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docs(samples): updated samples code to use async/await and tests to use mocha #241

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Nov 18, 2018
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docs(samples): updated samples code to use async/await and tests to use mocha #241

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6ba3bcc
updated samples code to use async await
vijay-qlogic Oct 24, 2018
aee3e5a
added code review updates
vijay-qlogic Oct 25, 2018
d1cec40
converted samples test from ava to mocha
vijay-qlogic Oct 27, 2018
a823282
updates samples code to use async await for productSearch
vijay-qlogic Oct 27, 2018
5ffa0a8
update automl samples to use async/await and mocha instead of ava
vijay-qlogic Oct 27, 2018
1d76f10
Added fix for test failuers
vijay-qlogic Nov 1, 2018
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2 changes: 2 additions & 0 deletions samples/automl/.eslintrc.yml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,6 @@
---
env:
mocha: true
rules:
no-console: off
node/no-missing-require: off
211 changes: 89 additions & 122 deletions samples/automl/automlVisionDataset.js
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,12 @@

`use strict`;

function createDataset(projectId, computeRegion, datasetName, multiLabel) {
async function createDataset(
projectId,
computeRegion,
datasetName,
multiLabel
) {
// [START automl_vision_create_dataset]
const automl = require(`@google-cloud/automl`).v1beta1;

Expand Down Expand Up @@ -58,31 +63,26 @@ function createDataset(projectId, computeRegion, datasetName, multiLabel) {
};

// Create a dataset with the dataset metadata in the region.
client
.createDataset({parent: projectLocation, dataset: myDataset})
.then(responses => {
const dataset = responses[0];

// Display the dataset information.
console.log(`Dataset name: ${dataset.name}`);
console.log(`Dataset id: ${dataset.name.split(`/`).pop(-1)}`);
console.log(`Dataset display name: ${dataset.displayName}`);
console.log(`Dataset example count: ${dataset.exampleCount}`);
console.log(`Image Classification type:`);
console.log(
`\t ${dataset.imageClassificationDatasetMetadata.classificationType}`
);
console.log(`Dataset create time:`);
console.log(`\tseconds: ${dataset.createTime.seconds}`);
console.log(`\tnanos: ${dataset.createTime.nanos}`);
})
.catch(err => {
console.error(err);
});
const [dataset] = await client.createDataset({
parent: projectLocation,
dataset: myDataset,
});
// Display the dataset information.
console.log(`Dataset name: ${dataset.name}`);
console.log(`Dataset id: ${dataset.name.split(`/`).pop(-1)}`);
console.log(`Dataset display name: ${dataset.displayName}`);
console.log(`Dataset example count: ${dataset.exampleCount}`);
console.log(`Image Classification type:`);
console.log(
`\t ${dataset.imageClassificationDatasetMetadata.classificationType}`
);
console.log(`Dataset create time:`);
console.log(`\tseconds: ${dataset.createTime.seconds}`);
console.log(`\tnanos: ${dataset.createTime.nanos}`);
// [END automl_vision_create_dataset]
}

function listDatasets(projectId, computeRegion, filter) {
async function listDatasets(projectId, computeRegion, filter) {
// [START automl_vision_list_datasets]
const automl = require(`@google-cloud/automl`).v1beta1;

Expand All @@ -98,36 +98,30 @@ function listDatasets(projectId, computeRegion, filter) {
const projectLocation = client.locationPath(projectId, computeRegion);

// List all the datasets available in the region by applying filter.
client
.listDatasets({parent: projectLocation, filter: filter})
.then(responses => {
const datasets = responses[0];

// Display the dataset information.
console.log(`List of datasets:`);
datasets.forEach(dataset => {
console.log(`Dataset name: ${dataset.name}`);
console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
console.log(`Dataset display name: ${dataset.displayName}`);
console.log(`Dataset example count: ${dataset.exampleCount}`);
console.log(`Image Classification type:`);
console.log(
`\t`,
dataset.imageClassificationDatasetMetadata.classificationType
);
console.log(`Dataset create time: `);
console.log(`\tseconds: ${dataset.createTime.seconds}`);
console.log(`\tnanos: ${dataset.createTime.nanos}`);
console.log(`\n`);
});
})
.catch(err => {
console.error(err);
});
const [datasets] = await client.listDatasets({
parent: projectLocation,
filter: filter,
});
console.log(`List of datasets:`);
datasets.forEach(dataset => {
console.log(`Dataset name: ${dataset.name}`);
console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
console.log(`Dataset display name: ${dataset.displayName}`);
console.log(`Dataset example count: ${dataset.exampleCount}`);
console.log(`Image Classification type:`);
console.log(
`\t`,
dataset.imageClassificationDatasetMetadata.classificationType
);
console.log(`Dataset create time: `);
console.log(`\tseconds: ${dataset.createTime.seconds}`);
console.log(`\tnanos: ${dataset.createTime.nanos}`);
console.log(`\n`);
});
// [END automl_vision_list_datasets]
}

function getDataset(projectId, computeRegion, datasetId) {
async function getDataset(projectId, computeRegion, datasetId) {
// [START automl_vision_get_dataset]
const automl = require(`@google-cloud/automl`).v1beta1;

Expand All @@ -144,32 +138,24 @@ function getDataset(projectId, computeRegion, datasetId) {
const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);

// Get complete detail of the dataset.
client
.getDataset({name: datasetFullId})
.then(responses => {
const dataset = responses[0];

// Display the dataset information.
console.log(`Dataset name: ${dataset.name}`);
console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
console.log(`Dataset display name: ${dataset.displayName}`);
console.log(`Dataset example count: ${dataset.exampleCount}`);
console.log(
`Classification type: ${
dataset.imageClassificationDatasetMetadata.classificationType
}`
);
console.log(`Dataset create time: `);
console.log(`\tseconds: ${dataset.createTime.seconds}`);
console.log(`\tnanos: ${dataset.createTime.nanos}`);
})
.catch(err => {
console.error(err);
});
const [dataset] = await client.getDataset({name: datasetFullId});
// Display the dataset information.
console.log(`Dataset name: ${dataset.name}`);
console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
console.log(`Dataset display name: ${dataset.displayName}`);
console.log(`Dataset example count: ${dataset.exampleCount}`);
console.log(
`Classification type: ${
dataset.imageClassificationDatasetMetadata.classificationType
}`
);
console.log(`Dataset create time: `);
console.log(`\tseconds: ${dataset.createTime.seconds}`);
console.log(`\tnanos: ${dataset.createTime.nanos}`);
// [END automl_vision_get_dataset]
}

function importData(projectId, computeRegion, datasetId, path) {
async function importData(projectId, computeRegion, datasetId, path) {
// [START automl_vision_import_data]
const automl = require(`@google-cloud/automl`).v1beta1;

Expand All @@ -195,26 +181,22 @@ function importData(projectId, computeRegion, datasetId, path) {
};

// Import the dataset from the input URI.
client
.importData({name: datasetFullId, inputConfig: inputConfig})
.then(responses => {
const operation = responses[0];
console.log(`Processing import...`);
return operation.promise();
})
.then(responses => {
// The final result of the operation.
if (responses[2].done) {
console.log(`Data imported.`);
}
})
.catch(err => {
console.error(err);
});
const [operation] = await client.importData({
name: datasetFullId,
inputConfig: inputConfig,
});
console.log(`Processing import...`);

const [, , response] = await operation.promise();

// The final result of the operation.
if (response.done) {
console.log(`Data imported.`);
}
// [END automl_vision_import_data]
}

function exportData(projectId, computeRegion, datasetId, outputUri) {
async function exportData(projectId, computeRegion, datasetId, outputUri) {
// [START automl_vision_export_data]
const automl = require(`@google-cloud/automl`).v1beta1;

Expand All @@ -239,26 +221,20 @@ function exportData(projectId, computeRegion, datasetId, outputUri) {
};

// Export the data to the output URI.
client
.exportData({name: datasetFullId, outputConfig: outputConfig})
.then(responses => {
const operation = responses[0];
console.log(`Processing export...`);
return operation.promise();
})
.then(responses => {
// The final result of the operation.
if (responses[2].done) {
console.log(`Data exported.`);
}
})
.catch(err => {
console.error(err);
});
const [operation] = await client.exportData({
name: datasetFullId,
outputConfig: outputConfig,
});
const [, , response] = await operation.promise();

// The final result of the operation.
if (response.done) {
console.log(`Data exported.`);
}
// [END automl_vision_export_data]
}

function deleteDataset(projectId, computeRegion, datasetId) {
async function deleteDataset(projectId, computeRegion, datasetId) {
// [START automl_vision_delete_dataset]
const automl = require(`@google-cloud/automl`).v1beta1;

Expand All @@ -275,21 +251,12 @@ function deleteDataset(projectId, computeRegion, datasetId) {
const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);

// Delete a dataset.
client
.deleteDataset({name: datasetFullId})
.then(responses => {
const operation = responses[0];
return operation.promise();
})
.then(responses => {
// The final result of the operation.
if (responses[2].done) {
console.log(`Dataset deleted.`);
}
})
.catch(err => {
console.error(err);
});
const [operation] = await client.deleteDataset({name: datasetFullId});
const [, , response] = await operation.promise();
// The final result of the operation.
if (response.done) {
console.log(`Dataset deleted.`);
}
// [END automl_vision_delete_dataset]
}

Expand Down
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