from __future__ import annotations

from typing import Any

from farm_data.services import clone_snapshot_as_runtime_plant, get_primary_plant_snapshot
from irrigation.evapotranspiration import calculate_forecast_water_needs, resolve_crop_profile

from farm_data.models import SensorData
from rag.services import get_water_need_prediction_insight

from .services import get_forecast_for_location


def build_water_need_prediction_payload(*, sensor: Any, forecasts: list[Any]) -> dict[str, Any]:
    location = getattr(sensor, "center_location", None)
    plant = clone_snapshot_as_runtime_plant(get_primary_plant_snapshot(sensor))
    irrigation_method = getattr(sensor, "irrigation_method", None)

    if not forecasts or location is None:
        return {
            "totalNext7Days": 0,
            "unit": "mm",
            "categories": [],
            "series": [],
            "dailyBreakdown": [],
            "cropProfile": {},
            "irrigationEfficiencyPercent": None,
        }

    crop_profile = resolve_crop_profile(plant)
    efficiency = getattr(irrigation_method, "water_efficiency_percent", None) if irrigation_method else None
    daily = calculate_forecast_water_needs(
        forecasts=forecasts[:7],
        latitude_deg=float(location.latitude),
        crop_profile=crop_profile,
        growth_stage=crop_profile.get("current_stage"),
        irrigation_efficiency_percent=efficiency,
    )
    daily_requirements = [round(item["gross_irrigation_mm"], 2) for item in daily]

    return {
        "totalNext7Days": round(sum(daily_requirements), 2),
        "unit": "mm",
        "categories": [f"روز {index}" for index in range(1, len(daily_requirements) + 1)],
        "series": [{"name": "نیاز آبی تعدیل‌شده", "data": daily_requirements}],
        "dailyBreakdown": daily,
        "cropProfile": crop_profile,
        "irrigationEfficiencyPercent": efficiency,
    }


class WaterNeedPredictionService:
    def get_water_need_prediction(self, *, farm_uuid: str) -> dict[str, Any]:
        sensor = (
            SensorData.objects.select_related("center_location", "irrigation_method")
            .prefetch_related("plant_assignments__plant")
            .filter(farm_uuid=farm_uuid)
            .first()
        )
        if sensor is None:
            raise ValueError("Farm not found.")

        forecasts = get_forecast_for_location(sensor.center_location, days=7)
        payload = build_water_need_prediction_payload(sensor=sensor, forecasts=forecasts)
        insight = get_water_need_prediction_insight(
            farm_uuid=farm_uuid,
            prediction_payload=payload,
        )

        return {
            "farm_uuid": farm_uuid,
            **payload,
            "insight": {
                "summary": insight.get("summary"),
                "irrigation_outlook": insight.get("irrigation_outlook"),
                "recommended_action": insight.get("recommended_action"),
                "risk_note": insight.get("risk_note"),
                "confidence": insight.get("confidence"),
            },
            "raw_response": insight.get("raw_response"),
        }