app.py

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)