Disclaimer: This is not an official Google product.

Organizing the issues in your GitHub repositories can be a different kind of animal, that's why you need LabelCat.

1. Install Node.js >= 8.x
2. git clone https://github.com/GoogleCloudPlatform/LabelCat
3. cd LabelCat
4. npm install
5. npm link .
6. cp defaultsettings.json settings.json (settings.json is where you customize the app)
7. Modify settings.json as necessary.

1. In the GCP Console, go to the Manage Resources page and select or create a new project:

   Go to the Manage Resources Page

2. Update settings.json to include your GCP Project ID and Compute Region.

3. Make sure that billing is enabled for your project:

   Learn How to Enable Billing

4. Enable the AutoML Natural Language APIs.

   Enable the APIs

5. Install the gcloud command line tool.

6. Follow the instructions to create a service account and download a key file.

   Create Account and Download Key File

7. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path to the Service Account key file that you downloaded when you created the Service Account. For example:

   export GOOGLE_APPLICATION_CREDENTIALS=key-file 

8. Give your new Service Account the AutoML Editor IAM role with the following commands:

   gcloud auth login gcloud config set project YOUR_PROJECT_ID gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \ --member=serviceAccount:SERVICE_ACCOUNT_NAME \ --role='roles/automl.editor' 

   replacing YOUR_PROJECT_ID with your GCP project ID and SERVICE_ACCOUNT_NAMEwith the name of your new Service Account, for example [email protected].

9. Allow the AutoML Natural Language service accounts to access your Google Cloud project resources:

   gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \ --member="serviceAccount:[email protected]" \ --role="roles/storage.admin" 

   replacing YOUR_PROJECT_ID with your GCP project ID.

10. Create a Google Cloud Storage bucket to store the documents that you will use to train your custom model. The bucket name must be in the format: YOUR_PROJECT_ID-lcm. Runy the following command to create a bucket in the us-central1 region:

    gsutil mb -p YOUR_PROJECT_ID -c regional -l `us-central1` gs://YOUR_PROJECT_ID-lcm/ 

    replacing YOUR_PROJECT_ID with your GCP project ID.

Run labelcat --help for usage information.

 labelcat <command> Commands: labelcat retrieveIssues <repoDataFilePath> Retrieves issues from a .txt file of gitHub <issuesDataFilePath> <label> repositories. Options: -a labelcat createDataset <datasetName> Create a new Google AutoML NL dataset with the specified name. Options: -m labelcat importData <issuesDataPath> <datasetId> Import the GitHub issues data from Google Cloud Storage bucket into the Google AutoML NL dataset by specifying the file's path in the bucket and the dataset ID. Options: --version Show version number [boolean] --help Show help [boolean] Examples: labelcat retrieveIssues repoData.txt issuesData.csv 'type: Retrieves issues with matching labels from list of repos bug' -a 'bug' -a 'bugger' in repoData.txt and saves the resulting information to issuesData.csv. labelcat createDataset Data Creates a new multilabel dataset with the specified name. labelcat importData gs://myproject/mytraindata.csv Imports the GitHub issues data into the dataset by 1248102981 specifying the file of issues data and the dataset ID. 

1. Create a repos.txt file with a single column list of GitHub repositories from which to collect issue data. The format should be :owner/:repository:

   Example:

   GoogleCloudPlatform/google-cloud-node GoogleCloudPlatform/google-cloud-java GoogleCloudPlatform/google-cloud-python 

2. From the project folder, run the retrieveIssues command with the path of the repository list file, path to a location to save the resulting .csv file, desired issue label, and optional alternative issue labels:

   Example:

   labelcat retrieveIssues repos.txt issues.csv "type: bug" -a "bug" 

3. Upload the resulting .csv file to your Google Cloud Storage Bucket:

   Example:

   gsutil cp repos.txt gs://YOUR_PROJECT_ID-lcm/ 

   replacing YOUR_PROJECT_ID with your GCP project ID.

1. From the project folder, run the createDataset command with the name of the dataset to create.

   Example:

   labelcat createDataset TestData 

1. Run listDataset to return a list of all AutoML NL datasets for the Google Cloud Platform project.

   Example:

   labelcat listDatasets 

1. Run importData using the Dataset ID returned by the createDataset command and the URI to the issue data .csv file.

   Example:

   labelcat importData gs://YOUR_PROJECT_ID-lcm/issues.csv 123ABCD456789 

   replacing YOUR_PROJECT_ID with your GCP project ID.

1. Run createModel using the Dataset ID and the name of the model to be created.

   Example:

   labelcat createModel 123ABCD456789 firstModel 

See CONTRIBUTING.

Copyright 2018, Google, Inc.

Licensed under the Apache License, Version 2.0

See LICENSE.