Stream a generator, iterable, or stream-like sequence to the app.
st.write_stream iterates through the given sequences and writes all chunks to the app. String chunks will be written using a typewriter effect. Other data types will be written using st.write.
| Function signature[source] | |
|---|---|
st.write_stream(stream) | |
| Parameters | |
stream (Callable, Generator, Iterable, OpenAI Stream, or LangChain Stream) | The generator or iterable to stream. Note To use additional LLM libraries, you can create a wrapper to manually define a generator function and include custom output parsing. |
| Returns | |
(str or list) | The full response. If the streamed output only contains text, this is a string. Otherwise, this is a list of all the streamed objects. The return value is fully compatible as input for st.write. |
Example
You can pass an OpenAI stream as shown in our tutorial, Build a basic LLM chat app. Alternatively, you can pass a generic generator function as input:
import time import numpy as np import pandas as pd import streamlit as st _LOREM_IPSUM = """ Lorem ipsum dolor sit amet, **consectetur adipiscing** elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. """ def stream_data(): for word in _LOREM_IPSUM.split(" "): yield word + " " time.sleep(0.02) yield pd.DataFrame( np.random.randn(5, 10), columns=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"], ) for word in _LOREM_IPSUM.split(" "): yield word + " " time.sleep(0.02) if st.button("Stream data"): st.write_stream(stream_data)
Tip
If your stream object is not compatible with st.write_stream, define a wrapper around your stream object to create a compatible generator function.
for chunk in unsupported_stream:
yield preprocess(chunk)
For an example, see how we use Replicate with Snowflake Arctic in this code.
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