streamlit-healthcheck 0.1.9

Comprehensive health monitoring for Streamlit apps: system, dependencies, custom checks, and page errors.

Streamlit HealthCheck

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Monitor, visualize, and manage the health of your Streamlit multi-page applications with ease.

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Overview

Streamlit HealthCheck is a comprehensive health monitoring solution for Streamlit apps. It tracks system resources, external dependencies, and custom application checks, providing a real-time dashboard for operational insights and troubleshooting.

• System Resource Monitoring: CPU, memory, disk usage with configurable thresholds
• Dependency Checks: API endpoints and database connectivity status
• Custom Health Checks: Easily register and visualize custom checks for your app
• Streamlit Page Error Tracking: Detects exceptions and st.error calls across pages
• Live Dashboard: Interactive Streamlit UI with tabs for system, dependencies, custom checks, and page errors
• Configurable: All checks and thresholds are managed via a simple JSON config file

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How to Set Up & Run

Follow these steps to set up and launch the Streamlit HealthCheck dashboard using the status_page app:

1. Install Dependencies

Make sure you have Python 3.11+ and Streamlit installed. You can install all required packages using:

pip install -r requirements.txt

Or, to install just the healthcheck package:

pip install streamlit_healthcheck

2. Configure Health Checks

Edit config/health_check_config.json to customize system thresholds, endpoints, and custom checks. Example config:

{
  "system": { "cpu": 80, "memory": 75 },
  "dependencies": ["https://api.example.com", "postgresql://..."],
  "custom_checks": ["my_custom_check"]
}

3. Run the Streamlit Dashboard

Launch the main dashboard app:

streamlit run status_page/Home.py

This will open the healthcheck dashboard in your browser. Use the sidebar to navigate between system, dependency, and custom check tabs.

4. Add More Pages (Optional)

You can extend the dashboard by adding new pages to the status_page/pages/ directory. Each .py file will appear as a tab in the Streamlit app.

5. Troubleshooting

If you encounter issues:

• Ensure your config file is valid JSON
• Check that all dependencies are reachable
• Review error messages in the dashboard tabs

Also follow the atatched how to guide

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Features

Feature	Description
System Health	Monitors CPU, memory, disk usage with warning/critical thresholds
Dependency Checks	Verifies API endpoints and database connections
Custom Checks	Register custom health checks for your app logic
Page Error Tracking	Captures exceptions and Streamlit errors per page
Live Dashboard	Interactive UI with tabs and status indicators
Configurable	JSON-based config for checks and thresholds

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Configuration

All health check settings are managed via config/health_check_config.json. You can customize:

• Check intervals
• System resource thresholds
• API endpoints and database connections
• Custom checks
• Supports environment variables and optional YAML/JSON config for check registration.
• Default timeouts and thresholds are overridable per-check.

[!TIP] Use the dashboard's configuration expander to adjust thresholds and save changes on the fly.

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Project Structure

src/streamlit_healthcheck/      # Core healthcheck logic
status_page/                   # Streamlit UI pages
config/health_check_config.json # Health check configuration
requirements.txt               # Python dependencies
Makefile                       # Build and test commands
tests/                         # Unit tests

The project structure and control flow looks like below

Project Control Flow

  [System Metrics]    [Dependency Probes]    [Custom Checks]
         |                   |                      |
         +--------+----------+----------+-----------+
                  |                     |
               Collector (polls / hooks / events)
                          |
                      Checker Engine
                 (thresholds, retries, caching)
                          |
                      Aggregator
                (status rollup + history store)
                          |
                   Streamlit Dashboard
               (status panels, graphs, runbooks)
                          |
              Alerts / Notifications / Webhooks

Figure: high-level architecture and control flow (image from assets/).

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Key features

• Run synchronous or async health checks with timeouts and recovery hints
• Register custom checks (liveness, readiness, dependency checks)
• Expose HTTP/Streamlit endpoints for machine-readable and human-readable status
• Emit structured metrics/events suitable for scraping or CI validation
• Simple integration helpers for common backends (Redis, Postgres, external APIs)

Quickstart

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1. Install:

pip install streamlit_healthcheck

2. Basic usage:

from streamlit_healthcheck import healthcheck
report = healthcheck.run_all(timeout=5)
if not report:
   # handle degraded state (log, alert, fail pipeline)
   print(report.summary)

API (common surface)

• healthcheck.run_all(timeout: float = 5) -> HealthReport Runs all registered checks and returns a HealthReport object with:

  • ok: bool
  • summary: str
  • details: dict
  • duration: float

• healthcheck.register(name: str, fn: Callable, *, critical: bool = False) Register a custom check function. Critical checks mark the whole service unhealthy.

• healthcheck.serve(endpoint: str = "/health", host: str = "0.0.0.0", port: int = 8000) Expose a simple HTTP endpoint (or embed in Streamlit) that returns JSON health status.

DevOps alignment

• Reliable: Designed to reduce deployment failures and improve service uptime.
• Automatable: Designed to be executed in CI/CD pipelines (pre-deploy checks, post-deploy smoke tests).
• Observable: Emits structured outputs and metrics for dashboards and alerting.
• Lean: Small, focused checks to enable frequent, low-risk deployments.
• Measurable: Integrates with monitoring to improve MTTR and change failure rate.
• Shareable: Clear APIs, runbooks examples, and integration docs for teams.

Integration tips

• Use canary deployments or blue-green deployments to minimize impact during rollouts.
• Use feature flags or conditional checks to avoid noisy alerts during rollouts.
• Run healthcheck.run_all in CI as a gating step for deployments.
• Expose metrics to Prometheus or your metrics backend for SLA tracking.

Contributing

• Tests, type hints, and small, focused PRs welcome.
• Follow the repository's CONTRIBUTING.md and code-of-conduct.
• Add unit tests for new checks and integrations; CI runs linting and tests.
• Use GitHub issues for bugs, feature requests, and discussions.

Each push on main with create a tag and publish to PyPI.

License

GNU GENERAL PUBLIC LICENSE v3

Troubleshooting

[!WARNING] If the dashboard reports a "critical" status, check the error details in the relevant tab. For API/database issues, verify connectivity and credentials. For system resource alerts, consider scaling your infrastructure.

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Getting Help

• Library Documentation
• Issues & Discussions

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