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Share a regression model as an app

You trained AutoML. What next?

You can share the trained regression model as an app so other people can enter values in a browser or upload a CSV file for scoring.

In this tutorial, we will:

  • train a regression model on house prices data,
  • keep the app input form compact by using only 10 features,
  • generate an app from the trained model,
  • run it locally,
  • and optionally publish it online.

Load data

We will use the house prices dataset from datasets-for-start:

import pandas as pd

data = pd.read_csv(
    "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/house_prices/data.csv"
)

print(data.head())

The target column is SalePrice.

Select a compact feature set

The full dataset has many columns. For an app tutorial, it is better to keep the single-prediction form short and readable.

We will use these 10 features:

  • OverallQual
  • GrLivArea
  • GarageCars
  • TotalBsmtSF
  • 1stFlrSF
  • FullBath
  • YearBuilt
  • YearRemodAdd
  • Neighborhood
  • LotArea
features = [
    "OverallQual",
    "GrLivArea",
    "GarageCars",
    "TotalBsmtSF",
    "1stFlrSF",
    "FullBath",
    "YearBuilt",
    "YearRemodAdd",
    "Neighborhood",
    "LotArea",
]

X = data[features]
y = data["SalePrice"]

This keeps the generated Mercury app beginner-friendly and pleasant to use.

Train AutoML

from supervised import AutoML

automl = AutoML(
    results_path="AutoML_House_Prices_App",
    mode="Explain",
    random_state=1,
)
automl.fit(X, y)

Now the regression model is trained and ready to use.

Generate the app

automl.app()

This creates the generated app workspace in:

AutoML_House_Prices_App/app

Because we selected only 10 features, the app will include the full single-prediction form instead of switching to batch-only mode.

Run the app locally

The quickest way to preview it is:

automl.local_app()

This starts Mercury, opens the browser, and keeps the app running until you stop it.

Note

Press Ctrl+C in the terminal to stop the local app.

Run the app manually

You can also run the generated app manually:

cd AutoML_House_Prices_App/app
pip install -r requirements.txt
mercury --working-dir=.

What the app looks like

The generated regression app can include:

  • a single prediction form for one property,
  • a predicted value summary,
  • batch CSV scoring,
  • a downloadable scored file,
  • feature context and feature importance plots when available.

This makes it easy to share a trained pricing model with non-technical users.

Publish the app online

If you want the fastest hosted flow, use:

automl.publish_app()

This helper will:

  • sign you in through platform.mljar.com,
  • create the app URL,
  • upload the generated files,
  • and print the final app address.

Using platform.mljar.com is simply the fastest path.

Host it on your own server

If you want full control, you can host the generated Mercury app yourself:

  1. generate the app with automl.app(),
  2. install dependencies on your server,
  3. run Mercury,
  4. deploy with your own infrastructure.

Summary

After training a regression model with AutoML, you can immediately turn it into an interactive app.

That gives you a practical path from:

  • trained model,
  • to browser-based demo,
  • to a shareable tool for other people.