import json
from pathlib import Path
from typing import Any

from app.agents.llm_adapter import DeepSeekClient
from app.core.config import settings
from app.models.source_case import CaseBase
from app.models.training import SessionOrder, TrainingSession


class HintAgent:
    """新手提示 Agent：基于病例、对话和检查结果调用快速模型生成结构化提示。"""

    def __init__(self, llm: DeepSeekClient | None = None) -> None:
        self.llm = llm or DeepSeekClient()
        self.template_path = Path(__file__).resolve().parents[1] / "prompts" / "hint" / "novice_case_hint.md"

    async def generate(
        self,
        session: TrainingSession,
        case: CaseBase,
        memory_messages: list[dict],
        orders: list[SessionOrder],
        last_user_message: str | None = None,
    ) -> dict:
        """LLM 提示：标准化输入病例上下文并要求模型返回固定 JSON 结构。"""
        payload = self._build_input(session, case, memory_messages, orders, last_user_message)
        messages = [
            {"role": "system", "content": self._load_template()},
            {"role": "user", "content": json.dumps(payload, ensure_ascii=False)},
        ]
        try:
            response = await self.llm.chat(
                messages,
                settings.llm_fast_model,
                thinking_enabled=settings.llm_fast_thinking_enabled,
                response_format={"type": "json_object"},
                max_tokens=settings.llm_hint_max_tokens,
            )
            data = json.loads(response.content)
            return self._normalize_output(data, payload)
        except Exception:
            return self._fallback_output(payload)

    def _build_input(
        self,
        session: TrainingSession,
        case: CaseBase,
        memory_messages: list[dict],
        orders: list[SessionOrder],
        last_user_message: str | None,
    ) -> dict:
        """输入构造：把病例、会话、对话摘要和已申请检查整理为稳定 JSON。"""
        return {
            "case": {
                "case_id": case.id,
                "department": case.department.name if getattr(case, "department", None) else "",
                "title": case.title,
                "chief_complaint": case.chief_complaint,
                "key_symptoms": case.key_symptoms or [],
                "key_exams": case.key_exams or [],
                "key_points": case.key_points or [],
            },
            "session": {
                "mode": session.mode,
                "status": session.status,
            },
            "conversation_summary": [
                {"role": item.get("role"), "content": str(item.get("content", ""))[:240]}
                for item in memory_messages[-12:]
                if item.get("content")
            ],
            "ordered_results": [
                {
                    "item_code": order.item_code,
                    "item_name": order.item_name,
                    "item_type": order.item_type,
                    "result_text": order.result_text,
                    "is_key": order.is_key,
                    "is_abnormal": order.is_abnormal,
                }
                for order in orders
            ],
            "last_user_message": last_user_message or "",
        }

    def _load_template(self) -> str:
        """提示词读取：加载新手模式病例提示模板。"""
        if self.template_path.exists():
            return self.template_path.read_text(encoding="utf-8")
        return "你是医疗问诊训练提示 Agent，只输出合法 JSON。"

    def _normalize_output(self, data: Any, payload: dict) -> dict:
        """输出校验：确保 LLM 返回结构稳定，不把原始文本透传给前端。"""
        if not isinstance(data, dict):
            return self._fallback_output(payload)
        normalized = {
            "hints": self._clean_str_list(data.get("hints"))[:4],
            "missing_dimensions": self._clean_str_list(data.get("missing_dimensions"))[:6],
            "next_questions": self._clean_str_list(data.get("next_questions"))[:5],
            "recommended_orders": self._clean_orders(data.get("recommended_orders"))[:5],
        }
        if not any(normalized.values()):
            return self._fallback_output(payload)
        return normalized

    def _fallback_output(self, payload: dict) -> dict:
        """提示兜底：LLM 异常或 JSON 不合法时按病例关键点生成稳定提示。"""
        case = payload.get("case", {})
        ordered_codes = {item.get("item_code") for item in payload.get("ordered_results", [])}
        key_exams = case.get("key_exams") or []
        recommended_orders = []
        if "blood_routine" not in ordered_codes:
            recommended_orders.append({"item_code": "blood_routine", "reason": "用于初步判断感染及炎症反应"})
        if "crp" not in ordered_codes:
            recommended_orders.append({"item_code": "crp", "reason": "用于辅助判断炎症程度"})
        if "chest_xray" not in ordered_codes:
            recommended_orders.append({"item_code": "chest_xray", "reason": "用于判断肺部感染影像学证据"})
        if "spo2" not in ordered_codes:
            recommended_orders.append({"item_code": "spo2", "reason": "用于判断氧合和病情严重程度"})
        return {
            "hints": [
                f"本病例主诉为{case.get('chief_complaint') or '当前症状'}，问诊要围绕起病时间、症状演变和严重程度展开。",
                "当前提示来自病例关键症状、关键检查和已完成对话的结构化兜底分析。",
                "获得检查结果后，需要把异常结果用于诊断依据和病情严重程度判断。",
            ],
            "missing_dimensions": ["既往史", "严重程度评估", "人文沟通"],
            "next_questions": [
                "孩子最高体温多少？退烧药后能不能降下来？",
                "有没有喘息、气促、口唇发绀或呼吸困难？",
                "以前有没有喘息、哮喘、湿疹或药物过敏史？",
                "精神、食欲、饮水和尿量怎么样？",
                "家属现在最担心什么？",
            ],
            "recommended_orders": recommended_orders or [
                {"item_code": str(item), "reason": "病例关键检查，需要结合结果完善诊断依据"} for item in key_exams[:3]
            ],
        }

    def _clean_str_list(self, value: Any) -> list[str]:
        """字段清洗：把模型返回的数组字段压成字符串列表。"""
        if not isinstance(value, list):
            return []
        return [str(item).strip() for item in value if str(item).strip()]

    def _clean_orders(self, value: Any) -> list[dict]:
        """推荐检查清洗：只保留 item_code 和 reason 两个前端需要的字段。"""
        if not isinstance(value, list):
            return []
        cleaned = []
        for item in value:
            if not isinstance(item, dict):
                continue
            code = str(item.get("item_code", "")).strip()
            reason = str(item.get("reason", "")).strip()
            if code:
                cleaned.append({"item_code": code, "reason": reason})
        return cleaned