Collecting usage statistics failure

[sibmel29]

Hi there.

App is crashing with this in the terminal:

Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.

I created a config.toml fil in my Github repo, and added the line:

[browser]
gatherUsageStats = false

After reboot, still facing the same issue.

What should I do to fix it?
The repo : https://github.com/cnoret/Energie_France/tree/main
The cloud : https://energie-france.streamlit.app/

Thanks for your time.

[arnaud]

Hey @sibmel29, welcome to our forum

My intuition is this is not the reason why the app crashes. This is just a default log that is shown for every app building.

Would you mind sharing the full traceback in your terminal? I’m hoping there is something else we could understand! Oh and btw, your repository is private, so can’t read it!

[sibmel29]

Thanks Arnaud for the swift reply.

So it seems to be crashing right after the installation of the requirements.

For context, the file contains this:

matplotlib
numpy
pandas
scikit_learn
streamlit
streamlit_option_menu
geopandas
ipywidgets
pyarrow
plotly
colorcet
seaborn
datetime

[sibmel29]

Probably much easier to read in this version:

[ UTC ] Logs for energie-france.streamlit.app/
────────────────────────────────────────────────────────────────────────────────────────
[10:16:00] Starting up repository: ‘energie_france’, branch: ‘main’, main module: ‘streamlit_app.py’
[10:16:00] Cloning repository…
[10:16:04] Cloning into ‘/mount/src/energie_france’…

[10:16:04] Cloned repository!
[10:16:04] Pulling code changes from Github…
[10:16:05] Processing dependencies…

──────────────────────────────────────── pip ───────────────────────────────────────────

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WARNING: You are using pip version 22.0.3; however, version 23.2.1 is available.
You should consider upgrading via the ‘/home/adminuser/venv/bin/python -m pip install --upgrade pip’ command.
Checking if Streamlit is installed
Found Streamlit version 1.27.2 in the environment

────────────────────────────────────────────────────────────────────────────────────────

[10:17:11] Python dependencies were installed from /mount/src/energie_france/requirements.txt using pip.
Check if streamlit is installed
Streamlit is already installed
[10:17:12] Processed dependencies!

Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.

[11:14:40] Streamlit server consistently failed status checks
[11:14:40] Please fix the errors, push an update to the git repo, or reboot the app.

[sibmel29]

Hi there, @arnaud !

We have made the repo public, hoping you’ll be able to check it out on your side when you get a chance.

Thanks a lot!

[arnaud]

Hey @sibmel29,

Thanks for sharing your logs, and also making the repository public. I was able to fork your repository, try to deploy it in Cloud and also locally.

A few notes:

1. I confirm the app crashes in Community Cloud after successfully building. Meaning, the requirements are properly installed, the app starts, but then the server crashes.
2. Launched the app locally and there it does work, but after painful computations being done repeatedly.

I suspect this (the heavy computations being done at app start) might be a reason why the app crashes. To test that hypothesis, I tried commenting all imports and the app now works. Well, the app is now almost empty so you better hope it does. But at least it confirms that all the different packages and utils are not causing issues. See the app and my forked repo.

My recommendation: try and hide all of this costly Pandas logic into a separated function that uses the @st.cache_data decorator. Maybe even better, pre-compute that into your .parquet file already, so this computation doesn’t have to be rerun all the time!

[sibmel29]

Thanks Arnaud!

I created a new parquet file, pushed it from github desktop, and it’s working like a charm now, all the pre-processing needs to be done outside StreamLit, I understand.

Thanks for your help!
Cheers

Simon

[arnaud]

Awesome! Yeah it’s generally best practice to take any job that doesn’t need to be done in Streamlit… outside Streamlit. That will maximize a good user experience for your app visitors.

Kinda resonates with a great article @blackary just wrote btw: How to improve Streamlit app loading speed

Best,