Health Metrics

A self-hostable Streamlit app for tracking personal health metrics with local SQLite storage, authenticated access, and simple dashboard views.

It is intentionally lightweight: no managed cloud database, no large backend stack, and no complicated deployment requirements. It is designed for personal use and small-scale self-hosting.

Features

• Email/password login
• Encrypted cookie-based session persistence
• Local SQLite storage for both authentication and metric data
• Add and view health entries
• Time-series dashboard with plots and table view
• Edit and delete entries with confirmation and password re-entry
• Admin account support for managing all users' entries
• Docker support for simple deployment

Important:

• In-app user registration is disabled.
• Creating and deleting users must be done from the terminal.

Metrics tracked

Each health entry can include:

• systolic blood pressure
• diastolic blood pressure
• heart rate
• weight
• note
• timestamp

Tech stack

• Streamlit
• SQLite
• Pandas
• Plotly
• Cryptography (Fernet)
• Docker

Local data storage

The app uses two SQLite databases:

• auth_users.db for user authentication data
• health_metrics.db for health metric entries

No separate database server is required.

Environment variables

Configure the app through a .env file:

• ENCRYPTION_KEY – Fernet key used for cookie encryption
• WEBSITE – URL used for the Home button
• LOCAL_DB_PATH – path to the health metrics database
  Default: health_metrics.db
• AUTH_DB_PATH – path to the auth database
  Default: auth_users.db

Run locally

1. Create and activate a virtual environment

python3 -m venv .venv
source .venv/bin/activate

2. Install dependencies

pip install -r requirements.txt

3. Start the app

streamlit run app.py

Docker

Build and run with Docker:

docker build -t health-metrics-app .
docker run -p 8501:8501 --env-file .env health-metrics-app

The container exposes the Streamlit app on port 8501

User management

In-app registration is intentionally disabled.

Users are managed from the terminal by writing directly to the authentication database.
The current repository includes example one-line commands for:

• listing users
• creating users
• deleting users

This keeps the app simple while still allowing controlled account management.

User Management Commands

Run these from the project root with your virtual environment activated:

1. Show current users

python -c "import sqlite3; c=sqlite3.connect('auth_users.db'); rows=c.execute('SELECT id,email,created_at FROM users ORDER BY id').fetchall(); print('\n'.join(str(r) for r in rows) or 'No users found'); c.close()"

2. Create a user

python -c "import sqlite3; from datetime import datetime, timezone; from werkzeug.security import generate_password_hash as h; email='newuser@example.com'; pw='ChangeThisPassword123'; c=sqlite3.connect('auth_users.db'); cur=c.cursor(); cur.execute('INSERT INTO users (email,password_hash,created_at) VALUES (?,?,?)',(email,h(pw),datetime.now(timezone.utc).isoformat())); c.commit(); print('rows_inserted=',cur.rowcount); c.close()"

3. Delete a user

python -c "import sqlite3; email='user_to_delete@example.com'; c=sqlite3.connect('auth_users.db'); cur=c.cursor(); cur.execute('DELETE FROM users WHERE lower(email)=lower(?)',(email,)); c.commit(); print('rows_deleted=',cur.rowcount); c.close()"

Notes:

• Replace the example emails/passwords before running commands.
• Use strong passwords and avoid shell special characters unless escaped.

Security notes

• Sessions are stored using encrypted cookies
• Edit and delete operations require password re-entry
• For self-hosted deployments behind a reverse proxy, binding Streamlit to localhost is the safer default

Notes

This is a small personal project, not a production medical platform.
The focus is on lightweight self-hosting, straightforward data entry, and simple visualization.

License

MIT License. See LICENSE.