"""ML-based decision strategy using IHP-ML-Models."""

from __future__ import annotations

import logging

from ..interfaces import ISchedulerReader
from ..interfaces.decision_strategy_interface import IDecisionStrategy
from ..value_objects import (
    EnvironmentState,
    HeatingAction,
    HeatingDecision,
)

_LOGGER = logging.getLogger(__name__)


class MLDecisionStrategy(IDecisionStrategy):
    """ML-powered heating decisions using IHP-ML-Models.

    This strategy delegates heating decisions to an AI model trained
    in the IHP-ML-Models add-on. It provides more sophisticated
    predictions by learning from historical data.

    Decision logic:
    - Queries ML model API for action predictions
    - Uses reinforcement learning for optimal timing
    - Adapts to specific home characteristics over time

    Requirements:
    - IHP-ML-Models Home Assistant add-on must be installed
    - ML model must be trained with historical heating data

    Attributes:
        _scheduler_reader: Interface to read scheduled timeslots
        _ml_client: Interface to ML model API (to be implemented)
    """

    def __init__(
        self,
        scheduler_reader: ISchedulerReader,
        # ml_client: IMLClient,  # TODO: Add interface for ML API
    ) -> None:
        """Initialize ML decision strategy.

        Args:
            scheduler_reader: Implementation of scheduler reading interface
            # ml_client: Client to communicate with ML model API
        """
        _LOGGER.debug("Initializing MLDecisionStrategy")
        self._scheduler_reader = scheduler_reader
        # self._ml_client = ml_client  # TODO: Implement ML client
        _LOGGER.warning(
            "MLDecisionStrategy is not fully implemented yet. "
            "Requires IMLClient interface and adapter."
        )

    async def decide_heating_action(
        self,
        environment: EnvironmentState,
    ) -> HeatingDecision:
        """Decide heating action using ML model.

        Args:
            environment: Current environmental conditions

        Returns:
            A heating decision predicted by the ML model
        """
        _LOGGER.debug("MLDecisionStrategy.decide_heating_action called")
        _LOGGER.debug(
            f"Environment: indoor={environment.indoor_temperature}°C, "
            f"outdoor={environment.outdoor_temp}°C, "
            f"humidity={environment.indoor_humidity}%"
        )

        # Get next scheduled timeslot for context
        next_timeslot = await self._scheduler_reader.get_next_timeslot()

        if next_timeslot is None:
            decision = HeatingDecision(
                action=HeatingAction.NO_ACTION, reason="No scheduled timeslots found"
            )
            _LOGGER.info(f"Decision: {decision.action.value} - {decision.reason}")
            return decision

        # TODO: Query ML model API for action prediction
        # Example:
        # ml_prediction = await self._ml_client.predict_action(
        #     environment=environment,
        #     next_timeslot=next_timeslot,
        # )
        #
        # return HeatingDecision(
        #     action=ml_prediction.action,
        #     target_temp=ml_prediction.target_temp,
        #     reason=f"ML prediction (confidence: {ml_prediction.confidence:.2%})"
        # )

        _LOGGER.warning(
            "ML model integration not implemented yet. " "Returning NO_ACTION as placeholder."
        )

        return HeatingDecision(
            action=HeatingAction.NO_ACTION,
            reason="ML strategy not fully implemented (requires IHP-ML-Models integration)",
        )

    async def check_overshoot_risk(
        self,
        environment: EnvironmentState,
        current_slope: float,
    ) -> HeatingDecision:
        """Check overshoot risk using ML model.

        Args:
            environment: Current environmental conditions
            current_slope: Current heating rate in °C/hour

        Returns:
            Decision to stop heating if ML model predicts overshoot
        """
        _LOGGER.debug("MLDecisionStrategy.check_overshoot_risk called")
        _LOGGER.debug(
            f"Current slope: {current_slope:.4f}°C/hour, "
            f"indoor_temp={environment.indoor_temperature}°C"
        )

        next_timeslot = await self._scheduler_reader.get_next_timeslot()

        if next_timeslot is None:
            decision = HeatingDecision(
                action=HeatingAction.NO_ACTION, reason="No scheduled timeslot to check against"
            )
            _LOGGER.info(f"Decision: {decision.action.value} - {decision.reason}")
            return decision

        # TODO: Query ML model for overshoot risk assessment
        # Example:
        # risk_assessment = await self._ml_client.assess_overshoot_risk(
        #     environment=environment,
        #     current_slope=current_slope,
        #     target_temp=next_timeslot.target_temp,
        #     target_time=next_timeslot.target_time,
        # )
        #
        # if risk_assessment.should_stop:
        #     return HeatingDecision(
        #         action=HeatingAction.STOP_HEATING,
        #         reason=f"ML detected overshoot risk (confidence: {risk_assessment.confidence:.2%})"
        #     )

        _LOGGER.warning(
            "ML overshoot detection not implemented yet. " "Returning NO_ACTION as placeholder."
        )

        return HeatingDecision(
            action=HeatingAction.NO_ACTION, reason="ML overshoot detection not fully implemented"
        )
