Upload app.py

app.py

@@ -0,0 +1,135 @@
+import os
+import pymssql
+import pandas as pd
+
+
+
+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
+
+def Loading():
+    return "데이터 로딩 중..."
+
+def LoadData(openai_key):
+    
+    if openai_key is not None:
+        
+        persist_directory = 'realdb_LLM'
+
+        embedding = OpenAIEmbeddings()
+
+        vectordb = Chroma(
+            persist_directory=persist_directory, 
+            embedding_function=embedding
+        )
+
+        global retriever
+        retriever = vectordb.as_retriever(search_kwargs={"k": 1})
+        
+        return "준비 완료"
+    else:
+        return "사용하시는 API Key를 입력하여 주시기 바랍니다."
+
+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):
+    try:
+        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))
+        
+        return "", chat_history
+    except:
+        chat_history.append(("", "API Key 입력 요망"))
+        
+        return " ", chat_history
+
+        # return "", chat_history
+
+    
+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")
+
+    #chk_key.click(Loading, None, langchain_status, queue=False)  
+    chk_key.click(LoadData, openai_key, outputs=[langchain_status], queue=False)
+    
+    # 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()