Upload app.py

app.py

@@ -1,7 +1,162 @@
+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
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+  
+UnivChatbot.launch(server_port=60001)