Good day everyone. I would like to ask why I’ m having an error like this.
Hi @Bosti07! Welcome to the Streamlit community!
Apps deployed on Streamlit Sharing get up to 1 CPU, 800 MB of RAM, and 800 MB of dedicated storage in a shared execution environment.
What is the size of your dataset? If it’s on the order of Synthetic Financial Datasets For Fraud Detection, uploading subsets of that size a couple of times while simultaneously training your models are bound to exhaust the allotted 800 MB of RAM. It might help to cache the dataset with a shorter TTL.
Thank you so much for your reply.
Good day snehankekre. How can I read a csv file from the upload file. I have a snippet code but I’m having an error.
@Bosti07 You can avoid the error by first reading the file with
pd.read_csv() and then returning the dataframe
df. Here’s an example:
import streamlit as st import pandas as pd @st.cache def read_file(file_path): df = pd.read_csv(file_path) return df def main(): uploaded_file = st.file_uploader("Upload file", type=".csv") if uploaded_file: st.markdown("Uploaded file:") df = read_file(uploaded_file) st.dataframe(df) if __name__=='__main__': main()
Hope this helps!
Wow it really works on me. Thank you so much.
Hi @snehankekre . Can I limit a upload file size from 200MB to 100MB?
Thank you so much.
You can change the default limit for
st.file_uploader by editing the following configuration option in your
config.toml to be 100MB:
# Max size, in megabytes, for files uploaded with the file_uploader. # Default: 200 maxUploadSize = 100
Thank you so much I’m gonna try it now.
Good day @snehankekre . Is there a chance I can view it I’m already hitting the limit for my resources?