from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from typing import List, Dict

# 导入 ChatbotAgent 类和配置
# 假设 ChatbotAgent 是单例或在应用启动时创建
from app.core.chatbot_agent import ChatbotAgent
from app.core.config import settings

# --- Pydantic Data Models ---
# 用于接收用户消息的请求体
class ChatRequest(BaseModel):
    user_id: str = Field(..., description="Unique identifier for the user.")
    user_message: str = Field(..., description="The user's text message.")

# 用于启动搭配推荐的请求体
class OutfitStartRequest(BaseModel):
    user_id: str = Field(..., description="Unique identifier for the user.")
    stylist_name: str = Field("crystal", description="The name of the stylist guide to use (e.g., 'crystal').")
    # 用于从已选单品继续搭配，可选
    start_outfit: List[Dict[str, str]] = Field(default_factory=list, description="Optional list of items already selected.")

# 用于返回 LLM/Agent 的文本回复
class ChatResponse(BaseModel):
    response_text: str = Field(..., description="The chatbot's text response.")

# 搭配推荐的响应体 (StylistAgent的输出是JSON，这里简化为字符串，实际项目中会返回结构化的JSON)
class OutfitResponse(BaseModel):
    summary: str = Field(..., description="The conversation summary used for styling.")
    next_item_json: Dict = Field(..., description="The next recommended item in JSON format.")
    
# --- Router Setup ---

router = APIRouter(
    prefix="/chat",
    tags=["Chatbot & Styling"]
)

# 在应用启动时实例化 ChatbotAgent，以便在路由中使用同一个实例
# 注意：在真实的 FastAPI 应用中，我们通常使用依赖注入 (Depends) 来管理实例
try:
    # 假设 ChatbotAgent 的 __init__ 已经包含了所有依赖的初始化
    global_agent = ChatbotAgent()
except Exception as e:
    # 如果依赖（如 Redis 或 VectorDB）初始化失败，抛出错误
    print(f"FATAL: ChatbotAgent failed to initialize. Error: {e}")
    global_agent = None # 保持 None 状态，路由会抛出 500

@router.post("/", response_model=ChatResponse, summary="Process user message and get chatbot response")
def handle_chat_message(request: ChatRequest):
    """
    处理用户的聊天消息，将消息添加到历史记录，并调用 LLM 生成回复。
    """
    if not global_agent:
        raise HTTPException(status_code=500, detail="Chatbot agent not initialized.")
        
    try:
        response = global_agent.process_query(request.user_id, request.user_message)
        return ChatResponse(response_text=response)
    except Exception as e:
        print(f"Error processing chat query: {e}")
        # 返回一个通用错误信息，而不是内部错误
        raise HTTPException(status_code=500, detail="Internal server error while processing message.")

@router.post("/outfit/start", summary="Start outfit recommendation based on conversation history")
def start_outfit_recommendation(request: OutfitStartRequest):
    """
    基于用户的对话历史，生成搭配总结，并启动 Stylist Agent 推荐第一个单品。
    
    返回：对话总结和 Stylist Agent 推荐的第一个单品的 JSON。
    """
    if not global_agent:
        raise HTTPException(status_code=500, detail="Chatbot agent not initialized.")

    try:
        # 1. 获取对话总结
        request_summary = global_agent.get_conversation_summary(request.user_id)
        
        # 2. 调用 Stylist Agent 运行搭配流程
        # run_styling_process 应该返回 Stylist Agent 的第一个 JSON 输出 (recommend_item)
        # 假设 StylistAgent.run_styling_process 返回一个 JSON 字典
        next_item_json = global_agent.stylist_agent.run_styling_process(
            request_summary, 
            request.stylist_name, 
            request.start_outfit
        )
        
        return OutfitResponse(
            summary=request_summary,
            next_item_json=next_item_json
        )
        
    except Exception as e:
        print(f"Error starting outfit recommendation: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to start styling process: {e}")

# (可选) 搭配继续接口：用于处理用户的反馈和继续推荐
@router.post("/outfit/continue", summary="Continue outfit recommendation with user feedback")
def continue_outfit_recommendation():
    """
    这是一个占位符，用于处理用户对已推荐单品（如图片）的反馈，并让 Stylist Agent 推荐下一个单品。
    """
    raise HTTPException(status_code=501, detail="Endpoint for outfit continuation is not yet implemented.")