mainfile cgpt 6

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import os
+import asyncio
 from langchain.vectorstores import Chroma
 from langchain.embeddings import HuggingFaceBgeEmbeddings
 from langchain_together import Together
@@ -81,6 +82,19 @@ def create_conversational_qa_chain(retriever, condense_llm, answer_llm):
         generate_answer=answer_chain
     )
 
+# Asynchronous function to handle streaming responses
+async def stream_response(conversational_qa_chain, prompts2, chistory):
+    response_chunks = []
+    async for chunk in conversational_qa_chain.astream(
+        {
+            "question": prompts2,
+            "chat_history": chistory,
+        }
+    ):
+        response_chunks.append(chunk['generate_answer'])
+        st.write("".join(response_chunks))
+    return "".join(response_chunks)
+
 # Define the Streamlit app
 def app():
     with st.sidebar:
@@ -130,17 +144,7 @@ def app():
         conversational_qa_chain = create_conversational_qa_chain(retriever, llmc, llm)
         with st.chat_message("assistant"):
             with st.spinner("Thinking..."):
-                response_chunks = []
-                async for chunk in conversational_qa_chain.astream(
-                    {
-                        "question": prompts2,
-                        "chat_history": chistory,
-                    }
-                ):
-                    response_chunks.append(chunk['generate_answer'])
-                    st.write("".join(response_chunks))
-                    
-        final_response = "".join(response_chunks)
+                final_response = asyncio.run(stream_response(conversational_qa_chain, prompts2, chistory))
         message = {"role": "assistant", "content": final_response}
         st.session_state.messages.append(message)