Update app.py

app.py

@@ -1,25 +1,19 @@
 import os
-import pymssql
 import pandas as pd
-
-
+import gradio as gr
 
 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):
     
+def LoadData(openai_key):
+        
     if openai_key is not None:
         
         os.environ["OPENAI_API_KEY"] = openai_key
@@ -28,6 +22,7 @@ def LoadData(openai_key):
 
         embedding = OpenAIEmbeddings()
 
+
         vectordb = Chroma(
             persist_directory=persist_directory, 
             embedding_function=embedding
@@ -42,13 +37,13 @@ def LoadData(openai_key):
 
 
 # 챗봇의 답변을 처리하는 함수
-def respond(message, chat_history):
+def respond(message, chat_history, temperature):
     try:
-        # if freelv == "":
-        #     freelv = 0
-            
+        
+        print(temperature)
+
         qa_chain = RetrievalQA.from_chain_type(
-            llm=OpenAI(temperature=0.4),
+            llm=OpenAI(temperature=temperature),
             # llm=OpenAI(temperature=0.4), 
             # llm=ChatOpenAI(temperature=0),
             chain_type="stuff", 
@@ -58,14 +53,6 @@ def respond(message, chat_history):
         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'] + ' '
-
-        # historySave(message=message, answer=str(result['result']).replace("'",""))
         
         # 채팅 기록에 사용자의 메시지와 봇의 응답을 추가.
         chat_history.append((message, bot_message))
@@ -76,7 +63,6 @@ def respond(message, chat_history):
         
         return " ", chat_history
     
-import gradio as gr
 
 # 챗봇 설명
 title = """
@@ -98,35 +84,52 @@ 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):
-            #     freelv = gr.Textbox(placeholder="유연성", 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")
+    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.Row():
+        with gr.Column(scale=5):
+            temperature = gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=2.0,
+            step=0.1,
+            value=0.7,
+        )
+        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)
+    chk_key.click(
+        fn=LoadData, 
+        inputs=[openai_key], 
+        outputs=[langchain_status], 
+        queue=False
+    )
     
     # 사용자의 입력을 제출(submit)하면 respond 함수가 호출.
-    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    msg.submit(
+        fn=respond, 
+        inputs=[msg, chatbot, temperature], 
+        outputs=[msg, chatbot]
+    )
 
-    submit.click(respond, [msg, chatbot], [msg, chatbot])
+    submit.click(respond, [msg, chatbot, temperature], [msg, chatbot])
 
     # '초기화' 버튼을 클릭하면 채팅 기록을 초기화.
     clear.click(lambda: None, None, chatbot, queue=False)