import json
from http import HTTPStatus

import dashscope
from dashscope import Generation
from openai import OpenAI

def qwen(query):
    client = OpenAI(
        api_key="sk-39b39862ebfb4735aae411cdaa4b99dd",
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
        messages=[
            {'role': 'system', 'content': "您是虚假新闻检测任务的助理。你需要检测给定的新闻是否正确，并根据你所知道的情况生成你的判断解释。"},
            {'role': 'user', 'content': query}],
    )
    result = json.loads(completion.model_dump_json())
    # print(result)
    return result['choices'][0]['message']['content']
    # print(completion.model_dump_json()['choices'][0]['message']['content'])


def llama(query):
    messages = [{'role': 'system', 'content': "您是虚假新闻检测任务的助理。你需要检测给定的新闻是否正确，并根据你所知道的情况生成你的判断解释。"},
                {'role': 'user', 'content': query}]
    response = dashscope.Generation.call(
        api_key="sk-39b39862ebfb4735aae411cdaa4b99dd",
        model='llama3.3-70b-instruct',
        messages=messages,
        result_format='message',  # set the result to be "message" format.
    )
    if response.status_code == HTTPStatus.OK:
        # print(response)

        return response['output']['choices'][0]['message']['content']
    else:
        return ('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
            response.request_id, response.status_code,
            response.code, response.message
        ))


def glm(query):
    messages = [
        {'role': 'system', 'content': "您是虚假新闻检测任务的助理。你需要检测给定的新闻是否正确，并根据你所知道的情况生成你的判断解释。"},
        {'role': 'user', 'content': query}]
    gen = Generation()
    response = gen.call(
        api_key="sk-39b39862ebfb4735aae411cdaa4b99dd",
        model='chatglm-6b-v2',
        messages=messages,
        result_format='message',
    )
    result = response['output']['choices'][0]['message']['content']
    return result


def doubao(query):
    client = OpenAI(
        api_key="272b1003-3823-4723-834d-c004e9072e2f",
        base_url="https://ark.cn-beijing.volces.com/api/v3",
    )
    completion = client.chat.completions.create(
        model="ep-20250111205740-qcbs7",  # your model endpoint ID
        messages=[
            {"role": "system", "content": "您是虚假新闻检测任务的助理。你需要检测给定的新闻是否正确，并根据你所知道的情况生成你的判断解释。"},
            {"role": "user", "content": query},
        ],
    )
    return completion.choices[0].message.content


def deepseek(query):
    client = OpenAI(api_key="sk-f138d39ff70c49409e69f30d2fc48d44", base_url="https://api.deepseek.com")
    response = client.chat.completions.create(
        model="deepseek-chat",
        messages=[
            {"role": "system", "content": "您是虚假新闻检测任务的助理。你需要检测给定的新闻是否正确，并根据你所知道的情况生成你的判断解释。"},
            {"role": "user", "content": query},
        ],
        stream=False
    )
    return response.choices[0].message.content


def baichuan(query):
    messages = [{'role': 'system', 'content': "您是虚假新闻检测任务的助理。你需要检测给定的新闻是否正确，并根据你所知道的情况生成你的判断解释。"},
                {'role': 'user', 'content': query}]
    response = dashscope.Generation.call(
        model='baichuan2-7b-chat-v1',
        api_key="sk-39b39862ebfb4735aae411cdaa4b99dd",
        messages=messages,
        result_format='message',  # set the result to be "message" format.
    )
    if response.status_code == HTTPStatus.OK:
        return response['output']['choices'][0]['message']['content']
    else:
        return ('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
            response.request_id, response.status_code,
            response.code, response.message
        ))

# print(qwen("9月19日，马来西亚最高元首 Ibrahim 应邀对中国进行为期8天国事访问，亦是2024年1月上任以来首次访问东盟外国家。"))