import os
from openai import OpenAI
import time
import random


class AI_Agent():
    """AI代理类，负责与AI模型交互生成文本内容"""
    
    def __init__(self, base_url, model_name, api, timeout=10, max_retries=3):
        self.url_list = {
            "ali": "https://dashscope.aliyuncs.com/compatible-mode/v1",
            "kimi": "https://api.moonshot.cn/v1",
            "doubao": "https://ark.cn-beijing.volces.com/api/v3/",
            "deepseek": "https://api.deepseek.com",
            "vllm": "http://localhost:8000/v1",
        }
        
        self.base_url = self.url_list[base_url] if base_url in self.url_list else base_url
        self.api = api
        self.model_name = model_name
        self.timeout = timeout  # 设置超时时间（秒）
        self.max_retries = max_retries  # 最大重试次数
        
        self.client = OpenAI(
            api_key=self.api,
            base_url=self.base_url,
            timeout=self.timeout  # 设置OpenAI客户端超时
        )
        
    def generate_text(self, system_prompt, user_prompt, temperature, top_p, presence_penalty):
        """生成文本内容，并返回完整响应和token估计值"""
        print("系统提示词:")
        print(system_prompt)
        print("\n用户提示词:")
        print(user_prompt)
        print(f"\nAPI Key: {self.api}")
        print(f"Base URL: {self.base_url}")
        print(f"Model: {self.model_name}")
        time.sleep(random.random())
        retry_count = 0
        max_retry_wait = 10  # 最大重试等待时间（秒）
        
        while retry_count <= self.max_retries:
            try:
                response = self.client.chat.completions.create(
                    model=self.model_name,
                    messages=[{"role": "system", "content": system_prompt}, 
                             {"role": "user", "content": user_prompt}],
                    temperature=temperature, 
                    top_p=top_p,
                    presence_penalty=presence_penalty,
                    stream=True,
                    max_tokens=8192,
                    timeout=self.timeout,  # 设置请求超时
                    extra_body={
                        "repetition_penalty": 1.05,
                    },
                )
                
                # 收集完整的输出内容
                full_response = ""
                try:
                    for chunk in response:
                        if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
                            content = chunk.choices[0].delta.content
                            full_response += content
                            print(content, end="", flush=True)
                        if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].finish_reason == "stop":
                            break
                    
                    # 成功完成，跳出重试循环
                    break
                    
                except:
                    # 处理流式响应中的超时
                    print(f"\n接收响应时超时")
                    if len(full_response) > 0:
                        print(f"已接收部分响应({len(full_response)}字符)")
                        # 如果已接收足够内容，可以考虑使用已有内容
                        if len(full_response) > 100:  # 假设至少需要100个字符才有意义
                            print("使用已接收的部分内容继续处理")
                            break
                    
                    # 否则准备重试
                    retry_count += 1
                    if retry_count <= self.max_retries:
                        wait_time = min(2 ** retry_count + random.random(), max_retry_wait)  # 指数退避
                        print(f"\n等待 {wait_time:.2f} 秒后重试({retry_count}/{self.max_retries})...")
                        time.sleep(wait_time)
                    continue
                
            except Exception as e:
                print(f"\n请求发生错误: {e}")
                retry_count += 1
                if retry_count <= self.max_retries:
                    wait_time = min(2 ** retry_count + random.random(), max_retry_wait)  # 指数退避
                    print(f"\n等待 {wait_time:.2f} 秒后重试({retry_count}/{self.max_retries})...")
                    time.sleep(wait_time)
                else:
                    print(f"已达到最大重试次数({self.max_retries})，放弃请求")
                    return "请求失败，无法生成内容。", 0
        
        print("\n完成生成，正在处理结果...")
        
        # 由于使用流式输出，无法获取真实的token计数，因此返回估计值
        estimated_tokens = len(full_response.split()) * 1.3  # 简单估算token数量
        
        return full_response, estimated_tokens

    def read_folder(self, file_folder):
        """读取指定文件夹下的所有文件内容"""
        if not os.path.exists(file_folder):
            return ""
            
        context = ""
        for file in os.listdir(file_folder):
            file_path = os.path.join(file_folder, file)
            if os.path.isfile(file_path):
                with open(file_path, "r", encoding="utf-8") as f:
                    context += f"文件名: {file}\n"
                    for line in f.readlines():
                        context += line
                    context += "\n"
        return context
    
    def work(self, system_prompt, user_prompt, file_folder, temperature, top_p, presence_penalty):
        """完整的工作流程：生成文本并返回结果"""
        # 生成时间戳
        date_time = time.strftime("%Y-%m-%d_%H-%M-%S")
        result_file = f"/root/autodl-tmp/xhsTweetGene/result/{date_time}.md"
        
        # 如果提供了参考文件夹，则读取其内容
        if file_folder:
            context = self.read_folder(file_folder)
            if context:
                user_prompt = f"{user_prompt}\n参考资料:\n{context}"
        
        # 计时
        time_start = time.time()
        
        # 生成文本
        result, tokens = self.generate_text(system_prompt, user_prompt, temperature, top_p, presence_penalty)
        
        # 计算耗时
        time_end = time.time()
        time_cost = time_end - time_start
        
        return result, system_prompt, user_prompt, file_folder, result_file, tokens, time_cost 
    
    def close(self):
        self.client.close()
        ## del self.client
        del self