145 lines
6.6 KiB
Python
145 lines
6.6 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from typing import Type
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from app.core.config import Settings, get_settings
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from app.schemas.detection import DetectionModelCapability
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from app.services.yolo_adapter import YoloDetectionAdapter
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class ModelRegistryService:
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@staticmethod
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def list_model_capabilities(
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settings: Settings | None = None,
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yolo_adapter_class: Type[YoloDetectionAdapter] = YoloDetectionAdapter,
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task_type: str = "object_detection",
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) -> list[DetectionModelCapability]:
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resolved_settings = settings or get_settings()
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if task_type == "segmentation":
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return ModelRegistryService.list_segmentation_model_capabilities()
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if task_type != "object_detection":
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return []
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return [
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DetectionModelCapability(
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model_id="yolo-placeholder",
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display_name="YOLO detector placeholder",
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framework="ultralytics/pytorch",
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task_type="object_detection",
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supported_classes=["building", "road", "water", "landuse"],
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configured=False,
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status="not_configured",
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limitation_message="YOLO/PyTorch inference is not configured in Sprint 8; no model is downloaded or executed.",
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version=None,
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),
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ModelRegistryService._configured_yolo_capability(resolved_settings, yolo_adapter_class),
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DetectionModelCapability(
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model_id="manual-fixture-detector",
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display_name="Manual fixture detector",
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framework="fixture",
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task_type="object_detection",
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supported_classes=["building"],
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configured=True,
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status="configured",
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limitation_message="Fixture detector is for explicit tests/demo fixtures only and is not production inference.",
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version="fixture-v1",
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),
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]
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@staticmethod
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def get_model_capability(
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model_id: str,
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settings: Settings | None = None,
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yolo_adapter_class: Type[YoloDetectionAdapter] = YoloDetectionAdapter,
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task_type: str = "object_detection",
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) -> DetectionModelCapability | None:
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normalized = model_id.strip()
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for model in ModelRegistryService.list_model_capabilities(settings=settings, yolo_adapter_class=yolo_adapter_class, task_type=task_type):
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if model.model_id == normalized:
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return model
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return None
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@staticmethod
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def list_segmentation_model_capabilities() -> list[DetectionModelCapability]:
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return [
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DetectionModelCapability(
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model_id="segmentation-placeholder",
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display_name="Segmentation placeholder",
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framework="placeholder",
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task_type="segmentation",
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supported_classes=["building", "vegetation", "water", "landuse"],
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configured=False,
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status="not_configured",
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limitation_message="Segmentation inference is not configured in Sprint 9; no SAM/YOLO-seg model is downloaded or executed.",
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version=None,
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),
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DetectionModelCapability(
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model_id="fixture-segmenter",
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display_name="Fixture segmenter",
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framework="fixture",
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task_type="segmentation",
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supported_classes=["building", "vegetation", "water", "landuse"],
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configured=True,
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status="configured",
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limitation_message="Fixture segmenter is for explicit tests/demo fixtures only and is not production inference.",
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version="fixture-v1",
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),
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DetectionModelCapability(
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model_id="yolo-seg-configured",
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display_name="Configured YOLO segmentation",
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framework="ultralytics/pytorch",
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task_type="segmentation",
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supported_classes=["building", "vegetation", "water", "landuse"],
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configured=False,
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status="not_configured",
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limitation_message="YOLO-seg is not configured in Sprint 9. GeoIntel will not download segmentation model weights automatically.",
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version=None,
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),
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DetectionModelCapability(
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model_id="sam-configured",
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display_name="Configured SAM segmentation",
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framework="sam",
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task_type="segmentation",
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supported_classes=["building", "vegetation", "water", "landuse"],
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configured=False,
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status="not_configured",
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limitation_message="SAM is not configured in Sprint 9 and is not installed as a backend dependency.",
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version=None,
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),
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]
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@staticmethod
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def _configured_yolo_capability(
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settings: Settings,
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yolo_adapter_class: Type[YoloDetectionAdapter],
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) -> DetectionModelCapability:
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configured = False
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status = "not_configured"
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limitation = "YOLO is disabled. Set YOLO_ENABLED=true and YOLO_MODEL_PATH to a local model file to enable inference."
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model_path = Path(settings.yolo_model_path).expanduser() if settings.yolo_model_path else None
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if settings.yolo_enabled:
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if not yolo_adapter_class.dependencies_available():
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status = "dependency_unavailable"
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limitation = "YOLO dependencies are not installed. Install backend optional extras with geointel-backend[ai]."
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elif model_path is None:
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limitation = "YOLO_MODEL_PATH is not set. GeoIntel will not download model weights automatically."
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elif not model_path.exists() or not model_path.is_file():
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limitation = "YOLO_MODEL_PATH does not point to an existing local model file. GeoIntel will not download model weights automatically."
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else:
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configured = True
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status = "configured"
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limitation = "Configured for local YOLO inference over an existing raster tile manifest."
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return DetectionModelCapability(
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model_id=settings.yolo_model_id,
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display_name=settings.yolo_model_display_name,
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framework="ultralytics/pytorch",
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task_type="object_detection",
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supported_classes=["building", "road", "water", "landuse"],
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configured=configured,
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status=status,
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limitation_message=limitation,
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version=settings.yolo_model_version,
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)
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