#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
选题生成模块
"""

import logging
import json
import re
from typing import List, Dict, Any, Optional
from json_repair import loads as json_repair_loads

from core.ai import AIAgent
from core.config import GenerateTopicConfig, ConfigManager
from utils.prompts import TopicPromptBuilder
from utils.file_io import OutputManager
from .topic_parser import TopicParser

logger = logging.getLogger(__name__)


class TopicGenerator:
    """
    负责生成、解析和保存主题
    """

    def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager):
        """
        初始化主题生成器

        Args:
            ai_agent: AI Agent
            config_manager: 配置管理器
            output_manager: 输出管理器
        """
        self.ai_agent = ai_agent
        self.config: GenerateTopicConfig = config_manager.get_config('topic_gen', GenerateTopicConfig)
        self.output_manager = output_manager
        self.parser = TopicParser()
        self.prompt_builder = TopicPromptBuilder(config_manager)
        logger.info(f"选题生成配置: {self.config}")
    async def generate_topics(self) -> Optional[List[Dict[str, Any]]]:
        """
        执行完整的选题生成流程：构建提示 -> 调用AI -> 解析结果 -> 保存产物
        """
        logger.info("开始执行选题生成流程...")

        # 1. 构建提示
        system_prompt = self.prompt_builder.get_system_prompt()
        user_prompt = self.prompt_builder.build_user_prompt(
            num_topics=self.config.topic.num,
            month=self.config.topic.date
        )
        self.output_manager.save_text(system_prompt, "topic_system_prompt.txt")
        self.output_manager.save_text(user_prompt, "topic_user_prompt.txt")
        
        # 获取模型参数
        model_params = {}
        if hasattr(self.config, 'model') and isinstance(self.config.model, dict):
            model_params = {
                'temperature': self.config.model.get('temperature'),
                'top_p': self.config.model.get('top_p'),
                'presence_penalty': self.config.model.get('presence_penalty')
            }
            # 移除None值
            model_params = {k: v for k, v in model_params.items() if v is not None}
        
        # 2. 调用AI生成
        try:
            raw_result, _, _, _ = await self.ai_agent.generate_text(
                system_prompt=system_prompt,
                user_prompt=user_prompt,
                use_stream=False, # 选题生成通常不需要流式输出
                stage="选题生成",
                **model_params
            )
            self.output_manager.save_text(raw_result, "topics_raw_response.txt")
        except Exception as e:
            logger.critical(f"AI调用失败，无法生成选题: {e}", exc_info=True)
            return None

        # 3. 解析结果
        topics = self.parser.parse(raw_result)
        if not topics:
            logger.error("未能从AI响应中解析出任何有效选题")
            return None
            
        # 4. 保存解析后的结果
        self.output_manager.save_json(topics, "topics_generated.json")
        
        # 5. （可选）保存为易于阅读的 .txt 格式
        topics_text = self._format_topics_to_text(topics)
        self.output_manager.save_text(topics_text, "topics_generated.txt")
        
        logger.info(f"选题生成流程成功完成，共生成 {len(topics)} 个选题。")
        return topics

    def _format_topics_to_text(self, topics: List[Dict[str, Any]]) -> str:
        """将选题列表格式化为人类可读的文本"""
        text_parts = [f"# 选题列表 (Run ID: {self.output_manager.run_id})\n"]
        for topic in topics:
            text_parts.append(f"## 选题 {topic.get('index', 'N/A')}")
            text_parts.append(f"- 日期: {topic.get('date', 'N/A')}")
            text_parts.append(f"- 对象: {topic.get('object', 'N/A')}")
            text_parts.append(f"- 产品: {topic.get('product', 'N/A')}")
            text_parts.append(f"- 风格: {topic.get('style', 'N/A')}")
            text_parts.append(f"- 目标受众: {topic.get('target_audience', 'N/A')}")
            text_parts.append(f"- 逻辑: {topic.get('logic', 'N/A')}")
        return "\n".join(text_parts)