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2.9 KiB
2.9 KiB
TypeScript typings for Prediction API v1.6
Lets you access a cloud hosted machine learning service that makes it easy to build smart apps For detailed description please check documentation.
Installing
Install typings for Prediction API:
npm install @types/gapi.client.prediction@v1.6 --save-dev
Usage
You need to initialize Google API client in your code:
gapi.load("client", () => {
// now we can use gapi.client
// ...
});
Then load api client wrapper:
gapi.client.load('prediction', 'v1.6', () => {
// now we can use gapi.client.prediction
// ...
});
Don't forget to authenticate your client before sending any request to resources:
// declare client_id registered in Google Developers Console
var client_id = '',
scope = [
// View and manage your data across Google Cloud Platform services
'https://www.googleapis.com/auth/cloud-platform',
// Manage your data and permissions in Google Cloud Storage
'https://www.googleapis.com/auth/devstorage.full_control',
// View your data in Google Cloud Storage
'https://www.googleapis.com/auth/devstorage.read_only',
// Manage your data in Google Cloud Storage
'https://www.googleapis.com/auth/devstorage.read_write',
// Manage your data in the Google Prediction API
'https://www.googleapis.com/auth/prediction',
],
immediate = true;
// ...
gapi.auth.authorize({ client_id: client_id, scope: scope, immediate: immediate }, authResult => {
if (authResult && !authResult.error) {
/* handle succesfull authorization */
} else {
/* handle authorization error */
}
});
After that you can use Prediction API resources:
/*
Submit input and request an output against a hosted model.
*/
await gapi.client.hostedmodels.predict({ hostedModelName: "hostedModelName", project: "project", });
/*
Get analysis of the model and the data the model was trained on.
*/
await gapi.client.trainedmodels.analyze({ id: "id", project: "project", });
/*
Delete a trained model.
*/
await gapi.client.trainedmodels.delete({ id: "id", project: "project", });
/*
Check training status of your model.
*/
await gapi.client.trainedmodels.get({ id: "id", project: "project", });
/*
Train a Prediction API model.
*/
await gapi.client.trainedmodels.insert({ project: "project", });
/*
List available models.
*/
await gapi.client.trainedmodels.list({ project: "project", });
/*
Submit model id and request a prediction.
*/
await gapi.client.trainedmodels.predict({ id: "id", project: "project", });
/*
Add new data to a trained model.
*/
await gapi.client.trainedmodels.update({ id: "id", project: "project", });