Upload main.py

main.py

@@ -267,15 +267,24 @@ conversational_rag_chain = RunnableWithMessageHistory(
 
 
 def get_response(userquery:str):
+    response_generator = conversational_rag_chain.stream(
+        {"input": userquery},
+        config={"configurable": {"session_id": "abc123"}}
+    )
+    #len_query = len(user_query)
+    #c = -1
+    # Since it's a generator, iterate over the response
+    for rag_response in response_generator :
         
-    rag_response =  conversational_rag_chain.invoke(
-            {"input": userquery},
-                config={
-                "configurable": {"session_id": "abc123"}
-                },
-        )["answer"]
-    
-    return rag_response
+        print(rag_response)
+        
+        for k, v in rag_response.items():
+            #if k == 'context':
+                #continue
+            #elif k == 'chat_history':
+                #continue
+            if k == 'answer':
+                yield v
 
 ##creating streamlit frontend now 
 
@@ -286,6 +295,7 @@ st.title("Calm Counsel")
 history = get_session_history(st.session_state.secret_id)
 
 user_query = st.chat_input("Type your message here...")
+
 sequence_number = 0
 if user_query is not None and user_query != "":
     
@@ -304,21 +314,11 @@ if user_query is not None and user_query != "":
     
     #adding user query to st history 
     st.session_state.msg_list.append({'role':'human','content':user_query})
-
-   
-    #trying for dict to json  
-   
-    
-    
-    #get ai response
-    with st.spinner("Thinking..."):
-        response = get_response(user_query)
-    
     
     #display ai response
-    
     with st.chat_message("ai"):
-        st.markdown(response)
+        with st.spinner('Thinking...'):
+            response = st.write_stream(get_response(user_query)) 
         
     print(st.session_state.msg_list)
     
@@ -326,6 +326,7 @@ if user_query is not None and user_query != "":
     print('Ii' * 100)
     
 
+
 print('Mm' * 100)