118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
from __future__ import annotations
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import logging
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from datetime import datetime, timezone
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from typing import List, Sequence
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from fastapi import APIRouter, FastAPI, HTTPException, Request, status
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from pydantic import BaseModel, Field
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from app.ml.feature_store import FeatureVector
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from app.ml.predictor import Predictor
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from app.ml.registry.model_registry import ModelRegistry
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/ml", tags=["ml"])
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class HealthResponse(BaseModel):
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status: str
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updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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class PredictRequest(BaseModel):
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model_id: str = Field(..., alias="modelId")
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sensor_id: str
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values: dict
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class PredictResponse(BaseModel):
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model_id: str
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sensor_id: str
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prediction: str
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class BatchRequest(BaseModel):
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requests: Sequence[PredictRequest]
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class BatchResponse(BaseModel):
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predictions: Sequence[PredictResponse]
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class ModelsResponse(BaseModel):
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models: List[str]
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@router.get("/health", response_model=HealthResponse, status_code=200)
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def health() -> HealthResponse:
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return HealthResponse(status="ok")
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@router.get("/models", response_model=ModelsResponse, status_code=200)
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def list_models(request: Request) -> ModelsResponse:
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registry = _require_registry(request)
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models = [artifact.artifact_id for artifact in registry.list_models()]
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return ModelsResponse(models=models)
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@router.post("/predict", response_model=PredictResponse, status_code=200)
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def predict(payload: PredictRequest, request: Request) -> PredictResponse:
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registry = _require_registry(request)
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predictor = Predictor(registry=registry)
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vector = FeatureVector(sensor_id=payload.sensor_id, values=payload.values)
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try:
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prediction = predictor.predict(payload.model_id, vector)
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except KeyError as exc:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
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except ValueError as exc:
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raise HTTPException(
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status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
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detail=str(exc),
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) from exc
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return PredictResponse(
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model_id=payload.model_id,
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sensor_id=payload.sensor_id,
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prediction=prediction,
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)
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@router.post("/batch", response_model=BatchResponse, status_code=200)
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def predict_batch(payload: BatchRequest, request: Request) -> BatchResponse:
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registry = _require_registry(request)
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predictor = Predictor(registry=registry)
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responses: List[PredictResponse] = []
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for item in payload.requests:
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vector = FeatureVector(sensor_id=item.sensor_id, values=item.values)
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try:
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prediction = predictor.predict(item.model_id, vector)
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except KeyError as exc:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
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except ValueError as exc:
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raise HTTPException(
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status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
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detail=str(exc),
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) from exc
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responses.append(
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PredictResponse(model_id=item.model_id, sensor_id=item.sensor_id, prediction=prediction)
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)
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return BatchResponse(predictions=responses)
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def _require_registry(request: Request) -> ModelRegistry:
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registry = getattr(request.app.state, "registry", None)
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if not isinstance(registry, ModelRegistry):
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raise HTTPException(
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status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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detail="ML registry nicht initialisiert.",
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)
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return registry
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def init_ml_routes(app: FastAPI, model_store: str = ".model_store") -> None:
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registry = ModelRegistry(model_store)
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app.state.registry = registry
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app.include_router(router)
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logger.info("ML routes registered")
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