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geointel/backend/app/services/model_asset_catalog_service.py
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Jens faeb58ef6d
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Initial public release
2026-08-31 21:56:53 +02:00

117 lines
5.1 KiB
Python

from __future__ import annotations
import hashlib
import re
from pathlib import Path
from app.core.config import Settings, get_settings
from app.core.errors import AppError
from app.schemas.detection import ModelAssetListResponse, ModelAssetRead
class ModelAssetCatalogService:
SUPPORTED_SUFFIXES = {
".pt": "ultralytics/pytorch",
".onnx": "onnx",
".engine": "tensorrt",
}
@staticmethod
def list_assets(settings: Settings | None = None) -> ModelAssetListResponse:
resolved_settings = settings or get_settings()
model_directory = ModelAssetCatalogService._model_directory(resolved_settings)
active_model_path = ModelAssetCatalogService._resolved_file_path(resolved_settings.yolo_model_path)
if not model_directory.exists() or not model_directory.is_dir():
return ModelAssetListResponse(items=[], total=0, model_directory=str(model_directory))
candidate_paths = [
path
for path in sorted(model_directory.iterdir(), key=lambda item: item.name.lower())
if path.is_file() and path.suffix.lower() in ModelAssetCatalogService.SUPPORTED_SUFFIXES
]
if active_model_path is not None:
candidate_paths = [path for path in candidate_paths if path.resolve() == active_model_path]
items = [
ModelAssetCatalogService._asset_from_file(path, active_model_path=active_model_path)
for path in candidate_paths
]
return ModelAssetListResponse(items=items, total=len(items), model_directory=str(model_directory))
@staticmethod
def resolve_asset(model_asset_id: str, settings: Settings | None = None) -> ModelAssetRead:
normalized = model_asset_id.strip()
for asset in ModelAssetCatalogService.list_assets(settings=settings).items:
if asset.model_asset_id == normalized:
return asset
raise AppError(
code="DETECTION_MODEL_ASSET_NOT_FOUND",
message="Selected local model asset was not found in the configured model directory",
details={"model_asset_id": normalized},
status_code=404,
)
@staticmethod
def settings_for_asset(settings: Settings, asset: ModelAssetRead) -> Settings:
return settings.model_copy(update={"yolo_model_path": asset.model_path})
@staticmethod
def _model_directory(settings: Settings) -> Path:
configured_directory = Path(settings.yolo_models_dir).expanduser()
if configured_directory.exists() and configured_directory.is_dir():
return configured_directory.resolve()
active_model_path = ModelAssetCatalogService._resolved_file_path(settings.yolo_model_path)
if active_model_path and active_model_path.parent.exists() and active_model_path.parent.is_dir():
return active_model_path.parent.resolve()
return configured_directory.resolve()
@staticmethod
def _asset_from_file(path: Path, *, active_model_path: Path | None) -> ModelAssetRead:
resolved_path = path.resolve()
return ModelAssetRead(
model_asset_id=ModelAssetCatalogService._asset_id(path),
filename=path.name,
display_name=path.stem,
model_path=str(resolved_path),
suffix=path.suffix.lower(),
framework=ModelAssetCatalogService.SUPPORTED_SUFFIXES[path.suffix.lower()],
task_type="object_detection",
size_bytes=path.stat().st_size,
sha256=ModelAssetCatalogService._sha256(path),
active=active_model_path == resolved_path,
runtime_available=True,
runtime_status="active" if active_model_path == resolved_path else "available",
governed_validation_status="not_verified_by_catalog",
promotion_status="not_verified_by_catalog",
status="runtime_active" if active_model_path == resolved_path else "runtime_available",
limitation_message=(
"Active local runtime model asset. Runtime selection is not evidence of governed validation or promotion."
if active_model_path == resolved_path
else "Local runtime model asset. Governed validation and promotion are not established by this catalog."
),
will_download_models=False,
)
@staticmethod
def _asset_id(path: Path) -> str:
raw = f"{path.stem}-{path.suffix.lower().lstrip('.')}"
normalized = re.sub(r"[^a-z0-9]+", "-", raw.lower()).strip("-")
return normalized or "model-asset"
@staticmethod
def _sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
@staticmethod
def _resolved_file_path(raw_path: str | None) -> Path | None:
if not raw_path:
return None
path = Path(raw_path).expanduser()
if not path.exists() or not path.is_file():
return None
return path.resolve()