from __future__ import annotations

from app.ml.explanation import explain_feature
from app.ml.training import FeatureModel


def _model(slope: float) -> FeatureModel:
    return FeatureModel(
        sample_count=4,
        mean=20.0,
        standard_deviation=1.0,
        minimum=18.0,
        maximum=22.0,
        slope=slope,
        intercept=18.5,
    )


def test_explain_feature_describes_rising_forecast() -> None:
    explanation = explain_feature("temperature", 21.0, 21.5, _model(0.5))

    assert explanation.direction == "steigend"
    assert explanation.change == 0.5
    assert explanation.historical_range == (18.0, 22.0)
    assert "4 Messwerte" in explanation.summary
    assert "Trend +0.500" in explanation.summary


def test_explain_feature_describes_stable_and_falling_forecasts() -> None:
    stable = explain_feature("humidity", 50.0, 50.0, _model(0.0))
    falling = explain_feature("temperature", 21.0, 20.5, _model(-0.5))

    assert stable.direction == "stabil"
    assert falling.direction == "fallend"