"""
سرویس توصیه آبیاری — بدون API، قابل فراخوانی از سایر سرویس‌ها
از RAG با پایگاه دانش irrigation و لحن مخصوص آبیاری استفاده می‌کند.
"""
import json
import logging

from django.apps import apps
from django.db import transaction
from irrigation.models import IrrigationMethod
from irrigation.evapotranspiration import calculate_forecast_water_needs, resolve_crop_profile, resolve_kc
from farm_data.models import SensorData
from rag.api_provider import get_chat_client
from rag.chat import (
    _complete_audit_log,
    _create_audit_log,
    _fail_audit_log,
    _load_service_tone,
    build_rag_context,
)
from rag.config import load_rag_config, RAGConfig, get_service_config
from rag.user_data import build_plant_text, build_irrigation_method_text
from weather.models import WeatherForecast

logger = logging.getLogger(__name__)

KB_NAME = "irrigation"
SERVICE_ID = "irrigation"

DEFAULT_IRRIGATION_PROMPT = (
    "از محاسبات FAO-56 و خروجی بهینه ساز شبیه سازی برای ساخت توصیه آبیاری استفاده کن. "
    "اگر بلوک [خروجی بهینه ساز شبیه سازی] وجود داشت، همان را مرجع اصلی اعداد قرار بده. "
    "پاسخ فقط JSON معتبر با کلید sections باشد و عدد جدید متناقض نساز."
)


def _get_optimizer():
    return apps.get_app_config("crop_simulation").get_recommendation_optimizer()


def _unique_items(items: list[str]) -> list[str]:
    seen = set()
    output = []
    for item in items:
        normalized = (item or "").strip()
        if not normalized or normalized in seen:
            continue
        seen.add(normalized)
        output.append(normalized)
    return output


def _find_section(sections: list[dict], section_type: str) -> dict | None:
    for section in sections:
        if isinstance(section, dict) and section.get("type") == section_type:
            return section
    return None


def _field_sources(llm_section: dict, fallback_section: dict, merged_section: dict) -> dict[str, str]:
    sources: dict[str, str] = {}
    for key, value in merged_section.items():
        if key == "provenance":
            continue
        llm_value = llm_section.get(key)
        fallback_value = fallback_section.get(key)
        if key in llm_section and value == llm_value and value != fallback_value:
            sources[key] = "llm"
        elif key in fallback_section and value == fallback_value and value != llm_value:
            sources[key] = "fallback"
        elif key in llm_section and key in fallback_section and llm_value == fallback_value == value:
            sources[key] = "shared"
        elif key in llm_section and key in fallback_section:
            sources[key] = "merged"
        else:
            sources[key] = "fallback" if key in fallback_section else "llm"
    return sources


def _attach_provenance(section_type: str, llm_section: dict, fallback_section: dict, merged_section: dict) -> dict:
    merged = dict(merged_section)
    field_sources = _field_sources(llm_section, fallback_section, merged)
    merged["provenance"] = {
        "sectionType": section_type,
        "llmProvided": bool(llm_section),
        "fallbackUsed": any(source != "llm" for source in field_sources.values()),
        "fieldSources": field_sources,
    }
    return merged


def _fallback_with_provenance(fallback: dict, reason: str) -> dict:
    sections = []
    for section in fallback.get("sections", []):
        section_with_provenance = dict(section)
        section_with_provenance["provenance"] = {
            "sectionType": section.get("type"),
            "llmProvided": False,
            "fallbackUsed": True,
            "fieldSources": {
                key: "fallback"
                for key in section.keys()
                if key != "provenance"
            },
        }
        sections.append(section_with_provenance)
    return {
        "sections": sections,
        "mergeMetadata": {
            "source": "fallback_only",
            "reason": reason,
        },
    }


def _build_irrigation_fallback(
    *,
    optimized_result: dict | None,
    daily_water_needs: list[dict],
) -> dict:
    if optimized_result:
        recommended = optimized_result["recommended_strategy"]
        content = (
            f"{recommended['events']} نوبت آبیاری با حدود "
            f"{recommended['amount_per_event_mm']} میلی متر در هر نوبت اجرا شود."
        )
        list_items = [
            f"در بازه اعتبار حدود {recommended['total_irrigation_mm']} میلی متر آب توزیع شود.",
            f"نوبت های پیشنهادی: {', '.join(recommended['event_dates']) or 'بر اساس پایش روزانه مزرعه'}",
            f"رطوبت خاک نزدیک {recommended['moisture_target_percent']} درصد نگه داشته شود.",
        ]
        warning = optimized_result.get("alternatives", [])
        warning_text = (
            f"اگر شرایط مزرعه تغییر کرد، سناریوی جایگزین {warning[0]['label']} هم قابل بررسی است."
            if warning
            else "در صورت تغییر ناگهانی بارش یا باد، برنامه را دوباره ارزیابی کنید."
        )
        explanation = " ".join(recommended.get("reasoning", []))
        return {
            "sections": [
                {
                    "type": "recommendation",
                    "title": "برنامه آبیاری بهینه",
                    "icon": "droplet",
                    "content": content,
                    "frequency": f"{recommended['events']} نوبت در بازه اعتبار",
                    "amount": (
                        f"{recommended['amount_per_event_mm']} میلی متر در هر نوبت "
                        f"(جمع کل {recommended['total_irrigation_mm']} میلی متر)"
                    ),
                    "timing": recommended["timing"],
                    "validityPeriod": recommended["validity_period"],
                    "expandableExplanation": explanation,
                },
                {
                    "type": "list",
                    "title": "اقدامات اجرایی",
                    "icon": "list",
                    "items": _unique_items(list_items),
                },
                {
                    "type": "warning",
                    "title": "هشدار آبیاری",
                    "icon": "alert-triangle",
                    "content": warning_text,
                },
            ]
        }

    total_mm = round(sum(float(item.get("gross_irrigation_mm", 0.0) or 0.0) for item in daily_water_needs), 2)
    return {
        "sections": [
            {
                "type": "recommendation",
                "title": "برنامه آبیاری پیشنهادی",
                "icon": "droplet",
                "content": "پایش رطوبت خاک ادامه پیدا کند و برنامه آبیاری بر اساس نیاز روزانه تنظیم شود.",
                "frequency": "بر اساس پایش روزانه",
                "amount": f"جمع نیاز تخمینی این بازه حدود {total_mm} میلی متر است.",
                "timing": "اوایل صبح یا نزدیک غروب",
                "validityPeriod": "معتبر برای 3 روز آینده",
                "expandableExplanation": "به دلیل محدود بودن داده ها، توصیه با اتکا به نیاز آبی روزانه ساخته شده است.",
            },
            {
                "type": "list",
                "title": "اقدامات اجرایی",
                "icon": "list",
                "items": [
                    "قبل از هر نوبت آبیاری رطوبت خاک سطحی را دوباره بررسی کنید.",
                    "اگر بارش موثر رخ داد، نوبت بعدی را به تعویق بیندازید.",
                ],
            },
            {
                "type": "warning",
                "title": "هشدار آبیاری",
                "icon": "alert-triangle",
                "content": "با تغییر دما یا بارش پیش بینی شده، برنامه فعلی باید بازبینی شود.",
            },
        ]
    }


def _merge_irrigation_response(
    *,
    parsed_result: dict,
    optimized_result: dict | None,
    daily_water_needs: list[dict],
) -> dict:
    fallback = _build_irrigation_fallback(
        optimized_result=optimized_result,
        daily_water_needs=daily_water_needs,
    )
    if not isinstance(parsed_result, dict):
        return _fallback_with_provenance(fallback, "invalid_llm_payload")

    sections = parsed_result.get("sections")
    if not isinstance(sections, list):
        return _fallback_with_provenance(fallback, "missing_sections")

    recommendation = _find_section(sections, "recommendation") or {}
    list_section = _find_section(sections, "list") or {}
    warning_section = _find_section(sections, "warning") or {}

    fallback_recommendation = fallback["sections"][0]
    fallback_list = fallback["sections"][1]
    fallback_warning = fallback["sections"][2]

    merged_recommendation = {**recommendation, **fallback_recommendation}
    merged_recommendation["expandableExplanation"] = (
        recommendation.get("expandableExplanation")
        or fallback_recommendation["expandableExplanation"]
    )
    merged_recommendation["content"] = recommendation.get("content") or fallback_recommendation["content"]
    merged_recommendation["title"] = recommendation.get("title") or fallback_recommendation["title"]

    merged_list = {
        **fallback_list,
        **list_section,
        "items": _unique_items(
            list(list_section.get("items", [])) + list(fallback_list["items"])
        )[:5],
    }
    merged_warning = {
        **fallback_warning,
        **warning_section,
        "content": warning_section.get("content") or fallback_warning["content"],
    }

    merged_recommendation = _attach_provenance(
        "recommendation",
        recommendation,
        fallback_recommendation,
        merged_recommendation,
    )
    merged_list = _attach_provenance(
        "list",
        list_section,
        fallback_list,
        merged_list,
    )
    merged_warning = _attach_provenance(
        "warning",
        warning_section,
        fallback_warning,
        merged_warning,
    )

    return {
        "sections": [merged_recommendation, merged_list, merged_warning],
        "mergeMetadata": {
            "source": "llm_with_fallback_merge",
            "llmSectionsDetected": [section.get("type") for section in sections if isinstance(section, dict)],
            "fallbackSectionsApplied": [
                item["type"]
                for item in (fallback_recommendation, fallback_list, fallback_warning)
            ],
        },
    }


def _resolve_irrigation_method(
    sensor: SensorData | None,
    irrigation_method_name: str | None,
) -> IrrigationMethod | None:
    if irrigation_method_name:
        return IrrigationMethod.objects.filter(name=irrigation_method_name).first()
    if sensor is not None:
        return sensor.irrigation_method
    return None


def _persist_irrigation_method_on_farm(
    sensor: SensorData | None,
    irrigation_method: IrrigationMethod | None,
) -> None:
    if sensor is None or irrigation_method is None:
        return
    if sensor.irrigation_method_id == irrigation_method.id:
        return

    with transaction.atomic():
        sensor.irrigation_method = irrigation_method
        sensor.save(update_fields=["irrigation_method", "updated_at"])


def get_irrigation_recommendation(
    farm_uuid: str | None = None,
    plant_name: str | None = None,
    growth_stage: str | None = None,
    irrigation_method_name: str | None = None,
    query: str | None = None,
    config: RAGConfig | None = None,
    limit: int = 8,
    sensor_uuid: str | None = None,
) -> dict:
    """
    توصیه آبیاری برای یک مزرعه.
    از RAG با پایگاه دانش irrigation استفاده می‌کند.

    Args:
        farm_uuid: شناسه مزرعه
        plant_name: نام گیاه (برای بارگذاری مشخصات از جدول Plant)
        growth_stage: مرحله رشد گیاه
        irrigation_method_name: نام روش آبیاری (برای بارگذاری از جدول IrrigationMethod)
        query: سوال اختیاری
        config: تنظیمات RAG
        limit: تعداد چانک‌های بازیابی‌شده

    Returns:
        dict ساختاریافته برای توصیه آبیاری
    """
    cfg = config or load_rag_config()
    service = get_service_config(SERVICE_ID, cfg)
    service_cfg = RAGConfig(
        embedding=cfg.embedding,
        qdrant=cfg.qdrant,
        chunking=cfg.chunking,
        llm=service.llm,
        knowledge_bases=cfg.knowledge_bases,
        services=cfg.services,
        chromadb=cfg.chromadb,
    )
    client = get_chat_client(service_cfg)
    model = service.llm.model

    resolved_farm_uuid = str(farm_uuid or sensor_uuid or "").strip()
    if not resolved_farm_uuid:
        raise ValueError("farm_uuid is required.")

    user_query = query or "توصیه آبیاری برای مزرعه من چیست؟"

    sensor = (
        SensorData.objects.select_related("center_location", "irrigation_method")
        .prefetch_related("plants")
        .filter(farm_uuid=resolved_farm_uuid)
        .first()
    )
    irrigation_method = _resolve_irrigation_method(sensor, irrigation_method_name)
    _persist_irrigation_method_on_farm(sensor, irrigation_method)

    plant = None
    resolved_plant_name = plant_name
    if sensor is not None and plant_name:
        plant = sensor.plants.filter(name=plant_name).first()
    elif sensor is not None:
        plant = sensor.plants.first()
        if plant is not None:
            resolved_plant_name = plant.name
    crop_profile = resolve_crop_profile(plant, growth_stage=growth_stage)
    active_kc = resolve_kc(crop_profile, growth_stage=growth_stage)
    forecasts = []
    daily_water_needs = []
    optimized_result = None
    if sensor is not None:
        forecasts = list(
            WeatherForecast.objects.filter(location=sensor.center_location, forecast_date__isnull=False)
            .order_by("forecast_date")[:7]
        )
        efficiency_percent = (
            getattr(irrigation_method, "water_efficiency_percent", None)
            if irrigation_method
            else None
        )
        daily_water_needs = calculate_forecast_water_needs(
            forecasts=forecasts,
            latitude_deg=float(sensor.center_location.latitude),
            crop_profile=crop_profile,
            growth_stage=growth_stage,
            irrigation_efficiency_percent=efficiency_percent,
        )
        if plant is not None and forecasts:
            optimized_result = _get_optimizer().optimize_irrigation(
                sensor=sensor,
                plant=plant,
                forecasts=forecasts,
                daily_water_needs=daily_water_needs,
                growth_stage=growth_stage,
                irrigation_method=irrigation_method,
            )

    context = build_rag_context(
        user_query, resolved_farm_uuid, config=cfg, limit=limit, kb_name=KB_NAME, service_id=SERVICE_ID,
    )

    extra_parts: list[str] = []
    resolved_irrigation_method_name = (
        irrigation_method.name if irrigation_method is not None else None
    )
    if resolved_plant_name and growth_stage:
        plant_text = build_plant_text(resolved_plant_name, growth_stage)
        if plant_text:
            extra_parts.append("[اطلاعات گیاه]\n" + plant_text)
    if resolved_irrigation_method_name:
        method_text = build_irrigation_method_text(resolved_irrigation_method_name)
        if method_text:
            extra_parts.append("[روش آبیاری انتخابی]\n" + method_text)
    if daily_water_needs:
        total_mm = round(sum(item["gross_irrigation_mm"] for item in daily_water_needs), 2)
        schedule_lines = [
            f"- {item['forecast_date']}: ET0={item['et0_mm']} mm, ETc={item['etc_mm']} mm, "
            f"بارش مؤثر={item['effective_rainfall_mm']} mm, نیاز آبی={item['gross_irrigation_mm']} mm, "
            f"زمان پیشنهادی={item['irrigation_timing']}"
            for item in daily_water_needs
        ]
        extra_parts.append(
            "[خروجی قطعی محاسبات FAO-56]\n"
            f"کل نیاز آبی ۷ روز آینده: {total_mm} mm\n"
            f"Kc مورد استفاده: {active_kc}\n"
            + "\n".join(schedule_lines)
        )
    if optimized_result is not None:
        extra_parts.append(
            "[خروجی بهینه ساز شبیه سازی]\n"
            + optimized_result["context_text"]
        )
    if extra_parts:
        context = "\n\n---\n\n".join(extra_parts) + ("\n\n---\n\n" + context if context else "")

    tone = _load_service_tone(service, cfg)
    system_parts = [tone] if tone else []
    if service.system_prompt:
        system_parts.append(service.system_prompt)
    system_parts.append(DEFAULT_IRRIGATION_PROMPT)
    if context:
        system_parts.append("\n\n" + context)
    system_content = "\n".join(system_parts)

    messages = [
        {"role": "system", "content": system_content},
        {"role": "user", "content": user_query},
    ]
    audit_log = _create_audit_log(
        farm_uuid=resolved_farm_uuid,
        service_id=SERVICE_ID,
        model=model,
        query=user_query,
        system_prompt=system_content,
        messages=messages,
    )

    try:
        response = client.chat.completions.create(
            model=model,
            messages=messages,
        )
        raw = response.choices[0].message.content.strip()
    except Exception as exc:
        logger.error("Irrigation recommendation error for %s: %s", resolved_farm_uuid, exc)
        result = _build_irrigation_fallback(
            optimized_result=optimized_result,
            daily_water_needs=daily_water_needs,
        )
        result["error"] = f"خطا در دریافت توصیه: {exc}"
        result["raw_response"] = None
        _fail_audit_log(
            audit_log,
            str(exc),
            response_text=json.dumps(result, ensure_ascii=False, default=str),
        )
        return result

    try:
        cleaned = raw
        if cleaned.startswith("```"):
            cleaned = cleaned.strip("`").removeprefix("json").strip()
        result = json.loads(cleaned)
    except (json.JSONDecodeError, ValueError):
        result = {}

    result = _merge_irrigation_response(
        parsed_result=result,
        optimized_result=optimized_result,
        daily_water_needs=daily_water_needs,
    )
    result["raw_response"] = raw
    result["water_balance"] = {
        "daily": daily_water_needs,
        "crop_profile": crop_profile,
        "active_kc": active_kc,
    }
    result["simulation_optimizer"] = optimized_result
    result["selected_irrigation_method"] = (
        {
            "id": irrigation_method.id,
            "name": irrigation_method.name,
            "category": irrigation_method.category,
            "water_efficiency_percent": irrigation_method.water_efficiency_percent,
        }
        if irrigation_method is not None
        else None
    )
    _complete_audit_log(
        audit_log,
        json.dumps(result, ensure_ascii=False, default=str),
    )
    return result