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
Read the docs about streaming.
Posting code text is better than posting code images.
type or paste code here
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))`
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I did reformat the code sorry for pasting it in that way really thankful for your assistance