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189 lines
6.6 KiB
Python
189 lines
6.6 KiB
Python
import json
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import logging
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import time
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import requests
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from dashscope import Generation
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from requests import RequestException
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from retry import retry
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from app.core.config import settings
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from app.service.chat_robot.script.service.CallQWen import get_language
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from app.service.prompt_generation.util import minio_util
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logger = logging.getLogger(__name__)
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# os.environ["http_proxy"] = "http://127.0.0.1:7890"
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# os.environ["https_proxy"] = "http://127.0.0.1:7890"
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# llm = ChatOpenAI(model_name=OPENAI_MODEL,
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# openai_api_key=OPENAI_API_KEY,
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# temperature=0)
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# prefix_for_llama = (
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# """
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# Translate everything within the brackets [] into English.
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# Never translate or modify any English input.
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# The input must be fully translated into coherent English sentences.
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# Please only output the translated result.\n
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# """
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# )
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def translate_to_en(text):
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# template = (
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# """You are a translation expert, proficient in various languages.
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# And can translate various languages into English.
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# Please translate to grammatically correct English regardless of the input language.
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# If the input is already in English, or consists of letters or numbers such as "cat", "abc", or "1",
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# output the input text exactly as it is without any modifications or additions.
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# If there are grammatical errors, correct them and then output the sentence."""
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# )
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#
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# prefix = (
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# """
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# Translate everything within the brackets [] into English.
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# Never translate or modify any English input.
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# The input must be fully translated into coherent English sentences.
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# Never present the translation results in the format
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# "The translation of \"Material suave\" into English would be \"Smooth material.\"". Instead, directly output "Smooth material".
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# """
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# )
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# messages = [
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# # {
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# # Translate the entire text and ensure the output is a complete and coherent sentence in English.
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# # "content": template, # 系统message
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# # "role": "system"
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# # },
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# {
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# # "content": input('请输入：'), # 用户message
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# "content": text, # 用户message
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# "role": "user"
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# }
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# ]
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first_response = get_response
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assistant_output = first_response.output.choices[0].message
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print("input : {}, translate result : {}".format(text, assistant_output.content))
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return assistant_output.content
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# llama3专用
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# data = get_translation_from_llama3(text)
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# translation = data
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# # print("Response from llama3 : " + translation)
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# return translation
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@retry(exceptions=RequestException, tries=3, delay=1)
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def get_response(messages):
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response = Generation.call(
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model='qwen-turbo',
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api_key=settings.QWEN_API_KEY,
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messages=messages,
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# 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
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result_format='message', # 将输出设置为message形式
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enable_search='True'
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)
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return response
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def get_translation_from_llama3(text):
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start_time = time.time()
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url = f"http://{settings.A6000_SERVICE_HOST}:11434/api/generate"
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# url = "http://10.1.1.240:1143/api/generate"
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# prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
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# 先获取用户输入文本的语言
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language = get_language(text)
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if 'English' in language:
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return text
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# 创建请求的负载 translator是自定义的翻译模型
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payload = {
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"model": "translator",
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"prompt": f"[{text}]",
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"stream": False
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}
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# 将负载转换为 JSON 格式
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headers = {'Content-Type': 'application/json'}
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response = requests.post(url, data=json.dumps(payload), headers=headers)
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# 处理响应
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if response.status_code == 200:
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# print("Response from server:")
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# print(response.json())
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resp = json.loads(response.content).get("response")
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logger.info(f"translation server runtime is {time.time() - start_time} , response is {resp}")
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print("input : {}, translate result : {}".format(text, resp))
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return resp
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else:
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logger.info(f"translation server runtime is {time.time() - start_time} , response is {response.content}")
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print(f"Request failed with status code {response.status_code}")
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print(response.text)
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return ""
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# 在llama3中创建一个翻译模型
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# def create_model_with_llama(text):
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# url = "http://localhost:11434/api/create"
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# # url = "http://10.1.1.240:1143/api/generate"
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#
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# # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
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#
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# # 创建翻译器的配置文件
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# payload = {
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# "model": "translator",
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# "modelfile": "FROM llama3\nSYSTEM Translate everything within the brackets [] into English."
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# "Never translate or modify any English input."
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# "The input must be fully translated into coherent English sentences."
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# }
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#
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# # 将负载转换为 JSON 格式
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# headers = {'Content-Type': 'application/json'}
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# response = requests.post(url, data=json.dumps(payload), headers=headers)
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def get_prompt_from_image(image_path, text):
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start_time = time.time()
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# url = "http://localhost:11434/api/generate"
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url = f"http://{settings.B_4_X_4090_SERVICE_HOST}:11434/api/generate"
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image_base64 = minio_util.minio_url_to_base64(image_path.img)
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# image_base64 = minio_url_to_base64(image_path)
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# 创建请求的负载 translator是自定义的翻译模型
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payload = {
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"model": "llama3.2-vision",
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"images": [image_base64],
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"prompt": f"{text}",
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"stream": False
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}
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# 将负载转换为 JSON 格式
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headers = {'Content-Type': 'application/json'}
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response = requests.post(url, data=json.dumps(payload), headers=headers)
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# 处理响应
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if response.status_code == 200:
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# print("Response from server:")
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# print(response.json())
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resp = json.loads(response.content).get("response")
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logger.info(f"sketch re-generate server runtime is {time.time() - start_time} \n, response is {resp}")
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# print("input : {}, sketch re-generate result : {}".format(text, resp))
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return resp
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else:
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logger.info(f"sketch re-generate server runtime is {time.time() - start_time} , response is {response.content}")
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print(f"Request failed with status code {response.status_code}")
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print(response.text)
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return ""
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def main():
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"""Main function"""
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text = get_translation_from_llama3("[火焰]")
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print(text)
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if __name__ == "__main__":
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main()
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