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Apps

mljar-supervised can generate Mercury apps from trained AutoML models. You can:

  • generate the app workspace with AutoML.app()
  • start it locally with AutoML.local_app()
  • publish it quickly with AutoML.publish_app()
  • run or host the generated app manually on your own infrastructure

Generate app files

Use app() to generate the app workspace:

from supervised import AutoML

automl = AutoML(results_path="AutoML")
automl.fit(X, y)
automl.app()

The generated app directory contains runtime files such as:

  • predict_single.ipynb
  • predict_batch.ipynb
  • app_support.py
  • mljar_app.json
  • config.toml
  • requirements.txt
  • runtime.txt
  • automl.zip

The default generated app title is MLJAR AutoML. You can provide your own title with:

automl.app(title="Churn Prediction")

Start locally

Use local_app() to generate the app, start Mercury, and open the browser:

automl.local_app()

The local app runs in the foreground.

Note

Press Ctrl+C to stop the local app.

Start manually

You can also start the generated app manually. This is useful if you want to control the environment yourself or debug the generated workspace.

Generate the app:

automl.app()

Then run Mercury in the generated app directory:

cd AutoML/app
mercury --working-dir=.

If Mercury is not installed in the current environment, install the generated app dependencies first:

pip install -r requirements.txt

Publish quickly

Use publish_app() for the fastest way to put the app online:

automl.publish_app()

This helper:

  • signs in through platform.mljar.com
  • creates or updates the app URL
  • uploads the generated runtime files
  • prints progress and friendly error messages
  • remembers the last successfully published app URL

If you want to reuse a specific published app URL:

automl.publish_app(url="https://your-app.ismvp.org")

Publish or host manually

Using platform.mljar.com is simply the fastest path, but it is not required.

You can generate the app workspace with app() and then host it yourself:

  1. Generate the app files
  2. Install the generated dependencies
  3. Run Mercury on your own server
  4. Deploy with your own preferred hosting setup

This is a good option when you want full control over infrastructure, networking, or deployment workflow.

Generated app behavior

The generated app can include:

  • a single prediction dashboard
  • batch CSV scoring
  • prediction download for batch mode
  • feature importance plots when available
  • feature context plots for single prediction mode

Limits

If the trained model has more than 15 features, the generated app will include batch CSV scoring only.

Warning

For datasets with more than 15 features, mljar-supervised skips the single prediction widget dashboard and generates a batch-only app.

  • use app() when you want the generated files
  • use local_app() when you want the quickest local preview
  • use publish_app() when you want the fastest hosted deployment
  • use manual startup or your own server when you need full control
  • regenerate the app after retraining the model

Tutorials

If you want a beginner-friendly walkthrough after training a model, check: