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from openai import OpenAI
 
prompt_dict = {
    'kimi': [ {"role": "system", "content": "你是 Kimi,由 Moonshot AI 提供的人工智能助手,你更擅长中文和英文的对话。"},
              {"role": "user", "content": "你好,请注意你现在生成的文字要按照人日常生活的口吻,你的回复将会后续用TTS模型转为语音,并且请把回答控制在100字以内。并且标点符号仅包含逗号和句号,将数字等转为文字回答。"},
              {"role": "assistant", "content": "好的,我现在生成的文字将按照人日常生活的口吻, 并且我会把回答控制在一百字以内, 标点符号仅包含逗号和句号,将阿拉伯数字等转为中文文字回答。下面请开始对话。"},],
    'deepseek': [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "你好,请注意你现在生成的文字要按照人日常生活的口吻,你的回复将会后续用TTS模型转为语音,并且请把回答控制在100字以内。并且标点符号仅包含逗号和句号,将数字等转为文字回答。"},
        {"role": "assistant", "content": "好的,我现在生成的文字将按照人日常生活的口吻, 并且我会把回答控制在一百字以内, 标点符号仅包含逗号和句号,将阿拉伯数字等转为中文文字回答。下面请开始对话。"},],
    'deepseek_TN': [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "你好,现在我们在处理TTS的文本输入,下面将会给你输入一段文本,请你将其中的阿拉伯数字等等转为文字表达,并且输出的文本里仅包含逗号和句号这两个标点符号"},
        {"role": "assistant", "content": "好的,我现在对TTS的文本输入进行处理。这一般叫做text normalization。下面请输入"},
        {"role": "user", "content": "We paid $123 for this desk."},
        {"role": "assistant", "content": "We paid one hundred and twenty three dollars for this desk."},
        {"role": "user", "content": "详询请拨打010-724654"},
        {"role": "assistant", "content": "详询请拨打零幺零,七二四六五四"},
        {"role": "user", "content": "罗森宣布将于7月24日退市,在华门店超6000家!"},
        {"role": "assistant", "content": "罗森宣布将于七月二十四日退市,在华门店超过六千家。"},
        ],
}          
                
class llm_api:
    def __init__(self, api_key, base_url, model):
        self.client =  OpenAI(
            api_key = api_key,
            base_url = base_url,
        )
        self.model = model
    def call(self, user_question, temperature = 0.3, prompt_version='kimi', **kwargs):
    
        completion = self.client.chat.completions.create(
            model = self.model,
            messages = prompt_dict[prompt_version]+[{"role": "user", "content": user_question},],
            temperature = temperature,
            **kwargs
        )
        return completion.choices[0].message.content