This sample demonstrates how to automatically publish models to Firebase ML for each TensorFlow Lite file that is uploaded to Firebase Storage.

See file functions/index.js for the model publishing code.

The dependencies are listed in functions/package.json.

The function triggers on upload of any file to your Firebase project's default Cloud Storage bucket.

To deploy and test the sample:

• Create a Firebase project on the Firebase Console and visit the Storage tab.
• Clone this repo: git clone https://github.com/firebase/functions-samples.
• Open this sample's directory: cd functions-samples/publish-model
• Setup your project by running firebase use --add and select the project you had created.
• Install dependencies in the functions directory: cd functions; npm install; cd -
• Deploy your project using firebase deploy
• Go to your project's Cloud Console > IAM & admin > IAM, Find the App Engine default service account.
• Add the Service Account Token Creator role to that member. This will allow your app to create signed public URLs to the models.
• Add the Firebase ML Kit Admin role to that member. This will allow your app to manage Firebase ML models.
• Add the Firebase Admin SDK Administrator Service Agent role to that member. This will allow your app to manage models with the Firebase Admin SDK.
• Go to the Firebase Console Storage tab and upload a Tensorflow Lite model (*.tflite). After a short time a model with same file name will be published to Firebase ML.
• Go to the Firebase Console ML Kit (Custom) tab and see that a new model with file name has been created.