Update app.py

app.py

@@ -6,6 +6,7 @@ import requests
 from pyvi import ViTokenizer, ViPosTagger
 import time
 from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+from langchain_community.chat_message_histories import ChatMessageHistory
 import torch
 
 retriever = load_the_embedding_retrieve(is_ready=False, k=3)
@@ -15,23 +16,39 @@ ensemble_retriever = EnsembleRetriever(
     retrievers=[bm25_retriever, retriever], weights=[0.2, 0.8]
 )
 
+llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0, openai_api_key= os.environ["OPENAI_API_KEY"])
 
 
-def greet2(quote):
+def greet3(quote, history):
 
-    qa_chain = get_qachain(retriever=ensemble_retriever)
+    demo_ephemeral_chat_history = ChatMessageHistory()
+    for user, assistant in eval(history):
+        demo_ephemeral_chat_history.add_user_message(user)
+        demo_ephemeral_chat_history.add_ai_message(assistant)
 
-    prompt = os.environ['PROMPT']
+    #Summary the message
 
-    qa_chain.combine_documents_chain.llm_chain.prompt.messages[0].prompt.template = prompt
+    chat_history = summarize_messages(demo_ephemeral_chat_history=demo_ephemeral_chat_history, llm=llm).messages
 
-    llm_response = qa_chain(quote)
+    #Get the new question
+    new_question = get_question_from_summarize(chat_history[0].content, quote, llm)
 
-    return llm_response['result']
+    #Retrieve
+    documents_query = ensemble_retriever.invoke(new_question)
 
+    # print(documents_query)
 
-if __name__ == "__main__":
-    quote = "Địa chỉ nhà trường?"
+    context = ''
+    for i in documents_query:
+        context += i.page_content + '\n'
+
+    #Get answer
+
+    answer = get_final_answer(question=new_question, context=context, chat_history=chat_history, prompt=os.environ['PROMPT'], llm=llm)
 
-    iface = gr.Interface(fn=greet2, inputs="text", outputs="text")
+    return new_question, answer
+
+
+if __name__ == "__main__":
+    iface = gr.Interface(fn=greet3, inputs=["text", "text"], outputs=["text", "text"])
     iface.launch()