from __future__ import annotations

from dataclasses import dataclass

from app.ml.training import FeatureModel


@dataclass(frozen=True)
class FeatureExplanation:
    feature: str
    current_value: float
    predicted_value: float
    change: float
    direction: str
    sample_count: int
    historical_mean: float
    historical_range: tuple[float, float]
    standard_deviation: float
    trend_per_step: float
    confidence: float
    summary: str


def explain_feature(
    feature_name: str,
    current_value: float,
    predicted_value: float,
    model: FeatureModel,
) -> FeatureExplanation:
    change = predicted_value - current_value
    direction = _direction(change)
    summary = (
        f"{feature_name}: {direction}; Prognose {predicted_value:.3f} "
        f"aus aktuellem Wert {current_value:.3f} und Trend {model.slope:+.3f}. "
        f"Basis: {model.sample_count} Messwerte, Mittelwert {model.mean:.3f}, "
        f"Confidence {model.confidence:.0%}."
    )
    return FeatureExplanation(
        feature=feature_name,
        current_value=current_value,
        predicted_value=predicted_value,
        change=change,
        direction=direction,
        sample_count=model.sample_count,
        historical_mean=model.mean,
        historical_range=(model.minimum, model.maximum),
        standard_deviation=model.standard_deviation,
        trend_per_step=model.slope,
        confidence=model.confidence,
        summary=summary,
    )


def _direction(change: float) -> str:
    if abs(change) < 1e-12:
        return "stabil"
    return "steigend" if change > 0 else "fallend"