"""
PregoPal - 对话管理器（全双工版本）
======================================
对话管理 + 系统提示词构建 + 标记解析 + 结构化提取

全双工对话流程：
  用户语音 → VoiceProcessor → ASR文本 → ConversationManager → 
  ModelLoader.voice_chat() → AI文本+TTS → DietExtractor → DietLogger
"""
import datetime
import logging
from typing import Optional

from modules.diet_extractor import DietExtractor
from modules.diet_logger import DietLogger
from modules.family_manager import PreferenceManager, MemoryManager
from modules.nutrition_analyzer import NutritionAnalyzer

logger = logging.getLogger(__name__)


class ConversationManager:
    """对话管理：模式切换、提示词构建、输出解析、结构化标记"""
    
    MODE_TASK = "task"    # 任务模式（自动提取营养信息）
    MODE_CHAT = "chat"    # 闲聊模式
    
    # 系统提示词模板（全双工语音版本）
    SYSTEM_PROMPT_ZH = (
        "你是PregoPal，一位贴心的孕期营养健康顾问。"
        "你通过语音对话与孕妇及其家人交流。"
        "请用中文回答，给出简短实用、温暖的孕期营养建议。\n\n"
        "【对话规则】\n"
        "1. 回答控制在2-3句话，简洁明了\n"
        "2. 关注孕期营养（叶酸、铁、钙、DHA、蛋白质等）\n"
        "3. 给出具体的食物推荐\n"
        "4. 语气温暖鼓励，像家人一样关心\n\n"
        "【结构化标记规则】\n"
        "当用户提到以下内容时，在回复末尾添加对应标记：\n"
        "- [EXTRACT_DIET]早餐：小米粥，午餐：番茄牛腩  — 当用户提到吃了什么时\n"
        "- [EXTRACT_PREFERENCE]不喜欢：油腻食物 — 当用户提到饮食偏好时\n"
        "- [EXTRACT_WEIGHT]体重：65kg — 当用户提到体重时\n"
        "- [EXTRACT_MEMORY]孕妇：对海鲜过敏 — 当需要记录家庭成员信息时\n\n"
        "如果什么都不确定，专注于给出友好的孕期营养建议即可。"
    )
    
    SYSTEM_PROMPT_EN = (
        "You are PregoPal, a caring pregnancy nutrition advisor. "
        "You communicate through voice conversation with pregnant women and their families. "
        "Please answer in Chinese warmly and concisely. "
        "Keep responses to 2-3 sentences. "
        "If the user mentions eating something, use [EXTRACT_DIET] marker. "
        "If the user mentions preferences, use [EXTRACT_PREFERENCE] marker."
    )
    
    def __init__(self, lang: str = "zh"):
        self.current_mode = self.MODE_CHAT
        self.current_speaker = None
        self.conversation_history = []
        self.lang = lang
        self.diet_extractor = DietExtractor()
        self.diet_logger = DietLogger()
        self.preference_manager = PreferenceManager()
        self.memory_manager = MemoryManager()
    
    def build_system_prompt(self, mode: Optional[str] = None) -> str:
        """
        构建系统提示词
        
        Args:
            mode: 对话模式 (task/chat)，None 使用当前模式
            
        Returns:
            str: 系统提示词
        """
        mode = mode or self.current_mode
        prompt = self.SYSTEM_PROMPT_ZH if self.lang == "zh" else self.SYSTEM_PROMPT_EN
        
        if self.current_speaker:
            prompt += f"\n\n当前说话人：{self.current_speaker.get('name', '用户')}"
        
        if mode == self.MODE_TASK:
            prompt += "\n\n【当前模式：营养记录】请主动询问用户的饮食情况，并记录"
        
        return prompt
    
    def switch_mode(self, mode: str):
        """切换对话模式"""
        self.current_mode = mode
        logger.info(f"对话模式切换为: {mode}")
    
    def set_speaker(self, speaker_info: dict):
        """设置当前说话人"""
        self.current_speaker = speaker_info
    
    def add_message(self, role: str, content: str):
        """添加对话历史"""
        self.conversation_history.append({
            "role": role,
            "content": content,
            "timestamp": datetime.datetime.now().isoformat(),
        })
        # 保持最近 10 轮
        if len(self.conversation_history) > 20:
            self.conversation_history = self.conversation_history[-20:]
    
    def build_messages(self, user_input: str, system_prompt: Optional[str] = None) -> list:
        """
        构建完整的 messages 列表供模型调用
        
        Args:
            user_input: 用户输入文本
            system_prompt: 可覆盖默认 system prompt
            
        Returns:
            list[dict]: messages
        """
        messages = [{"role": "system", "content": system_prompt or self.build_system_prompt()}]
        
        # 添加上下文（最近 4 轮）
        for msg in self.conversation_history[-8:]:
            messages.append({"role": msg["role"], "content": msg["content"]})
        
        messages.append({"role": "user", "content": user_input})
        return messages
    
    def parse_response(self, response: str) -> dict:
        """
        解析模型回复，提取结构化信息
        
        Args:
            response: 模型回复文本
            
        Returns:
            dict: {
                "text": str,               # 纯文本（去除标记）
                "diet_record": dict|None,   # 饮食记录
                "preferences": list|None,   # 偏好
                "memories": list|None,      # 记忆
                "weight": dict|None,        # 体重
            }
        """
        result = {
            "text": response,
            "diet_record": None,
            "preferences": None,
            "memories": None,
            "weight": None,
        }
        
        # 用 DietExtractor 做统一提取
        try:
            extracted = self.diet_extractor.extract_all(response)
            if extracted:
                result.update(extracted)
        except Exception as e:
            logger.warning(f"DietExtractor 解析异常: {e}")
        
        return result
    
    def process_voice_result(self, result: dict) -> dict:
        """
        处理语音对话结果，自动执行结构化提取和存储
        
        Args:
            result: ModelLoader.voice_chat() 的返回
            
        Returns:
            dict: 增强后的处理结果
        """
        if not result.get("success"):
            return result
        
        text = result.get("text", "")
        if not text:
            return result
        
        # 解析标记
        parsed = self.parse_response(text)
        result["parsed"] = parsed
        result["clean_text"] = parsed["text"]
        
        # 自动存储饮食记录
        if parsed.get("diet_record"):
            try:
                self.diet_logger.log_diet(
                    member_id=self.current_speaker.get("id", "ai") if self.current_speaker else "ai",
                    member_name=self.current_speaker.get("name", "AI识别") if self.current_speaker else "AI识别",
                    meals=parsed["diet_record"].get("meals", {}),
                    notes=f"语音对话 {datetime.date.today()}"
                )
                logger.info("饮食记录已自动存储")
            except Exception as e:
                logger.warning(f"存储饮食记录失败: {e}")
        
        # 自动存储偏好
        if parsed.get("preferences"):
            try:
                for pref in parsed["preferences"]:
                    self.preference_manager.add_preference(
                        member_name=pref.get("member", "家人"),
                        category=pref.get("category", "饮食偏好"),
                        content=pref.get("content", ""),
                    )
            except Exception as e:
                logger.warning(f"存储偏好失败: {e}")
        
        return result
    
    def get_context_summary(self) -> str:
        """获取对话上下文摘要（用于系统提示词注入）"""
        if not self.conversation_history:
            return ""
        recent = self.conversation_history[-4:]
        summary = "最近对话：\n"
        for msg in recent:
            role = "用户" if msg["role"] == "user" else "AI"
            summary += f"{role}: {msg['content'][:100]}...\n"
        return summary