71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
import os
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from langchain.chains import LLMChain
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from langchain.chat_models import ChatOpenAI
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from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, \
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PromptTemplate
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from app.core.config import OPENAI_MODEL, OPENAI_API_KEY
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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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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 in English or numbers, check for grammatical errors. If there are no errors, output the input directly.
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If there are grammatical errors, correct them and then output the sentence."""
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)
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system_message_prompt = SystemMessagePromptTemplate.from_template(template)
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# 待翻译文本由 Human 角色输入
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human_template = "User input : {text}"
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human_message_prompt = HumanMessagePromptTemplate.from_template(input_variables=["text"], template=human_template)
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# 使用 System 和 Human 角色的提示模板构造 ChatPromptTemplate
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chat_prompt_template = ChatPromptTemplate.from_messages(
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[system_message_prompt, human_message_prompt]
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)
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translate_chain = LLMChain(llm=llm, prompt=chat_prompt_template)
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template = (
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"""
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Input sentence:
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{translate}
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1. Based on the input,adjust the input sentence to make it more suitable for prompts for generating images,
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ensuring all key nouns or adjectives related to the image are retained.
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2. Simplify complex sentence structures and clarify ambiguous expressions.
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3. Only Output the adjusted English sentence.
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Output :
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"""
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)
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# "Based on the input sentence, extract key adjectives and nouns.Only Output extracted key words."
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# 1. Check if the input sentence contains any grammatical errors. If there are errors, please correct them before proceeding.
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prompt_template = PromptTemplate(input_variables=["translate"], template=template)
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prompt_chain = LLMChain(llm=llm, prompt=prompt_template)
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from langchain.chains import SimpleSequentialChain
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overall_chain = SimpleSequentialChain(chains=[translate_chain, prompt_chain], verbose=True)
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response = overall_chain.run(text)
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return response
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def main():
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"""Main function"""
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translate_to_en("生成一件运动风格的夹克，带有拉链和口袋，适合休闲穿着")
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if __name__ == "__main__":
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main()
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