Use R Shiny app to create SIR model.ipynb

Kernel: watsonx-ai-samples-py-312

Use R Shiny app to create SIR model with ibm-watsonx-ai

This notebook contains steps and code to demonstrate support of external R Shiny application code with ibm-watsonx-ai library available in PyPI repository.

Some familiarity with Python is helpful. This notebook uses Python 3.12.

Learning goals

The learning goals of this notebook are:

• Persist a R Shiny app in in watsonx.ai repository.

• Deploy application for online scoring using client library.

• Score sample records using client library.

Contents

This notebook contains the following parts:

1. Setup

2. Application upload

3. Web service creation

4. Clean up

5. Summary and next steps

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

• Contact with your Cloud Pak for Data administrator and ask them for your account credentials

Install dependencies

Note: ibm-watsonx-ai documentation can be found here.

%pip install -U wget | tail -n 1
%pip install -U ibm-watsonx-ai | tail -n 1

Successfully installed wget-3.2
Successfully installed anyio-4.9.0 certifi-2025.4.26 charset-normalizer-3.4.2 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 ibm-cos-sdk-2.14.1 ibm-cos-sdk-core-2.14.1 ibm-cos-sdk-s3transfer-2.14.1 ibm-watsonx-ai-1.3.13 idna-3.10 jmespath-1.0.1 lomond-0.3.3 numpy-2.2.5 pandas-2.2.3 pytz-2025.2 requests-2.32.2 sniffio-1.3.1 tabulate-0.9.0 typing_extensions-4.13.2 tzdata-2025.2 urllib3-2.4.0

Define credentials

Authenticate the watsonx.ai Runtime service on IBM Cloud Pak for Data. You need to provide the admin's username and the platform url.

username = "PASTE YOUR USERNAME HERE"
url = "PASTE THE PLATFORM URL HERE"

Use the admin's api_key to authenticate watsonx.ai Runtime services:

import getpass
from ibm_watsonx_ai import Credentials

credentials = Credentials(
    username=username,
    api_key=getpass.getpass("Enter your watsonx.ai API key and hit enter: "),
    url=url,
    instance_id="openshift",
    version="5.2",
)

Alternatively you can use the admin's password:

import getpass
from ibm_watsonx_ai import Credentials

if "credentials" not in locals() or not credentials.api_key:
    credentials = Credentials(
        username=username,
        password=getpass.getpass("Enter your watsonx.ai password and hit enter: "),
        url=url,
        instance_id="openshift",
        version="5.2",
    )

Create APIClient instance

from ibm_watsonx_ai import APIClient

client = APIClient(credentials)

Working with spaces

First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use {PLATFORM_URL}/ml-runtime/spaces?context=icp4data to create one.

• Click New Deployment Space

• Create an empty space

• Go to space Settings tab

• Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: Assign space ID below

space_id = "PASTE YOUR SPACE ID HERE"

You can use list method to print all existing spaces.

client.spaces.list(limit=10)

To be able to interact with all resources available in Watson Machine Learning, you need to set space which you will be using.

client.set.default_space(space_id)

'SUCCESS'

2. Application upload

In this section you will learn how to store downloaded application as an asset.

Download an R Shiny app

import os
import wget

filename = "app.R.zip"
url = "https://github.com/IBM/watsonx-ai-samples/raw/master/cpd5.2/models/r_shiny/app.R.zip"
if not os.path.isfile(filename):
    filename = wget.download(url)

Upload application as data asset

shiny_details = client.shiny.store(
    meta_props={
        client.shiny.ConfigurationMetaNames.NAME: "R Shiny app - SIR",
    },
    file_path=filename,
)
shiny_asset_id = client.shiny.get_id(shiny_details)

Creating Shiny asset...
SUCCESS

You can now see that the application is saved in watsonx.ai

client.shiny.list(limit=5)

3. Create online deployment

You can use commands bellow to create online deployment for stored application (web service).

deployment = client.deployments.create(
    artifact_id=shiny_asset_id,
    meta_props={
        client.deployments.ConfigurationMetaNames.NAME: "Deployment R Shiny's SIR",
        client.deployments.ConfigurationMetaNames.R_SHINY: {
            "authentication": "anyone_with_url"
        },
        client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {
            "name": "S",
            "num_nodes": 1,
        },
    },
)

######################################################################################

Synchronous deployment creation for id: 'dd053eb9-b8fc-4e98-9fad-3bae97f8fc37' started

######################################################################################


initializing
Note: rshiny_url is deprecated and will be removed in a future release. Use serving_urls instead.
.........
ready


-----------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_id='145cf352-3d40-40f5-a460-adb6a1a4ac4c'
-----------------------------------------------------------------------------------------------

Get deployments details

deployment_id = client.deployments.get_id(deployment)
client.deployments.get_details(deployment_id)

4. Clean up

If you want to clean up all created assets:

• experiments

• trainings

• pipelines

• model definitions

• models

• functions

• deployments

please follow up this sample notebook.

5. Summary and next steps

You successfully completed this notebook! You learned how to use Watson Machine Learning for SPSS model deployment and scoring.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Author

Mateusz Szewczyk, Software Engineer at watsonx.ai.

Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License.