Create model.py

model.py

@@ -0,0 +1,105 @@
+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。"))