import os

from openai import OpenAI


def openAIChatLLM(model_name=None, api_key=None, base_url=None):
    """
    model_name 取值
    - deepseek-chat
    """
    api_key = os.environ.get("OPENAI_API_KEY", api_key)
    base_url = os.environ.get("OPENAI_BASE_URL", base_url)
    model_name = os.environ.get("OPENAI_API_MODEL", model_name)
    client = OpenAI(api_key=api_key, base_url=base_url)

    def chatLLM(
        messages: list,
        temperature=None,
        top_p=None,
        max_tokens=None,
        stream=False,
        model=model_name, #可使用自定义模型
    ) -> dict:
        if not stream:
            response = client.chat.completions.create(
                #model=model_name,
                model=model,
                messages=messages,
                temperature=temperature,
                top_p=top_p,
                max_tokens=max_tokens,
            )
            return {
                "content": response.choices[0].message.content,
                "total_tokens": response.usage.total_tokens,
            }
        else:
            responses = client.chat.completions.create(
                #model=model_name,
                model=model,
                messages=messages,
                temperature=temperature,
                top_p=top_p,
                max_tokens=max_tokens,
                stream=True,
            )

            def respGenerator():
                content = ""
                for response in responses:
                    delta = response.choices[0].delta.content

                    #判断内容时候为空，非空才添加
                    if delta is not None:
                        content += delta
                    #content += delta

                    # if response.usage:
                    #     total_tokens = response.usage.total_tokens
                    # else:
                    total_tokens = None

                    yield {
                        "content": content,
                        "total_tokens": total_tokens,
                    }

            return respGenerator()

    return chatLLM