Exporting the data frame uploaded on Streamlit cloud to AWS bucket

[snehankekre]

Hi @gwk01,

Welcome to the Streamlit forum!

Here’s a working example of using a Streamlit app to upload a dataframe to an AWS bucket:

Save the results of a button click to a csv in S3

Summary I want the user to be able to click a button, and save the selection to a csv in S3. Steps to reproduce Code snippet: # option 1 (two individual buttons) if st.sidebar.button("Yep."): feedback = '1' if st.sidebar.button("Nope."): feedback = '0' df = pd.DataFrame({'feedback': feedback}, index=[0]) with s3.open(f"{path}.csv", 'wb') as f: df.to_csv(f) # option 2 (radio buttons) feedback = st.sidebar.radio( "Am I right?", ('Yes', 'No'), horizontal=True ) # Save …

It is indeed a huge security risk to post your tokens publicly. Which is why Streamlit and Community Cloud have a Secrets management feature:

Secrets management - Streamlit Docs

When developing your app locally, you can create at the root of your app’s directory a .streamlit/secrets.toml file containing your tokens. For example:

.streamlit/secrets.toml

AWS_TOKEN="thisismytoken"
AWS_OTHER_SECRET="myothersecret"

If you create the above secrets, you can reference them in your app like so:

# streamlit_app.py
import streamlit as st

# Display secrets
st.write(st.secrets["AWS_TOKEN"])
st.write(st.secrets["AWS_OTHER_SECRET"])

The above will output:

thisismytoken
myothersecret

When deploying remotely, however, add this file to your .gitignore so you don’t commit your secrets to GitHub! On Community Cloud, you can paste in your secrets using the Secrets management console.

Also, check out the following as an example of how to use secrets:

Connect Streamlit to AWS S3 - Streamlit Docs