Update app.py

app.py

@@ -6,6 +6,8 @@ from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import RunnablePassthrough
 from langchain_groq import ChatGroq
 from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferWindowMemory
 
 
 
@@ -52,6 +54,16 @@ Answer:""",
     ]
 )
 
+# Conversation history memory
+memory = ConversationBufferWindowMemory(k=3)
+
+# Define the conversation chain
+conversation = ConversationChain(
+    llm=llm,
+    memory=memory,
+    verbose=True
+)
+
 def format_docs(docs):
     return "\n\n".join(doc.page_content for doc in docs)
 
@@ -62,36 +74,40 @@ rag_chain = (
     | StrOutputParser()
 )
 
-# Function to handle chatbot interaction
-def rag_chain_response(messages, user_message):
-    # Generate a response using the RAG chain
-    response = rag_chain.invoke(user_message)
-    
-    # Append the user's message and the response to the chat
+
+
+# Define the Gradio app
+def chatbot_response(messages, user_message):
+    # Update memory with the user's message
+    memory.chat_memory.add_user_message(user_message)
+
+    # Get the response from the conversation chain
+    response = conversation.run(input=user_message)
+
+    # Append the user's message and the response to the chat history
     messages.append((user_message, response))
-    
-    # Return the updated chat and clear the input box
+
+    # Return the updated messages and clear the input box
     return messages, ""
 
 
-# Define the Gradio app
 with gr.Blocks(theme="rawrsor1/Everforest") as app:
-    
-    
+   
+
     chatbot = gr.Chatbot([], elem_id="RADAR", bubble_full_width=False)
     question_input = gr.Textbox(label="Ask a Question", placeholder="Type your question here...")
     submit_btn = gr.Button("Submit")
-    
+
     # Set up interaction for both Enter key and Submit button
     question_input.submit(
-        rag_chain_response,          # Function to handle input and generate response
-        inputs=[chatbot, question_input],  # Pass current conversation state and user input
-        outputs=[chatbot, question_input]  # Update conversation state and clear the input
+        chatbot_response,
+        inputs=[chatbot, question_input],
+        outputs=[chatbot, question_input]
     )
     submit_btn.click(
-        rag_chain_response,          # Function to handle input and generate response
-        inputs=[chatbot, question_input],  # Pass current conversation state and user input
-        outputs=[chatbot, question_input]  # Update conversation state and clear the input
+        chatbot_response,
+        inputs=[chatbot, question_input],
+        outputs=[chatbot, question_input]
     )
 
 # Launch the Gradio app