Delete app.py

app.py

@@ -1,162 +0,0 @@
-import os
-import pymssql
-import pandas as pd
-
-# os.environ["OPENAI_API_KEY"] = "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"
-os.environ["OPENAI_API_KEY"] = "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"
-
-from langchain.vectorstores import Chroma
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import RetrievalQA
-from langchain.document_loaders import TextLoader
-from langchain.document_loaders import DirectoryLoader
-from langchain.document_loaders import CSVLoader
-from langchain.memory import ConversationBufferMemory
-
-# # 사용자가 질문한 내역 저장해서 나중에 학습용으로 쓰기 위해 DB 접속
-# # MSSQL 접속
-# conn = pymssql.connect(host=r"(local)", database='Chatbot_Manage')
-# conn.autocommit(True) # 오토 커밋 활성화
-# # Connection 으로부터 Cursor 생성
-# _cursor = conn.cursor()
-# _cursorConfig = conn.cursor()
-
-# # _query = "SELECT UserInput as query, SystemAnswer as answer FROM ChatHistory WHERE AcceptFlag = 'Y'"
-# _queryConfig = "SELECT Temperature FROM ChatConfig WHERE IsUse = 1"
-
-# # _cursor.execute(_query)
-# _cursorConfig.execute(_queryConfig)
-
-
-# #실행한 값, 이름 값을 DataFrame에 저장
-# #dfsql = ['query','answer'] #데이터프레임 컬럼에 이름 설정.
-# # _row = cursor.fetchall()
-# # df1 = pd.DataFrame(_row, columns=dfsql)
-
-# _rowConfing = _cursorConfig.fetchone()
-
-# while _rowConfing:
-#     for col in range(len(_rowConfing)):
-#         temperature = _rowConfing[col]
-#         _rowConfing = _cursorConfig.fetchone()
-
-# conn.close()    ## 연결 끊기
-
-persist_directory = 'realdb_LLM'
-
-embedding = OpenAIEmbeddings()
-
-vectordb = Chroma(
-    persist_directory=persist_directory, 
-    embedding_function=embedding
-)
-
-retriever = vectordb.as_retriever(search_kwargs={"k": 1})
-
-
-def process_llm_response(llm_response):
-    print(llm_response['result'])
-    print('\n\nSources:')
-    for source in llm_response["source_documents"]:
-        print(source.metadata['source'])
-        
-        
-# 챗봇의 답변을 처리하는 함수
-def respond(message, chat_history, temperature):
-    
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=OpenAI(temperature=0.4),
-        # llm=OpenAI(temperature=0.4), 
-        # llm=ChatOpenAI(temperature=0),
-        chain_type="stuff", 
-        retriever=retriever
-    )
-
-    result = qa_chain(message)
-    
-    bot_message = result['result']
-
-    # bot_message += '\n\n' + ' [출처]'
-
-    # # 답변의 출처를 표기
-    # for i, doc in enumerate(result['source_documents']):
-    #     bot_message += str(i+1) + '. ' + doc.metadata['source'] + ' '
-
-    # 채팅 기록에 사용자의 메시지와 봇의 응답을 추가.
-    chat_history.append((message, bot_message))
-    
-    historySave(message=message, answer=str(result['result']).replace("'",""))
-    # historySave(message=message, answer="")
-
-    return "", chat_history
-
-def historySave(message, answer):
-
-    conn = pymssql.connect(host=r"(local)", database='Chatbot_Manage', charset='utf8')
-    conn.autocommit(True) # 오토 커밋 활성화
-    # Connection 으로부터 Cursor 생성
-    cursor = conn.cursor()
-
-    SystemType = "OpenAI(Real LLM)"
-    
-    # SQL문 실행'
-    _sql = "EXEC ChatHistory_InsUpd '" + SystemType + "','" + message + "', '" + answer + "'"
-    cursor.execute(_sql)
-    
-    conn.close()    ## 연결 끊기
-    
-import gradio as gr
-
-# 챗봇 설명
-title = """
-<div style="text-align: center; max-width: 500px; margin: 0 auto;">
-    <div>
-        <h1>Pretraining Chatbot V2 Real</h1>
-    </div>
-    <p style="margin-bottom: 10px; font-size: 94%">
-        OpenAI LLM를 이용한 Chatbot (Similarity)
-    </p>
-</div>
-"""
-
-# 꾸미기
-css="""
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-"""
-with gr.Blocks(css=css) as UnivChatbot:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(title)
-
-        # with gr.Row():
-        #     with gr.Column(scale=3):
-        #         openai_key = gr.Textbox(label="You OpenAI API key", type="password", placeholder="OpenAI Key Type", elem_id="InputKey", show_label=False, container=False)
-        #     with gr.Column(scale=1):
-        #         langchain_status = gr.Textbox(placeholder="Status", interactive=False, show_label=False, container=False)
-        #     with gr.Column(scale=1):
-        #         chk_key = gr.Button("확인", variant="primary")
-                
-        chatbot = gr.Chatbot(label="대학 챗봇시스템(OpenAI LLM)", elem_id="chatbot") # 상단 좌측 
-
-        with gr.Row():
-            with gr.Column(scale=9):
-                msg = gr.Textbox(label="입력", placeholder="궁금하신 내역을 입력하여 주세요.", elem_id="InputQuery", show_label=False, container=False)
-            
-        with gr.Row():
-            with gr.Column(scale=1):
-                submit = gr.Button("전송", variant="primary")
-            with gr.Column(scale=1):
-                clear = gr.Button("초기화", variant="stop")
-
-    # 사용자의 입력을 제출(submit)하면 respond 함수가 호출.
-    msg.submit(respond, [msg, chatbot], [msg, chatbot])
-
-    submit.click(respond, [msg, chatbot], [msg, chatbot])
-
-    # '초기화' 버튼을 클릭하면 채팅 기록을 초기화.
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-  
-UnivChatbot.launch(server_port=60001)