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

logger = logging.getLogger(__name__)


class Predictor:
    def __init__(self, pipeline: TrainingPipeline) -> None:
        self._pipeline = pipeline

    def predict(self, artifact_id: str, entity: FeatureVector) -> str:
        artifact = self._pipeline.export(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."
            )
        return f"{artifact_id}:{entity.sensor_id}:{entity.values}"

    def predict_batch(self, artifact_id: str, entities: Sequence[FeatureVector]) -> list[str]:
        return [self.predict(artifact_id, entity) for entity in entities]

    @staticmethod
    def default_artifact(pipeline: TrainingPipeline) -> TrainedArtifact:
        artifacts = list(pipeline._artifacts)
        if not artifacts:
            raise ValueError("Kein trainiertes Modell gefunden.")
        return pipeline.export(artifacts[-1])