"""
对话 Agent：基于访谈问题清单，动态选择问题，实时生成回应
"""
from typing import List, Optional

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate

from services.llm_service import llm_service
from .prompts import ConversationStage, get_conversation_prompt


class ConversationAgent:
    """对话 Agent"""
    
    def __init__(self):
        # 使用 LLM 服务获取 LLM 实例
        self.llm = llm_service.get_llm()
        
        # 对话记忆
        self.memories: dict[str, ConversationBufferMemory] = {}
    
    def _get_memory(self, conversation_id: str) -> ConversationBufferMemory:
        """获取或创建对话记忆"""
        if conversation_id not in self.memories:
            self.memories[conversation_id] = ConversationBufferMemory(
                return_messages=True,
                memory_key="history"
            )
        return self.memories[conversation_id]
    
    def generate_response(
        self,
        conversation_id: str,
        user_message: str,
        current_stage: Optional[ConversationStage] = None,
        covered_topics: Optional[List[str]] = None
    ) -> str:
        """
        生成 Agent 回应
        
        Args:
            conversation_id: 对话 ID
            user_message: 用户消息
            current_stage: 当前对话阶段
            covered_topics: 已聊过的话题列表
        
        Returns:
            Agent 回应文本
        """
        if current_stage is None:
            current_stage = ConversationStage.CHILDHOOD
        
        if covered_topics is None:
            covered_topics = []
        
        # 如果没有配置 LLM，返回默认回应
        if not self.llm:
            return "抱歉，LLM 服务未配置。请设置 DEEPSEEK_API_KEY 或 LLM_API_KEY 环境变量。"
        
        # 获取系统提示词
        system_prompt = get_conversation_prompt(current_stage, covered_topics, user_message)
        
        # 获取对话记忆
        memory = self._get_memory(conversation_id)
        
        # 创建对话链
        prompt_template = PromptTemplate(
            input_variables=["history", "input"],
            template=f"{system_prompt}\n\n{{history}}\n\nHuman: {{input}}\n\nAssistant:"
        )
        
        chain = ConversationChain(
            llm=self.llm,
            prompt=prompt_template,
            memory=memory,
            verbose=False
        )
        
        # 生成回应
        response = chain.predict(input=user_message)
        
        return response
    
    def detect_stage(self, conversation_id: str, user_message: str) -> ConversationStage:
        """
        检测对话阶段
        
        Args:
            conversation_id: 对话 ID
            user_message: 用户消息
        
        Returns:
            检测到的对话阶段
        """
        # 简单的关键词检测（实际应该使用更智能的方法）
        message_lower = user_message.lower()
        
        if any(word in message_lower for word in ["童年", "小时候", "出生", "家庭背景"]):
            return ConversationStage.CHILDHOOD
        elif any(word in message_lower for word in ["上学", "学校", "老师", "同学", "教育"]):
            return ConversationStage.EDUCATION
        elif any(word in message_lower for word in ["工作", "职业", "事业", "公司", "同事"]):
            return ConversationStage.CAREER
        elif any(word in message_lower for word in ["伴侣", "孩子", "家庭", "家人", "结婚"]):
            return ConversationStage.FAMILY
        elif any(word in message_lower for word in ["信念", "价值观", "座右铭", "坚持", "原则"]):
            return ConversationStage.BELIEFS
        elif any(word in message_lower for word in ["总结", "回顾", "感激", "希望", "未来"]):
            return ConversationStage.SUMMARY
        else:
            # 默认返回当前阶段或童年阶段
            return ConversationStage.CHILDHOOD
    
    def clear_memory(self, conversation_id: str):
        """清除对话记忆"""
        if conversation_id in self.memories:
            del self.memories[conversation_id]