This sample script shows how you can use the Firebase Admin SDK to manage your Firebase-hosted ML models.

Also see the Firebase ML API Tutorial Colab/Jupyter notebook.

1. Install the Admin SDK (probably in a virtual environment):

   $ pip install -U pip setuptools
   $ pip install firebase_admin
   

2. Clone the quickstart repository and change to the machine-learning   directory:

   $ git clone https://github.com/firebase/quickstart-python.git
   $ cd quickstart-python/machine-learning
   $ chmod u+x manage-ml.py  # Optional
   

3. If you don't already have a Firebase project, create a new project in the Firebase console. Then, open your project in the Firebase console and do the following:

   1. On the Settings page, create a service account and download the service account key file. Keep this file safe, since it grants administrator access to your project.
   2. On the Storage page, enable Cloud Storage. Take note of your default bucket name (or create a new bucket for ML models.)
   3. On the ML Kit page, click Get started if you haven't yet enabled ML Kit.

4. In the Google APIs console, open your Firebase project and enable the Firebase ML API.

5. At the top of manage-ml.py, set the SERVICE_ACCOUNT_KEY and STORAGE_BUCKET:

   SERVICE_ACCOUNT_KEY = '/path/to/your/service_account_key.json'
   STORAGE_BUCKET = 'your-storage-bucket'
   

$ ./manage-ml.py list
fish_detector    8716935   vision
barcode_scanner  8716959   vision
smart_reply      8716981   natural_language
$ ./manage-ml.py new ~/yak.tflite yak_detector --tags vision,experimental
Uploading model to Cloud Storage...
Model uploaded and published:
yak_detector     8717019   experimental, vision
$ ./manage-ml.py update 8717019 --remove_tags experimental
$ ./manage-ml.py delete 8716959
$ ./manage-ml.py list
fish_detector    8716935   vision
smart_reply      8716981   natural_language
yak_detector     8717019   vision
$