from __future__ import annotations

import logging
from dataclasses import dataclass
from typing import Sequence

from app.ml.feature_store import FeatureVector, FeatureStore

logger = logging.getLogger(__name__)


@dataclass
class TrainedArtifact:
    artifact_id: str
    supported_sensors: tuple[str, ...]


class TrainingPipeline:
    def __init__(self, store: FeatureStore) -> None:
        self._store = store
        self._artifacts: dict[str, TrainedArtifact] = {}

    def run(self, artifact_id: str) -> TrainedArtifact:
        vectors = self._store.all()
        if not vectors:
            raise ValueError("FeatureStore enthält keine Trainingsdaten.")

        sensors = tuple({vector.sensor_id for vector in vectors})
        artifact = TrainedArtifact(artifact_id=artifact_id, supported_sensors=sensors)
        self._artifacts[artifact_id] = artifact
        logger.info("Training abgeschlossen für %s mit %d Sensoren", artifact_id, len(sensors))
        return artifact

    def export(self, artifact_id: str) -> TrainedArtifact:
        if artifact_id not in self._artifacts:
            raise KeyError(f"Artifact '{artifact_id}' nicht gefunden.")
        return self._artifacts[artifact_id]