Deploying your Language Model with Google Cloud

Note: Ensure your model is saved in a folder alongside all necessary files (.pkl) needed during inference. Before we create the interface, let's create the REST API the interface will be interacting with, using Flask and Flask_restful. In the code snippet above, we can see the # digit comment. This represents the step in which we'll analyze the code: In the code above, the route "/" is used to load the page to render our interface, and the "/translate" route is used to accept the incoming text and also return the translated text. The class Translate is used to create the Rest API.