"""
Aftersale Agent - Handles returns, exchanges, and complaints
"""
import json
from typing import Any

from core.state import AgentState, ConversationState, add_tool_call, set_response, update_context
from core.llm import get_llm_client, Message
from utils.logger import get_logger

logger = get_logger(__name__)


AFTERSALE_AGENT_PROMPT = """你是一个专业的 B2B 售后服务助手。
你的职责是帮助用户处理售后问题，包括：
- 退货申请
- 换货申请
- 投诉处理
- 工单创建
- 售后进度查询

## 可用工具

1. **apply_return** - 退货申请
   - order_id: 订单号
   - items: 退货商品列表 [{item_id, quantity, reason}]
   - description: 问题描述
   - images: 图片URL列表（可选）

2. **apply_exchange** - 换货申请
   - order_id: 订单号
   - items: 换货商品列表 [{item_id, reason}]
   - description: 问题描述

3. **create_complaint** - 创建投诉
   - type: 投诉类型（product_quality/service/logistics/other）
   - title: 投诉标题
   - description: 详细描述
   - related_order_id: 关联订单号（可选）
   - attachments: 附件URL列表（可选）

4. **create_ticket** - 创建工单
   - category: 工单类别
   - priority: 优先级（low/medium/high/urgent）
   - title: 工单标题
   - description: 详细描述

5. **query_aftersale_status** - 查询售后状态
   - aftersale_id: 售后单号（可选，不填查询全部）

## 工具调用格式

当需要使用工具时，请返回 JSON 格式：
```json
{
  "action": "call_tool",
  "tool_name": "工具名称",
  "arguments": {
    "参数名": "参数值"
  }
}
```

当需要向用户询问更多信息时：
```json
{
  "action": "ask_info",
  "question": "需要询问的问题",
  "required_fields": ["需要收集的字段列表"]
}
```

当可以直接回答时：
```json
{
  "action": "respond",
  "response": "回复内容"
}
```

## 售后流程引导

退货流程：
1. 确认订单号和退货商品
2. 了解退货原因
3. 收集问题描述和图片（质量问题时）
4. 提交退货申请
5. 告知用户后续流程

换货流程：
1. 确认订单号和换货商品
2. 了解换货原因
3. 确认是否有库存
4. 提交换货申请

## 注意事项
- 售后申请需要完整信息才能提交
- 对用户的问题要表示理解和歉意
- 复杂投诉建议转人工处理
- 金额较大的退款需要特别确认
"""


async def aftersale_agent(state: AgentState) -> AgentState:
    """Aftersale agent node
    
    Handles returns, exchanges, complaints and aftersale queries.
    
    Args:
        state: Current agent state
        
    Returns:
        Updated state with tool calls or response
    """
    logger.info(
        "Aftersale agent processing",
        conversation_id=state["conversation_id"],
        sub_intent=state.get("sub_intent")
    )
    
    state["current_agent"] = "aftersale"
    state["agent_history"].append("aftersale")
    state["state"] = ConversationState.PROCESSING.value
    
    # Check if we have tool results to process
    if state["tool_results"]:
        return await _generate_aftersale_response(state)
    
    # Build messages for LLM
    messages = [
        Message(role="system", content=AFTERSALE_AGENT_PROMPT),
    ]
    
    # Add conversation history
    for msg in state["messages"][-8:]:  # More history for aftersale context
        messages.append(Message(role=msg["role"], content=msg["content"]))
    
    # Build context info
    context_info = f"用户ID: {state['user_id']}\n账户ID: {state['account_id']}\n"
    
    if state["entities"]:
        context_info += f"已提取的信息: {json.dumps(state['entities'], ensure_ascii=False)}\n"
    
    if state["context"]:
        context_info += f"会话上下文: {json.dumps(state['context'], ensure_ascii=False)}\n"
    
    user_content = f"{context_info}\n用户消息: {state['current_message']}"
    messages.append(Message(role="user", content=user_content))
    
    try:
        llm = get_llm_client()
        response = await llm.chat(messages, temperature=0.5)
        
        # Parse response
        content = response.content.strip()
        if content.startswith("```"):
            content = content.split("```")[1]
            if content.startswith("json"):
                content = content[4:]
        
        result = json.loads(content)
        action = result.get("action")
        
        if action == "call_tool":
            arguments = result.get("arguments", {})
            arguments["user_id"] = state["user_id"]
            
            # Use entity if available
            if "order_id" not in arguments and state["entities"].get("order_id"):
                arguments["order_id"] = state["entities"]["order_id"]
            
            state = add_tool_call(
                state,
                tool_name=result["tool_name"],
                arguments=arguments,
                server="aftersale"
            )
            state["state"] = ConversationState.TOOL_CALLING.value
            
        elif action == "ask_info":
            state = set_response(state, result["question"])
            state["state"] = ConversationState.AWAITING_INFO.value
            # Store required fields in context for next iteration
            if result.get("required_fields"):
                state = update_context(state, {"required_fields": result["required_fields"]})
            
        elif action == "respond":
            state = set_response(state, result["response"])
            state["state"] = ConversationState.GENERATING.value
            
        elif action == "handoff":
            state["requires_human"] = True
            state["handoff_reason"] = result.get("reason", "Complex aftersale issue")
            
        return state
        
    except json.JSONDecodeError:
        state = set_response(state, response.content)
        return state
        
    except Exception as e:
        logger.error("Aftersale agent failed", error=str(e))
        state["error"] = str(e)
        return state


async def _generate_aftersale_response(state: AgentState) -> AgentState:
    """Generate response based on aftersale tool results"""
    
    tool_context = []
    for result in state["tool_results"]:
        if result["success"]:
            data = result["data"]
            tool_context.append(f"工具 {result['tool_name']} 返回:\n{json.dumps(data, ensure_ascii=False, indent=2)}")
            
            # Extract aftersale_id for context
            if isinstance(data, dict) and data.get("aftersale_id"):
                state = update_context(state, {"aftersale_id": data["aftersale_id"]})
        else:
            tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
    
    prompt = f"""基于以下售后系统返回的信息，生成对用户的回复。

用户问题: {state["current_message"]}

系统返回信息:
{chr(10).join(tool_context)}

请生成一个体贴、专业的回复：
- 如果是申请提交成功，告知用户售后单号和后续流程
- 如果是状态查询，清晰说明当前进度
- 如果申请失败，说明原因并提供解决方案
- 对用户的问题表示理解

只返回回复内容，不要返回 JSON。"""
    
    messages = [
        Message(role="system", content="你是一个专业的售后客服助手，请根据系统返回的信息回答用户的售后问题。"),
        Message(role="user", content=prompt)
    ]
    
    try:
        llm = get_llm_client()
        response = await llm.chat(messages, temperature=0.7)
        state = set_response(state, response.content)
        return state
        
    except Exception as e:
        logger.error("Aftersale response generation failed", error=str(e))
        state = set_response(state, "抱歉，处理售后请求时遇到问题。请稍后重试或联系人工客服。")
        return state