ML-005 vorbereiten: Registry, API-Routen und kompatibler Predictor

app/ml/predictor.py

@@ -1,21 +1,31 @@
 from __future__ import annotations
 
 import logging
-from functools import lru_cache
 from typing import Sequence
 
 from app.ml.feature_store import FeatureVector
-from app.ml.training import TrainingPipeline, TrainedArtifact
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainedArtifact, TrainingPipeline
 
 logger = logging.getLogger(__name__)
 
 
 class Predictor:
-    def __init__(self, pipeline: TrainingPipeline) -> None:
+    def __init__(
+        self,
+        pipeline: TrainingPipeline | None = None,
+        registry: ModelRegistry | None = None,
+    ) -> None:
+        if isinstance(pipeline, ModelRegistry) and registry is None:
+            registry = pipeline
+            pipeline = None
+        if pipeline is None and registry is None:
+            raise ValueError("Predictor erfordert TrainingPipeline oder ModelRegistry.")
         self._pipeline = pipeline
+        self._registry = registry
 
     def predict(self, artifact_id: str, entity: FeatureVector) -> str:
-        artifact = self._pipeline.export(artifact_id)
+        artifact = self._get_artifact(artifact_id)
         if entity.sensor_id not in artifact.supported_sensors:
             raise ValueError(
                 f"Sensor '{entity.sensor_id}' wird vom Modell '{artifact_id}' nicht unterstützt."
@@ -31,3 +41,10 @@ class Predictor:
         if not artifacts:
             raise ValueError("Kein trainiertes Modell gefunden.")
         return pipeline.export(artifacts[-1])
+
+    def _get_artifact(self, artifact_id: str) -> TrainedArtifact:
+        if self._registry is not None:
+            return self._registry.load_artifact(artifact_id)
+        if self._pipeline is not None:
+            return self._pipeline.export(artifact_id)
+        raise RuntimeError("Predictor nicht initialisiert.")

app/ml/registry/__init__.py

@@ -0,0 +1,3 @@
+from .model_registry import ModelRegistry
+
+__all__ = ["ModelRegistry"]

app/ml/registry/model_registry.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Iterable
+
+from app.ml.training import TrainedArtifact
+
+logger = logging.getLogger(__name__)
+
+
+class ModelRegistry:
+    def __init__(self, root: str | Path) -> None:
+        self._root = Path(root)
+        self._root.mkdir(parents=True, exist_ok=True)
+        self._artifacts: dict[str, TrainedArtifact] = {}
+
+    def register(self, artifact: TrainedArtifact) -> TrainedArtifact:
+        self._artifacts[artifact.artifact_id] = artifact
+        self._persist(artifact)
+        return artifact
+
+    def load_artifact(self, artifact_id: str) -> TrainedArtifact:
+        if artifact_id not in self._artifacts:
+            raise KeyError(f"Artifact '{artifact_id}' nicht registriert.")
+        return self._artifacts[artifact_id]
+
+    def list_models(self) -> Iterable[TrainedArtifact]:
+        return list(self._artifacts.values())
+
+    def _persist(self, artifact: TrainedArtifact) -> None:
+        target = self._root / f"{artifact.artifact_id}.json"
+        target.write_text(
+            f"{artifact.artifact_id}\t{','.join(artifact.supported_sensors)}\n",
+            encoding="utf-8",
+        )
+        logger.info("Modell gespeichert: %s", target)

backend/app.py

@@ -0,0 +1,32 @@
+from fastapi import FastAPI
+
+from backend.routes.ml import init_ml_routes
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainingPipeline
+from app.ml.feature_store import FeatureStore
+
+
+def create_app() -> FastAPI:
+    application = FastAPI(title="SillyHome Next ML")
+    init_ml_routes(application)
+    _seed_default_model(application.state if hasattr(application, "state") else application)
+    return application
+
+
+def _seed_default_model(state) -> None:  # noqa: ANN001
+    registry = getattr(state, "registry", None)
+    if registry is None:
+        registry = ModelRegistry(".model_store")
+        state.registry = registry
+
+    if list(registry.list_models()):
+        return
+
+    store = FeatureStore()
+    store.add("sensor.kitchen", {"temperature": 19.0})
+    pipeline = TrainingPipeline(store)
+    artifact = pipeline.run("default")
+    registry.register(artifact)
+
+
+app = create_app()

backend/routes/ml.py

@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import List, Sequence
+
+from fastapi import APIRouter
+from pydantic import BaseModel, Field
+
+from app.ml.feature_store import FeatureVector
+from app.ml.predictor import Predictor
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.training import TrainedArtifact
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/ml", tags=["ml"])
+
+
+class HealthResponse(BaseModel):
+    status: str
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+class PredictRequest(BaseModel):
+    model_id: str = Field(..., alias="modelId")
+    sensor_id: str
+    values: dict
+
+
+class PredictResponse(BaseModel):
+    model_id: str
+    sensor_id: str
+    prediction: str
+
+
+class BatchRequest(BaseModel):
+    requests: Sequence[PredictRequest]
+
+
+class BatchResponse(BaseModel):
+    predictions: Sequence[PredictResponse]
+
+
+class ModelsResponse(BaseModel):
+    models: List[str]
+
+
+@router.get("/health", response_model=HealthResponse, status_code=200)
+def health() -> HealthResponse:
+    return HealthResponse(status="ok")
+
+
+@router.get("/models", response_model=ModelsResponse, status_code=200)
+def list_models() -> ModelsResponse:
+    registry = _require_registry()
+    models = [artifact.artifact_id for artifact in registry.list_models()]
+    return ModelsResponse(models=models)
+
+
+@router.post("/predict", response_model=PredictResponse, status_code=200)
+def predict(request: PredictRequest) -> PredictResponse:
+    registry = _require_registry()
+    predictor = Predictor(registry=registry)
+    vector = FeatureVector(sensor_id=request.sensor_id, values=request.values)
+    try:
+        prediction = predictor.predict(request.model_id, vector)
+    except KeyError as exc:  # unknown artifact
+        raise _not_found_error(str(exc)) from exc
+    return PredictResponse(model_id=request.model_id, sensor_id=request.sensor_id, prediction=prediction)
+
+
+@router.post("/batch", response_model=BatchResponse, status_code=200)
+def predict_batch(request: BatchRequest) -> BatchResponse:
+    registry = _require_registry()
+    predictor = Predictor(registry=registry)
+    responses: List[PredictResponse] = []
+    for item in request.requests:
+        vector = FeatureVector(sensor_id=item.sensor_id, values=item.values)
+        try:
+            prediction = predictor.predict(item.model_id, vector)
+        except KeyError as exc:
+            raise _not_found_error(str(exc)) from exc
+        responses.append(
+            PredictResponse(model_id=item.model_id, sensor_id=item.sensor_id, prediction=prediction)
+        )
+    return BatchResponse(predictions=responses)
+
+
+def _require_registry() -> ModelRegistry:
+    from backend.app import _app_state
+
+    state_obj = _app_state()
+    registry = getattr(state_obj, "registry", None)
+    if registry is None:
+        raise RuntimeError("ML registry nicht initialisiert.")
+    return registry
+
+
+def init_ml_routes(app) -> None:  # noqa: ANN001
+    registry = ModelRegistry(".model_store")
+    app.state.registry = registry
+    app.include_router(router)
+    logger.info("ML routes registered")