dashml-lang 0.1.3

Declarative YAML-based compiler for data visualization dashboards. Write your dashboard once, compile to Streamlit, Plotly, Observable Plot, Apache Superset, Vega-Lite, or Grafana.

DashML

A declarative language for data visualization dashboards that compiles to multiple platforms.

Write your dashboard spec once in .dashml format, then compile it to Streamlit, Plotly, Observable Plot, Apache Superset, Vega-Lite, or Grafana.

Install

pip install dashml-lang

Or with uv for an isolated tool install:

uv tool install dashml-lang

The CLI command is dashml (the distribution name dashml-lang only matters at install time).

Quick Start

The repo includes a ready-to-build example at examples/startup_funding.dashml — a 4-page, 20-chart dashboard against real startup funding data. Clone the repo or copy those two files to try it.

# Generate a Streamlit app
dashml build examples/startup_funding.dashml --target streamlit --output app_dir
streamlit run app_dir/app.py

# Generate a standalone Plotly HTML dashboard
dashml build examples/startup_funding.dashml --target plotly --output dashboard_dir

# Generate an Observable Plot HTML dashboard
dashml build examples/startup_funding.dashml --target observable --output dashboard_dir

# Generate a Vega-Lite JSON specification
dashml build examples/startup_funding.dashml --target vegalite --output dashboard_dir

# Generate a Grafana dashboard JSON
dashml build examples/startup_funding.dashml --target grafana --output dashboard_dir

# Create a dashboard directly in Apache Superset
dashml build examples/startup_funding.dashml --target superset \
  --superset-user admin --superset-password admin

For Streamlit, you'll also need the runtime extras:

pip install 'dashml-lang[streamlit]'

dashml list shows all available transformers; dashml --help shows full CLI options.

Database credentials

For SQL or BigQuery data sources, the generated artifact reads connection parameters from environment variables at runtime — never from baked-in literals. The generator emits .env.example, .gitignore, and SECRETS.md next to the generated app.py. Copy .env.example to .env, fill in the values, and run. The same artifact directory is safe to commit to a public repository.

dashml build dashboard.dashml --target plotly --output app_dir
cd app_dir
cp .env.example .env       # edit .env, set DASHML_DB_PASSWORD etc.
python app.py              # reads from env (or .env via python-dotenv)

See SECRETS.md in any generated SQL/BigQuery artifact for the full list of environment variables and recommended deployment patterns (Docker, Kubernetes, systemd, CI/CD, GCP Workload Identity).

Example spec

version: "1.0"
title: "Sales Dashboard"
style: "dracula"

data:
  type: csv
  path: sales.csv

charts:
  - id: "sales_by_country"
    type: "bar"
    title: "Sales by Country"
    x: "country"
    y: "sales"
    agg: "sum"
    sort: "y"
    sort_order: "desc"
    limit: 10

Documentation

• Architecture — system design, compiler pipeline, type system
• Testing — running the test suite
• Quick start notes — extra command snippets

Key Concepts

• Compiler, not runtime — DashML generates standalone code; it never loads or touches your data
• Credentials never baked — generated SQL/BigQuery artifacts read database credentials from environment variables at runtime; safe to version-control
• 12 chart types — bar, line, scatter, pie, area, histogram, stacked_bar, grouped_bar, bubble, heatmap, box, geo
• 3 data sources — CSV, SQL (PostgreSQL/MySQL/SQLite), Google BigQuery
• 6 backends — Streamlit, Plotly, Observable Plot, Apache Superset, Vega-Lite, Grafana
• 6 built-in themes — Dracula, Nord, Gruvbox, Monokai, One Dark, Solarized Light

Authors

• Dawid Olejniczak
• Szymon Nowaczyk

License

MIT