from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage
from app.config import settings
from app.monitoring import monitor_agent
from app.prompt_service import get_prompt
import json
import logging

logger = logging.getLogger(__name__)

SYSTEM_PROMPT = """你是一位家庭教育出题助手。根据给定的文章内容，为读者生成 3 道单项选择题。

要求：
1. 每题 4 个选项，选项以 "A. "、"B. "、"C. "、"D. " 开头
2. 题目考察文章核心内容的理解，适合青少年读者
3. 每题标注所属五维（body/mind/wisdom/action/wealth），缺省为 wisdom
4. 以 JSON 数组格式返回，不要返回其他文字，只返回 JSON 数组

输出格式：
[
  {"question": "问题", "options": ["A. xxx", "B. xxx", "C. xxx", "D. xxx"], "answer": "A", "dimension": "wisdom", "explanation": "解析"}
]
"""


class ArticleQuizAgent:
    def __init__(self):
        self.llm = ChatOpenAI(
            model=settings.llm_model,
            api_key=settings.llm_api_key,
            base_url=settings.llm_base_url,
            temperature=settings.llm_temperature,
        )

    @monitor_agent("article_quiz")
    async def run(self, article_title: str, article_content: str, profile_summary: str = "") -> dict:
        """根据文章内容生成 3 道选择题，失败返回空 questions"""
        try:
            human = f"文章标题：{article_title}\n文章内容：{article_content}"
            if profile_summary:
                human += f"\n用户画像摘要：{profile_summary}"
            messages = [
                SystemMessage(content=await get_prompt("article_quiz") or SYSTEM_PROMPT),
                HumanMessage(content=human),
            ]
            response = await self.llm.ainvoke(messages)
            content = response.content.strip()
            if "```json" in content:
                content = content.split("```json")[1].split("```")[0].strip()
            elif "```" in content:
                content = content.split("```")[1].split("```")[0].strip()
            questions = json.loads(content)
            if not isinstance(questions, list):
                return {"questions": [], "fallback_used": True}
            return {"questions": questions, "fallback_used": False}
        except Exception as e:
            logger.warning("文章出题生成失败: %s", e)
            return {"questions": [], "fallback_used": True}