Update app.py

app.py

@@ -150,4 +150,46 @@ def main():
     st.sidebar.markdown("### Select Large Language Model (LLM)")
     llm_option = st.sidebar.radio("Available LLMs", list_llm_simple)
 
-    if
+    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)
+
+        message = st.text_input("Ask a question", key="message")
+        if st.button("Submit"):
+            if message:
+                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
+
+                    # Display the new response immediately
+                    st.markdown(f"**User:** {message}")
+                    st.markdown(f"**Assistant:** {response_answer}")
+
+                    st.markdown("### Relevant context from the source document")
+                    with st.expander("Relevant context from the source document"):
+                        for i, source in enumerate(sources):
+                            st.text_area(f"Source {i + 1} - Page {source['page']}", value=source["content"], height=100)
+
+if __name__ == "__main__":
+    main()