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Add AIAvailabilityStatus and AIRecognitionConfig classes for AI model management

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- 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.
This commit is contained in:
Oleksandr Bezdieniezhnykh
2026-03-31 05:49:51 +03:00
parent fc57d677b4
commit 8ce40a9385
43 changed files with 1190 additions and 462 deletions
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_docs/02_document/architecture.md
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@@ -25,7 +25,6 @@
| ML Runtime (CPU) | ONNX Runtime | 1.22.0 | Portable model format, CPU/CUDA provider fallback |
| ML Runtime (GPU) | TensorRT + PyCUDA | 10.11.0 / 2025.1.1 | Maximum GPU inference performance |
| Image Processing | OpenCV | 4.10.0 | Frame decoding, preprocessing, tiling |
| Serialization | msgpack | 1.1.1 | Compact binary serialization for annotations and configs |
| HTTP Client | requests | 2.32.4 | Synchronous HTTP to Loader and Annotations services |
| Logging | loguru | 0.7.3 | Structured file + console logging |
| GPU Monitoring | pynvml | 12.0.0 | GPU detection, capability checks, memory queries |