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app/service/prompt_generation/chatgpt_for_translation_20.1.1.33.py

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+import json
+import logging
+import time
+
+import requests
+from dashscope import Generation
+from requests import RequestException
+from retry import retry
+
+from app.core.config import settings
+from app.service.chat_robot.script.prompt import GET_LANGUAGE_PREFIX
+from app.service.prompt_generation.util import minio_util
+
+logger = logging.getLogger(__name__)
+
+
+def get_assistant_response(messages):
+    response = Generation.call(
+        model='qwen-max',
+        api_key=settings.QWEN_API_KEY,
+        messages=messages,
+        # seed=random.randint(1, 10000),  # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234
+        result_format='message',  # 将输出设置为message形式
+        enable_search='false'
+    )
+    return response
+
+
+def get_language(message: str) -> str:
+    messages = [
+        {
+            "content": GET_LANGUAGE_PREFIX,  # ai message
+            "role": "system"
+        },
+        {
+            "content": "Tree",  # 用户message
+            "role": "user"
+        },
+        {
+            "content": "English",  # 用户message
+            "role": "assistant"
+        },
+        {
+            "content": "玩具",  # 用户message
+            "role": "user"
+        },
+        {
+            "content": "Chinese",  # 用户message
+            "role": "assistant"
+        },
+        {
+            "content": message,  # 用户message
+            "role": "user"
+        }
+    ]
+
+    first_response = get_assistant_response(messages)
+    assistant_output = first_response.output.choices[0].message.content
+    logging.info(f"大模型输出信息：{first_response}\n判断用户输入的语言为：{assistant_output}")
+    # print(f"大模型输出信息：{first_response}\n判断用户输入的语言为：{assistant_output}")
+    return assistant_output
+
+
+@retry(exceptions=RequestException, tries=3, delay=1)
+def get_response(messages):
+    response = Generation.call(
+        model='qwen-turbo',
+        api_key=settings.QWEN_API_KEY,
+        messages=messages,
+        # seed=random.randint(1, 10000),  # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234
+        result_format='message',  # 将输出设置为message形式
+        enable_search='True'
+    )
+    return response
+
+
+def get_translation_from_llama3(text):
+    start_time = time.time()
+    url = f"http://{settings.A6000_SERVICE_HOST}:12434/api/generate"
+    # 先获取用户输入文本的语言
+    language = get_language(text)
+
+    if 'English' in language:
+        return text
+
+    # 创建请求的负载 translator是自定义的翻译模型
+    payload = {
+        "model": "AiDA-translator:latest",
+        "prompt": f"[{text}]",
+        "stream": False
+    }
+    # 将负载转换为 JSON 格式
+    headers = {'Content-Type': 'application/json'}
+    response = requests.post(url, data=json.dumps(payload), headers=headers)
+    # 处理响应
+    if response.status_code == 200:
+        # print("Response from server:")
+        # print(response.json())
+        resp = json.loads(response.content).get("response")
+        logger.info(f"translation server runtime is {time.time() - start_time} , response is {resp}")
+        print("input : {}, translate result : {}".format(text, resp))
+        return resp
+    else:
+        logger.info(f"translation server runtime is {time.time() - start_time} , response is {response.content}")
+        print(f"Request failed with status code {response.status_code}")
+        print(response.text)
+        return ""
+
+
+# 在llama3中创建一个翻译模型
+# def create_model_with_llama(text):
+#     url = "http://localhost:11434/api/create"
+#     # url = "http://20.1.1.43:1143/api/generate"
+#
+#     # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
+#
+#     # 创建翻译器的配置文件
+#     payload = {
+#         "model": "translator",
+#         "modelfile": "FROM llama3\nSYSTEM Translate everything within the brackets [] into English."
+#                      "Never translate or modify any English input."
+#                      "The input must be fully translated into coherent English sentences."
+#     }
+#
+#     # 将负载转换为 JSON 格式
+#     headers = {'Content-Type': 'application/json'}
+#     response = requests.post(url, data=json.dumps(payload), headers=headers)
+
+
+def get_prompt_from_image(image_path, text):
+    start_time = time.time()
+    # url = "http://localhost:11434/api/generate"
+    url = f"http://{settings.B_4_X_4090_SERVICE_HOST}:11434/api/generate"
+
+    image_base64 = minio_util.minio_url_to_base64(image_path.img)
+    # image_base64 = minio_url_to_base64(image_path)
+
+    # 创建请求的负载 translator是自定义的翻译模型
+    payload = {
+        "model": "llama3.2-vision",
+        "images": [image_base64],
+        "prompt": f"{text}",
+        "stream": False
+    }
+    # 将负载转换为 JSON 格式
+    headers = {'Content-Type': 'application/json'}
+    response = requests.post(url, data=json.dumps(payload), headers=headers)
+    # 处理响应
+    if response.status_code == 200:
+        # print("Response from server:")
+        # print(response.json())
+        resp = json.loads(response.content).get("response")
+        logger.info(f"sketch re-generate server runtime is {time.time() - start_time} \n, response is {resp}")
+        # print("input : {}, sketch re-generate result : {}".format(text, resp))
+        return resp
+    else:
+        logger.info(f"sketch re-generate server runtime is {time.time() - start_time} , response is {response.content}")
+        print(f"Request failed with status code {response.status_code}")
+        print(response.text)
+        return ""
+
+
+def main():
+    """Main function"""
+    text = get_translation_from_llama3("[火焰]")
+    print(text)
+
+
+if __name__ == "__main__":
+    main()