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Deploy

optimumai dashboard runs the Streamlit progress dashboard locally (pip install "optimumai[dashboard]"). To share it as a public link — for teaching, demos, or a portfolio — deploy it to a free host. Both options below run the same app: src/optimumai/dashboard/app.py.

pip install "optimumai[dashboard]"
optimumai dashboard                    # localhost:8501
optimumai dashboard --port 8888
  1. Push this repo to GitHub (already done for the canonical project).
  2. Go to share.streamlit.io → New app.
  3. Point it at the repo, branch main, main file src/optimumai/dashboard/app.py.
  4. Add a requirements.txt (or reuse the project) containing optimumai[dashboard].
  5. Click Deploy — you get a public *.streamlit.app URL in under a minute.

Option 2 — Hugging Face Spaces

  1. Create a new Space → SDK: Streamlit.
  2. In the Space repo, add a one-line app.py:
from optimumai.dashboard import app   # runs the Streamlit script on import
  1. Add requirements.txt with optimumai[dashboard].
  2. Push — the Space builds automatically and serves a public URL.

Both options are free and give you a shareable link without running a server yourself. The dashboard is read-only (it only reads ~/.optimumai/progress.json on the server side), so there are no security concerns with public deployment.

What the dashboard shows

  • Per-track completion breakdown (progress bars per track)
  • Overall completion percentage
  • "What's next" recommendation (the first incomplete lesson in the next track)
  • Quiz performance history
  • Spaced-repetition review schedule (what's due and when)

Custom progress file path

If you want the deployed dashboard to reflect a different user's progress, set the OPTIMUMAI_PROGRESS_PATH environment variable in your Space or Streamlit app settings before deploying.