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

"""
内容生成模块
"""

import logging
import json
from typing import Dict, Any, Tuple, Optional

from core.ai import AIAgent
from core.config import ConfigManager, GenerateTopicConfig, GenerateContentConfig
from utils.prompts import ContentPromptBuilder
from utils.file_io import OutputManager, process_llm_json_text

logger = logging.getLogger(__name__)


class ContentGenerator:
    """负责为单个选题生成内容"""

    def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager):
        self.ai_agent = ai_agent
        self.config_manager = config_manager
        self.topic_config = config_manager.get_config('topic_gen', GenerateTopicConfig)
        self.content_config = config_manager.get_config('content_gen', GenerateContentConfig)
        self.output_manager = output_manager
        self.prompt_builder = ContentPromptBuilder(config_manager)

    async def generate_content_for_topic(self, topic: Dict[str, Any]) -> Dict[str, Any]:
        """
        为单个选题生成内容

        Args:
            topic: 选题信息字典

        Returns:
            包含生成内容的字典
        """
        topic_index = topic.get('index', 'N/A')
        logger.info(f"开始为选题 {topic_index} 生成内容...")

        # 1. 构建提示
        # 使用模板构建器分别获取系统和用户提示
        system_prompt = self.prompt_builder.get_system_prompt()
        user_prompt = self.prompt_builder.build_user_prompt(topic=topic)
        
        # 保存提示以供调试
        output_dir = self.output_manager.get_topic_dir(topic_index)
        self.output_manager.save_text(system_prompt, "content_system_prompt.txt", subdir=output_dir.name)
        self.output_manager.save_text(user_prompt, "content_user_prompt.txt", subdir=output_dir.name)

        # 获取模型参数
        model_params = {}
        if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):
            model_params = {
                'temperature': self.content_config.model.get('temperature'),
                'top_p': self.content_config.model.get('top_p'),
                'presence_penalty': self.content_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
        try:
            raw_result, _, _, _ = await self.ai_agent.generate_text(
                system_prompt=system_prompt,
                user_prompt=user_prompt,
                use_stream=True,
                stage="内容生成",
                **model_params
            )
            self.output_manager.save_text(raw_result, "content_raw_response.txt", subdir=output_dir.name)
        except Exception as e:
            logger.critical(f"为选题 {topic_index} 生成内容时AI调用失败: {e}", exc_info=True)
            return {"error": str(e)}

        # 3. 解析和保存结果
        content_data = process_llm_json_text(raw_result)
        if content_data:
            self.output_manager.save_json(content_data, "article.json", subdir=output_dir.name)
            logger.info(f"成功为选题 {topic_index} 生成并保存内容。")
            return content_data
        else:
            logger.error(f"解析内容JSON失败 for {topic_index}")
            return {"error": "JSONDecodeError", "raw_content": raw_result}
            
    async def generate_content_with_prompt(self, topic: Dict[str, Any], system_prompt: str, user_prompt: str) -> Dict[str, Any]:
        """
        使用已构建的提示词生成内容

        Args:
            topic: 选题信息字典
            system_prompt: 已构建好的系统提示词
            user_prompt: 已构建好的用户提示词

        Returns:
            包含生成内容的字典
        """
        topic_index = topic.get('index', 'N/A')
        logger.info(f"使用预构建提示词为选题 {topic_index} 生成内容...")
        
        # 保存提示以供调试
        output_dir = self.output_manager.get_topic_dir(topic_index)
        self.output_manager.save_text(system_prompt, "content_system_prompt.txt", subdir=output_dir.name)
        self.output_manager.save_text(user_prompt, "content_user_prompt.txt", subdir=output_dir.name)

        # 获取模型参数
        model_params = {}
        if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):
            model_params = {
                'temperature': self.content_config.model.get('temperature'),
                'top_p': self.content_config.model.get('top_p'),
                'presence_penalty': self.content_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}

        # 调用AI
        try:
            raw_result, _, _, _ = await self.ai_agent.generate_text(
                system_prompt=system_prompt,
                user_prompt=user_prompt,
                use_stream=True,
                stage="内容生成",
                **model_params
            )
            self.output_manager.save_text(raw_result, "content_raw_response.txt", subdir=output_dir.name)
        except Exception as e:
            logger.critical(f"为选题 {topic_index} 生成内容时AI调用失败: {e}", exc_info=True)
            return {"error": str(e)}

        # 解析和保存结果
        content_data = process_llm_json_text(raw_result)
        if content_data:
            self.output_manager.save_json(content_data, "article.json", subdir=output_dir.name)
            logger.info(f"成功为选题 {topic_index} 生成并保存内容。")
            return content_data
        else:
            logger.error(f"解析内容JSON失败 for {topic_index}")
            return {"error": "JSONDecodeError", "raw_content": raw_result}