# -*- coding: utf-8 -*- from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Chroma import openai import time import gradio as gr import os # 输入 API KEY os.environ["OPENAI_API_KEY"] = "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" #读取PDF文件 def doc_read_pdf(file): # 读取PDF reader = PdfReader(file) # reader = PdfReader('.\data\資治通鑑全集_部分1.pdf') raw_text = '' for i, page in enumerate(reader.pages): text = page.extract_text() if text: raw_text += text return raw_text # 读取txt文件 def doc_read_txt(file): with open(file, encoding='utf-8') as f: text = f.read() return text #补全 #从开始到调用openai模型前的一些步骤,主要是文件读取和拆解 def doc_split(file): #分解文本 text_splitter = CharacterTextSplitter( separator = "\n", chunk_size = 1000, chunk_overlap = 100, length_function = len, ) # texts = text_splitter.split_text(raw_text) texts = text_splitter.split_text(doc_read_txt(file)) return texts #将文本向量化 def doc_vectorize(texts): embeddings = OpenAIEmbeddings() docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]) return docsearch #文本被拆解储存在数组texts中 # raw_file = texts = doc_split("./资治通鉴.txt") docsearch = doc_vectorize((texts)) def openai_reply(word1, word2, word3, temp, file): words = word1 + "*****" + word2 + "*****" + word3 # 文本相似查找,最终结果是一个列表 docs = docsearch.similarity_search(words) reference_1 = docs[0].page_content reference_2 = docs[1].page_content reference = reference_1 + reference_2 print(words) request = "请使用文言文帮我补全[" + words + "]" response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": f"""你是一个古汉语与中国历史专家,擅长补全古代文献。在后续的会话中,你需要补全我给你的残缺文本, 这些残缺文本用[]包括,并且其中的几段残缺汉字用“*”来代替着,且每段“*”代表的残缺汉字不少于10个。 请你阅读并且理解下文背景资料,并且补全我待会在会话中给出的残缺文本。如果你在背景资料中找不到相关文字,请根据你对于背景资料和我给出的残缺文本的理解, 使用《资治通鉴》的文言文风格自行补全残缺文本。请注意,不要改动我提供的残缺文本中非“*”的原文,并且一定要用文言文替代“*”!!! \n背景资料:\n{reference} """}, {"role": "user", "content": request}, ], max_tokens=512, n=1, stop=None, temperature=temp ) print(response.choices[0].message['content']) shijian = time.strftime("%Y年%m月%d日%H点%M分",time.localtime()) answer = response.choices[0].message['content'] return answer, reference, shijian # 以下是界面搭建 headline = '碎片化文本复原' description = """请给出至多三条碎片文本,系统将会根据文献数据库尽可能进行理解和匹配,给出猜想。 当然,你也可以上传自己的TXT文件作为数据来源之一。结果仅供参考和启发。""" with gr.Blocks() as demo: gr.Markdown(f'

{headline}

') gr.Markdown(description) with gr.Row(): with gr.Group(): raw_text_1 = gr.Textbox(label='在此输入碎片文本') raw_text_2 = gr.Textbox(label='在此输入碎片文本') raw_text_3 = gr.Textbox(label='在此输入碎片文本') temp = gr.Slider(minimum=0.0, maximum=2.0, value=0.3, label="无序程度(Temperature)") Title = gr.Textbox(label='在此输入提交的材料的标题(无需加《》)') file = gr.File( label='上传你的本地TXT文件', file_types=['.txt'] ) btn = gr.Button(value='提交') btn.style(full_width=True) with gr.Group(): shijian = gr.Label(label='生成时时间') answer = gr.Textbox(label='回答') reference = gr.Textbox(label='参考材料') btn.click( openai_reply, inputs= [raw_text_1, raw_text_2, raw_text_3, temp, file], outputs= [answer, reference, shijian], ) demo.launch() # if __name__ == "__main__": # demo.launch(server_port=7860, share=True)