Create Python apps with IBM Watson and IBM Bluemix

This course is for Python developers who want to learn more about IBM Bluemix and Watson cognitive services. The labs in this course show you how to develop Python applications in both the Flask and Django web frameworks.

To be successful in this course, you should understand how to create Python/Django applications in a Python Web Framework. If you use a framework other than Django or Flask, you must modify the code in the lab.

To create cloud applications in Python, you can use Python boilerplates or runtimes in IBM Bluemix. You can modify that code locally in your own environment and push it back to Bluemix.

This lab shows you how to create your first Python and Django application on IBM® Bluemix® that consumes Watson services. You’ll need a working knowledge Python programming.

First, let’s explore the Python runtimes that are available on Bluemix.
Open a web browser and open the Bluemix console on https://console.ng.bluemix.net/.

You will initially create your Django projects and application outside of Bluemix. That way you will have more control over the project structure. You can start from either the flask boilerplate or the Python runtime.

1. Create a manifest.yml file. Put this file in the project directory, which is the directory where your manage.py file is stored.

You won’t need the Watson services until you start using them, but you should install them now so that you’re ready to use them later.

1. Open a command prompt and connect to the appropriate Bluemix data center:

In this section, you’ll use the Watson Language Translation service to detect the language. This simple use case will show you how to get started with the watson-developer-cloud Python SDK.

You now have your shell application and can add the Watson service.
In Bluemix, add or bind the Watson Language Translation service. You don’t need to restart the application for now.

That’s it. You should have a working application. In a subsequent lab, you will add a REST API intermediary in the Django code between the form and the translation service, primarily to act as a buffer for actions that might take a long time. This buffer will allow the application to further process the response.

The Watson Language Translation service provides domain-specific translation for several domains that provide translation for different languages. The current domains are:

This lab shows you how to add Watson services into the Django application that you created in the first lab.
You will create an application that can check which language is being used. In this lab, you will add code that:

Make all the changes for this step in the view/wl.py file.

You should now have an application that detects language and determines whether it can be translated to English. If the language is detected, the application translates and then classifies the text.

The Watson Language Translation service provides domain-specific translation for several domains that provide translation for different languages. The current domains are:

You can use the Natural Language Classifier service in your applications to understand the language of short textual statements and questions, and then predict how best to classify, or process that text.

This lab shows you how to add more Watson services to the Django application that you created in the first two labs.

Use the Natural Language Classifier toolkit to create your classifications:
https://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/nl-classifier/tool_overview.shtml

1. Find the credentials for your Natural Language Classifier service on Bluemix.
Import the Natural language classifier service from the Watson Developer Cloud SDK:

You should now have an application that detects a language and determines whether it can translate that language into English.

AlchemyAPI offers two categories of services that enable businesses and developers to build cognitive applications that understand the content and context within text: AlchemyLanguage and AlchemyData News.

All Watson Developer Cloud services are exposed as REST APIs. This allows them to be readily consumed and incorporated into your applications.

In this lab, you’ll learn how to create a REST API that wraps the hybrid service that you created in the first three labs.

Now that you created a function that both translates and classifies, you will make that function available as a REST API.

You should understand how to work in IBM Bluemix to create basic, cognitive Python applications that use either the Flask or Django frameworks. You should be able to run the application inside or outside of Bluemix.