Hi streamlit community members glad to be in touch with you , I have been trying to incorporate streaming response feature of streamlit in my retrieval augmented generation application but it return the response as shown in the attached images any one has a clue as to how to solve this issue, thanks 
for your collaboration
import os
from dotenv import load_dotenv
import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.vectorstores.qdrant import Qdrant
import qdrant_client
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
import tiktoken
from langchain.chains.combine_documents import create_stuff_documents_chain
# load the variables
load_dotenv()
collection_name = os.getenv("QDRANT_COLLECTION_NAME")
# get the vector stor
def get_vector_store():
client = qdrant_client.QdrantClient(
url=os.getenv("QDRANT_HOST"),
api_key=os.getenv("QDRANT_API_KEY"),
)
embeddings = OpenAIEmbeddings()
vector_store = Qdrant(
client=client,
collection_name=collection_name,
embeddings=embeddings,
)
return vector_store
vector_store = get_vector_store()
def get_context_retriever_chain(vector_store=vector_store):
llm = ChatOpenAI()
retriever = vector_store.as_retriever()
prompt = ChatPromptTemplate.from_messages(
[
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
(
"user",
"Given the above conversation, generate a search query to look up in order to get information relevant to the conversation",
),
]
)
retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
return retriever_chain
def get_conversational_rag_chain(retriever_chain):
llm = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"""
You are a specialized doctor AI medical assistant programmed to address inquiries about medicine, ICD10 codes, diagnosis,, symptoms and differential diagnosis .
Your responses should strictly adhere to the medical field context:\n\n{context} you have been trained in. Avoid providing general knowledge answers or responses outside of your medical training.
If a question falls outside of the medical realm or exceeds your expertise, reply with: Sorry, I don't know about this as it's beyond my training context as a medical AI assistant.
Refrain from answering queries on unrelated topics such as religions, sports, programming, and others listed here
[ religions, general knowledge , sports ,non-medical sciences ,
universe,math , programming, coding, outfits , cultures, ethnicities, Management ,
business , politics , how to make something like food, agriculture all general knowledge topics except medicine,..... etc ], as they lie outside your scope of expertise be polite and recognize greetings like hi , hello etc.
"""
),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
]
)
stuff_documents_chain = create_stuff_documents_chain(llm, prompt)
return create_retrieval_chain(retriever_chain, stuff_documents_chain)
def get_response(user_input):
retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
response = conversation_rag_chain.stream(
{"chat_history": st.session_state.chat_history, "input": user_input}
)
# If a response was retrieved, proceed with using it
return response
# app layout
st.set_page_config("Conversational AI Doctor ", "🤖")
st.title("Doctor AI Assistant 👨⚕️")
with st.sidebar:
photo_url = "https://i.ibb.co/3k14LmY/Whats-App-Image-2024-02-10-at-9-03-47-AM.jpg"
# Add HTML to the sidebar to display the image as a circle
st.markdown(
f'<a href="https://ibb.co/6NYrf0J"><img src="{photo_url}" alt="Your Photo" style="width: 100px; height: 100px; border-radius: 50%;"></a>',
unsafe_allow_html=True
)
st.markdown(
"<div style='text-align: justify'>"
"The Doctor AI Assistant is an advanced artificial intelligence tool designed to aid physicians in diagnosing diseases swiftly and accurately. "
"It provides comprehensive support by addressing a wide array of queries, including those related to ICD10 codes, diagnoses, symptoms, and differential diagnoses across all medical specialties. "
"Additionally, it assists in the submission of relevant insurance claims and ensures adherence to drug indications consistent with ICD10 codes, guidelines, and best medical practices. "
"With multilingual capabilities, it offers assistance in all languages spoken worldwide, empowering healthcare professionals with unparalleled efficiency and accuracy in patient care. "
"This AI App was developed by <b>MOHAMMED BAHAGEEL</b>, Artificial intelligence scientist as a part of his experiments using Retrieval Augmented Generation."
"</div>",
unsafe_allow_html=True
)
if "chat_history" not in st.session_state:
st.session_state.chat_history = [AIMessage(content=" Hello ! with you is Doctor Assistant AI chatbot how can I assist you today with your medical questions ? 🥰")]
if "vector_store" not in st.session_state:
st.session_state.vector_store = get_vector_store()
for message in st.session_state.chat_history:
if isinstance(message, AIMessage):
with st.chat_message("AI", avatar="🤖"):
st.write(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message("Human", avatar="👨⚕️"):
st.write(message.content)
# user input
user_query = st.chat_input("Type your message here...")
if user_query is not None and user_query != "":
st.session_state.chat_history.append(HumanMessage(content=user_query))
with st.chat_message("Human", avatar="👨⚕️"):
st.markdown(user_query)
with st.chat_message("AI",avatar="🤖"):
response=st.write_stream(response)
st.session_state.chat_history.append(AIMessage(content=response))`
if anyone has a clue how to solve this issue please help me
