ML-007: add retraining pipeline and API

Closes #13

backend/routes/ml.py

@@ -10,6 +10,7 @@ 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.retraining import retrain_model
 
 logger = logging.getLogger(__name__)
 
@@ -45,6 +46,23 @@ class ModelsResponse(BaseModel):
     models: list[str]
 
 
+class TrainingSample(BaseModel):
+    sensor_id: str = Field(min_length=1)
+    values: dict[str, float]
+    label: str | None = None
+
+
+class RetrainRequest(BaseModel):
+    model_id: str = Field(..., alias="modelId", min_length=1, max_length=128)
+    samples: list[TrainingSample] = Field(min_length=1)
+
+
+class RetrainResponse(BaseModel):
+    model_id: str
+    supported_sensors: list[str]
+    replaced: bool
+
+
 @router.get("/health", response_model=HealthResponse, status_code=200)
 def health() -> HealthResponse:
     return HealthResponse(status="ok")
@@ -57,6 +75,31 @@ def list_models(request: Request) -> ModelsResponse:
     return ModelsResponse(models=models)
 
 
+@router.post("/retrain", response_model=RetrainResponse, status_code=200)
+def retrain(payload: RetrainRequest, request: Request) -> RetrainResponse:
+    registry = _require_registry(request)
+    vectors = [
+        FeatureVector(
+            sensor_id=sample.sensor_id,
+            values=sample.values,
+            label=sample.label,
+        )
+        for sample in payload.samples
+    ]
+    try:
+        result = retrain_model(registry, payload.model_id, vectors)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
+            detail=str(exc),
+        ) from exc
+    return RetrainResponse(
+        model_id=result.artifact.artifact_id,
+        supported_sensors=list(result.artifact.supported_sensors),
+        replaced=result.replaced,
+    )
+
+
 @router.post("/predict", response_model=PredictResponse, status_code=200)
 def predict(payload: PredictRequest, request: Request) -> PredictResponse:
     registry = _require_registry(request)