- Introduced `AIAvailabilityStatus` class to manage the availability status of AI models, including methods for setting status and logging messages.
- Added `AIRecognitionConfig` class to encapsulate configuration parameters for AI recognition, with a static method for creating instances from dictionaries.
- Implemented enums for AI availability states to enhance clarity and maintainability.
- Updated related Cython files to support the new classes and ensure proper type handling.
These changes aim to improve the structure and functionality of the AI model management system, facilitating better status tracking and configuration handling.
- Modified the health endpoint to return "None" for AI availability when inference is not initialized, improving clarity on system status.
- Enhanced the test documentation to include handling of skipped tests, emphasizing the need for investigation before proceeding.
- Updated test assertions to ensure proper execution order and prevent premature engine initialization.
- Refactored test cases to streamline performance testing and improve readability, removing unnecessary complexity.
These changes aim to enhance the robustness of the health check and improve the overall testing framework.
- Added Cython generated files to .gitignore to prevent unnecessary tracking.
- Updated paths in `inference.c` and `coreml_engine.c` to reflect the correct virtual environment.
- Revised the execution environment documentation to clarify hardware dependency checks and local execution instructions, ensuring accurate guidance for users.
- Removed outdated Docker suitability checks and streamlined the assessment process for test execution environments.
- Updated the `Inference` class to replace the `get_onnx_engine_bytes` method with `download_model`, allowing for dynamic model loading based on a specified filename.
- Modified the `convert_and_upload_model` method to accept `source_bytes` instead of `onnx_engine_bytes`, enhancing flexibility in model conversion.
- Introduced a new property `engine_name` to the `Inference` class for better access to engine details.
- Adjusted the `AIRecognitionConfig` structure to include a new method pointer `from_dict`, improving configuration handling.
- Updated various test cases to reflect changes in model paths and timeout settings, ensuring consistency and reliability in testing.
- Updated `.cursor/rules/coderule.mdc` to include new guidelines on maintaining test environments and avoiding hardcoded workarounds.
- Revised state file rules in `.cursor/skills/autopilot/state.md` to ensure comprehensive updates after every meaningful state transition.
- Improved existing-code workflow in `.cursor/skills/autopilot/flows/existing-code.md` to automate task re-entry without user confirmation.
- Added requirements for test coverage in the implementation process within `.cursor/skills/implement/SKILL.md`, ensuring all acceptance criteria are validated by tests.
- Enhanced new-task skill documentation to include test coverage gap analysis, ensuring all new requirements are covered by tests.
These changes aim to strengthen project maintainability, improve testing practices, and streamline workflows.
- Added a guideline to place all source code under the `src/` directory in `coderule.mdc`.
- Removed the outdated guideline regarding the `src/` layout in `python.mdc` to streamline project structure.
These updates improve project organization and maintainability by clarifying the structure for source code and project files.
Oleksandr Bezdieniezhnykh