Update app.py

app.py

@@ -150,39 +150,4 @@ def main():
     st.sidebar.markdown("### Select Large Language Model (LLM)")
     llm_option = st.sidebar.radio("Available LLMs", list_llm_simple)
 
-    if st.sidebar.button("Initialize Question Answering Chatbot"):
-        with st.spinner("Initializing QA chatbot..."):
-            qa_chain, llm_message = initialize_LLM(list_llm_simple.index(llm_option), st.session_state['vector_db'])
-            st.session_state['qa_chain'] = qa_chain
-            st.sidebar.success(llm_message)
-
-    st.title("Chat with your Document")
-
-    if st.session_state['qa_chain']:
-        st.markdown("### Chatbot Response")
-
-        # Display the chat history in a chat-like interface
-        for i, (user_msg, bot_msg) in enumerate(st.session_state['chat_history']):
-            st.markdown(f"**User:** {user_msg}")
-            st.markdown(f"**Assistant:** {bot_msg}")
-
-        st.markdown("### Relevant context from the source document")
-
-        with st.expander("Relevant context from the source document"):
-            if 'sources' in st.session_state:
-                for i, source in enumerate(st.session_state['sources']):
-                    st.text_area(f"Source {i + 1} - Page {source['page']}", value=source["content"], height=100)
-
-        with st.form(key="question_form"):
-            message = st.text_input("Ask a question", key="message")
-            submit_button = st.form_submit_button(label="Submit")
-
-        if submit_button:
-            with st.spinner("Generating response..."):
-                qa_chain, chat_history, response_answer, sources = conversation(st.session_state['qa_chain'], message, st.session_state['chat_history'])
-                st.session_state['qa_chain'] = qa_chain
-                st.session_state['chat_history'] = chat_history
-                st.session_state['sources'] = sources
-
-if __name__ == "__main__":
-    main()
+    if