Building Streamlit Chat UI

Streamlit chat interfaces

Build conversational UIs with Streamlit's chat elements.

Basic chat structure

import streamlit as st

if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat history
for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.write(msg["content"])

# Handle new input
if prompt := st.chat_input("Ask a question"):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user"):
        st.write(prompt)

    with st.chat_message("assistant"):
        response = get_response(prompt)  # Your LLM call
        st.write(response)

    st.session_state.messages.append({"role": "assistant", "content": response})

Streaming responses

Use st.write_stream for token-by-token display. Pass any generator that yields strings, including the OpenAI generator directly:

def get_streaming_response(prompt):
    # Replace with your LLM client (OpenAI, Anthropic, Cortex, etc.)
    for chunk in your_llm_client.stream(prompt):
        yield chunk

with st.chat_message("assistant"):
    response = st.write_stream(get_streaming_response(prompt))

st.session_state.messages.append({"role": "assistant", "content": response})

With OpenAI, you can pass the stream directly:

from openai import OpenAI

client = OpenAI()
with st.chat_message("assistant"):
    stream = client.chat.completions.create(
        model="gpt-4o",
        messages=st.session_state.messages,
        stream=True,
    )
    response = st.write_stream(stream)

Chat message avatars

Streamlit provides default avatars for "user" and "assistant" roles—only customize if you have a specific need. You can use icons or images:

# With icons
with st.chat_message("assistant", avatar=":material/robot:"):
    st.write(assistant_message)

# With images
with st.chat_message("user", avatar="https://example.com/avatar.png"):
    st.write(user_message)

Suggestion chips

Offer clickable suggestions before the first message. The pills disappear once the user sends a message, creating a clean onboarding experience:

SUGGESTIONS = {
    ":blue[:material/help:] What is Streamlit?": "Explain what Streamlit is",
    ":green[:material/code:] Show me an example": "Show a simple Streamlit example",
}

# Only show before first message - they disappear after
if not st.session_state.messages:
    selected = st.pills("Try asking:", list(SUGGESTIONS.keys()), label_visibility="collapsed")
    if selected:
        # Use the selection as the first prompt
        prompt = SUGGESTIONS[selected]
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.rerun()

The if not st.session_state.messages check ensures the suggestions only appear on an empty chat. Once a message is added, the pills vanish and the conversation takes over.

File uploads

Enable file attachments with accept_file. When enabled, st.chat_input returns a dict-like object with text and files attributes:

prompt = st.chat_input(
    "Ask about an image",
    accept_file=True,
    file_type=["jpg", "jpeg", "png"],
)

if prompt:
    with st.chat_message("user"):
        if prompt.text:
            st.write(prompt.text)
        if prompt.files:
            st.image(prompt.files[0])

    # Send to vision model
    with st.chat_message("assistant"):
        response = analyze_image(prompt.files[0], prompt.text)
        st.write(response)

Use accept_file="multiple" to allow multiple files.

Audio input

Enable voice recording with accept_audio. The recorded audio is available as a WAV file:

prompt = st.chat_input("Say something", accept_audio=True)

if prompt:
    if prompt.audio:
        st.audio(prompt.audio)
    if prompt.text:
        st.write(prompt.text)

Dictation with speech-to-text

Convert audio to text and inject it back into the chat input:

prompt = st.chat_input("Say something", accept_audio=True, key="chat")

if prompt and prompt.audio:
    # Transcribe with Whisper or another STT model
    transcript = openai.audio.transcriptions.create(
        model="whisper-1",
        file=prompt.audio,
    )
    # Set the transcribed text as the next input
    st.session_state.chat = transcript.text
    st.rerun()

User feedback

Add thumbs up/down feedback to assistant messages. Also supports "stars" and "faces" ratings:

with st.chat_message("assistant"):
    st.markdown(response)
    feedback = st.feedback("thumbs")
    if feedback is not None:
        st.toast(f"Feedback received: {'👍' if feedback == 1 else '👎'}")

Clear chat

Add a button to reset the conversation:

def clear_chat():
    st.session_state.messages = []

st.button("Clear chat",

Related skills

• connecting-streamlit-to-snowflake: Database queries and Cortex chat example
• optimizing-streamlit-performance: Caching strategies for LLM calls

References

• st.chat_message
• st.chat_input
• st.write_stream