# -*- coding: utf-8 -*-
"""
LLM调用辅助模块
"""

import asyncio
import yaml
from typing import Optional, Callable, AsyncIterator
from config.llm_config import LLMConfig
from config.app_config import app_config
from utils.fallback_openai_client import AsyncFallbackOpenAIClient
from utils.cache_manager import LLMCacheManager

# 初始化LLM缓存管理器
llm_cache = LLMCacheManager(
    cache_dir=app_config.llm_cache_dir,
    enabled=app_config.llm_cache_enabled
)

class LLMHelper:
    """LLM调用辅助类，支持同步和异步调用"""
    
    def __init__(self, config: LLMConfig = None):
        self.config = config
        self.client = AsyncFallbackOpenAIClient(
            primary_api_key=config.api_key,
            primary_base_url=config.base_url,
            primary_model_name=config.model
        )
    
    async def async_call(self, prompt: str, system_prompt: str = None, max_tokens: int = None, temperature: float = None) -> str:
        """异步调用LLM"""
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": prompt})
        
        kwargs = {}
        if max_tokens is not None:
            kwargs['max_tokens'] = max_tokens
        else:
            kwargs['max_tokens'] = self.config.max_tokens
            
        if temperature is not None:
            kwargs['temperature'] = temperature
        else:
            kwargs['temperature'] = self.config.temperature
            
        try:
            response = await self.client.chat_completions_create(
                messages=messages,
                **kwargs
            )
            return response.choices[0].message.content
        except Exception as e:
            print(f"LLM调用失败: {e}")
            return ""
    
    def call(self, prompt: str, system_prompt: str = None, max_tokens: int = None, temperature: float = None) -> str:
        """同步调用LLM"""
        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            
        import nest_asyncio
        nest_asyncio.apply()
        
        return loop.run_until_complete(self.async_call(prompt, system_prompt, max_tokens, temperature))
    
    def parse_yaml_response(self, response: str) -> dict:
        """解析YAML格式的响应"""
        try:
            # 提取```yaml和```之间的内容
            if '```yaml' in response:
                start = response.find('```yaml') + 7
                end = response.find('```', start)
                yaml_content = response[start:end].strip()
            elif '```' in response:
                start = response.find('```') + 3
                end = response.find('```', start)
                yaml_content = response[start:end].strip()
            else:
                yaml_content = response.strip()
            
            parsed = yaml.safe_load(yaml_content)
            return parsed if parsed is not None else {}
        except Exception as e:
            print(f"YAML解析失败: {e}")
            print(f"原始响应: {response}")
            return {}
    
    
    async def close(self):
        """关闭客户端"""
        await self.client.close()
    
    async def async_call_with_cache(
        self, 
        prompt: str, 
        system_prompt: str = None, 
        max_tokens: int = None, 
        temperature: float = None,
        use_cache: bool = True
    ) -> str:
        """带缓存的异步LLM调用"""
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": prompt})
        
        # 生成缓存键
        cache_key = llm_cache.get_cache_key_from_messages(messages, self.config.model)
        
        # 尝试从缓存获取
        if use_cache and app_config.llm_cache_enabled:
            cached_response = llm_cache.get(cache_key)
            if cached_response:
                print("[CACHE] 使用LLM缓存响应")
                return cached_response
        
        # 调用LLM
        response = await self.async_call(prompt, system_prompt, max_tokens, temperature)
        
        # 缓存响应
        if use_cache and app_config.llm_cache_enabled and response:
            llm_cache.set(cache_key, response)
        
        return response
    
    def call_with_cache(
        self, 
        prompt: str, 
        system_prompt: str = None, 
        max_tokens: int = None, 
        temperature: float = None,
        use_cache: bool = True
    ) -> str:
        """带缓存的同步LLM调用"""
        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            
        import nest_asyncio
        nest_asyncio.apply()
        
        return loop.run_until_complete(
            self.async_call_with_cache(prompt, system_prompt, max_tokens, temperature, use_cache)
        )
    
    async def async_call_stream(
        self, 
        prompt: str, 
        system_prompt: str = None,
        max_tokens: int = None,
        temperature: float = None,
        callback: Optional[Callable[[str], None]] = None
    ) -> AsyncIterator[str]:
        """流式异步LLM调用"""
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": prompt})
        
        kwargs = {
            'stream': True,
            'max_tokens': max_tokens or self.config.max_tokens,
            'temperature': temperature or self.config.temperature
        }
        
        try:
            response = await self.client.chat_completions_create(
                messages=messages,
                **kwargs
            )
            
            full_response = ""
            async for chunk in response:
                if chunk.choices[0].delta.content:
                    content = chunk.choices[0].delta.content
                    full_response += content
                    
                    # 调用回调函数
                    if callback:
                        callback(content)
                    
                    yield content
            
        except Exception as e:
            print(f"流式LLM调用失败: {e}")
            yield ""