Update app.py

app.py

@@ -129,7 +129,7 @@ def initialize_llmchain(temperature, max_tokens, top_k, vector_db, progress=gr.P
     
     progress(0.75, desc="Defining buffer memory...")
     #memory = ConversationBufferMemory(memory_key="chat_history",output_key='answer',return_messages=True)
-    memory = ConversationBufferWindowMemory(memory_key = 'history', k=3)
+    memory = ConversationBufferWindowMemory(memory_key = 'chat_history', k=3)
     retriever=vector_db.as_retriever()
     progress(0.8, desc="Defining retrieval chain...")
     qa_chain = ConversationalRetrievalChain.from_llm(llm,retriever=retriever,chain_type="stuff", 
@@ -176,20 +176,20 @@ def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Pr
     return qa_chain, "Complete!"
 
 
-def format_chat_history(message, history):
+def format_chat_history(message, chat_history):
     formatted_chat_history = []
-    for user_message, bot_message in history:
+    for user_message, bot_message in chat_history:
         formatted_chat_history.append(f"User: {user_message}")
         formatted_chat_history.append(f"Assistant: {bot_message}")
     return formatted_chat_history
     
 
-def conversation(qa_chain, message, history):
-    formatted_chat_history = format_chat_history(message, history)
+def conversation(qa_chain, message, chat_history):
+    formatted_chat_history = format_chat_history(message, chat_history)
     #print("formatted_chat_history",formatted_chat_history)
    
     # Generate response using QA chain
-    response = qa_chain({"question": message, "history": formatted_chat_history})
+    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
     response_answer = response["answer"]
     if response_answer.find("Helpful Answer:") != -1:
         response_answer = response_answer.split("Helpful Answer:")[-1]