# from sparkai.llm.llm import ChatSparkLLM, ChunkPrintHandler # from sparkai.core.messages import ChatMessage import pandas as pd nv_prompt_file = pd.read_excel('汉服-女词库.xlsx') na_prompt_file = pd.read_excel('汉服-男词库.xlsx') nv_prompt = nv_prompt_file.to_string(index=False) na_prompt = na_prompt_file.to_string(index=False) # # #星火认知大模型Spark Max的URL值,其他版本大模型URL值请前往文档(https://www.xfyun.cn/doc/spark/Web.html)查看 # SPARKAI_URL = 'wss://spark-api.xf-yun.com/v3.5/chat' # #星火认知大模型调用秘钥信息,请前往讯飞开放平台控制台(https://console.xfyun.cn/services/bm35)查看 # SPARKAI_APP_ID = '11ce2152' # SPARKAI_API_SECRET = 'N2ExOTc3MDc1OWZjMTkyNzFlYjA3ZTAz' # SPARKAI_API_KEY = '4f6313fa6c05dea06e4e18b46e63b20f' # #星火认知大模型Spark Max的domain值,其他版本大模型domain值请前往文档(https://www.xfyun.cn/doc/spark/Web.html)查看 # SPARKAI_DOMAIN = 'generalv3.5' # # def prompt_gen(advise): # spark = ChatSparkLLM( # spark_api_url=SPARKAI_URL, # spark_app_id=SPARKAI_APP_ID, # spark_api_key=SPARKAI_API_KEY, # spark_api_secret=SPARKAI_API_SECRET, # spark_llm_domain=SPARKAI_DOMAIN, # streaming=False, # ) # messages = [ChatMessage( # role="user", # content=advise + "\n根据建议,从触发词、种类、上衣、裙子、领子、袖子、袖口、腰饰、裙子详述中每个挑选一个词,分点描述,把英文也附在后面的括" # "号里,最后下面加一条prompt,总结所有英文描述,用逗号间隔\n" + nv_prompt, # )] # print(messages[0].content) # handler = ChunkPrintHandler() # a = spark.generate([messages], callbacks=[handler]) # print(a.generations[0][0].text) # return a.generations[0][0].text import os os.environ["OPENAI_API_KEY"] = "sk-vtyR3fdgk08jmJ5e3eF6F5Ef663c4a3bAd0166C3549a1a8e" #输入网站发给你的转发key os.environ["OPENAI_BASE_URL"] = "http://15.204.101.64:4000/v1" from openai import OpenAI def prompt_gen(advise, gender): if gender == "男": prompt = na_prompt else: prompt = nv_prompt client = OpenAI() completion = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": "You are a helpful assistant.",}, {"role": "user", "content": advise + "根据建议,从以下的触发词、种类、上衣、裙子、领子、袖子、袖口、腰饰、裙子详述中每个挑选一个词,分点描述," "把英文也附在后面的括号里,最后下面加一条prompt,总结所有英文描述,用逗号间隔" + prompt, } ] ) print(completion.choices[0].message.content) return completion.choices[0].message.content