temporary directory management
  • sample_config: Mock configuration data
  • mock_transformers_model: Mocked transformer models
  • mock_tokenizer: Mocked tokenizer functionality
  • sample_text_data: Multilingual sample data
  • sample_dataset_dict: Dataset structure fixtures
  • reset_random_seeds: Reproducible test runs
  • And more utilities for testing ML/NLP workflows

Validation & Quality Assurance

• Created validation test suite (test_setup_validation.py):
  • Infrastructure verification tests
  • Python version compatibility checks
  • Project structure validation
  • Fixture functionality verification
  • Custom marker testing
  • Mock utility validation

Additional Improvements

• Updated .gitignore with Claude Code settings (.claude/*)
• Preserved existing entries while adding testing infrastructure exclusions

How to Run Tests

Basic Testing

# Run all tests
poetry run test
# or
poetry run tests

# Run with verbose output
poetry run pytest -v

# Run without coverage (for faster development)
poetry run pytest --no-cov

Marker-Based Testing

# Run only unit tests
poetry run pytest -m "unit"

# Run only integration tests  
poetry run pytest -m "integration"

# Skip slow tests
poetry run pytest -m "not slow"

Coverage Reporting

# Generate HTML coverage report
poetry run pytest --cov-report=html

# View coverage in terminal
poetry run pytest --cov-report=term-missing

Validation Results

All validation tests pass successfully:

• ✅ 12/12 tests passing
• ✅ Custom markers (unit, integration, slow) working correctly
• ✅ Shared fixtures available and functional
• ✅ Project structure properly configured
• ✅ Mock utilities ready for ML/NLP testing

Next Steps

The testing infrastructure is now ready for development. Developers can:

1. Start writing unit tests in tests/unit/
2. Add integration tests in tests/integration/
3. Use existing fixtures from conftest.py for common testing patterns
4. Leverage markers to organize and filter test execution
5. Monitor coverage to ensure comprehensive testing

Notes

• Poetry configuration uses flexible version constraints (^x.y.z) for better dependency resolution
• Coverage configuration excludes test files themselves to focus on source code coverage
• All standard pytest plugins and markers are available for advanced testing scenarios
• The infrastructure supports both synchronous and asynchronous testing patterns