deprecate mlkit

mlkit/README.md

@@ -1,83 +1,7 @@
-UPDATE: On June 3, 2020, we started offering ML Kit's on-device APIs through a new standalone SDK. Cloud APIs, AutoML Vision Edge, and custom model deployment will continue to be available via Firebase Machine Learning. [Learn more](https://developers.google.com/ml-kit)
+The Firebase ML Vision SDK is now deprecated.
 
-ML Kit for Firebase Quickstart
-==============================
+These [docs](https://firebase.google.com/docs/ml/recognize-text) describe how, as an alternative to the deprecated SDK, you can call Cloud Vision APIs using Firebase Auth and Firebase Functions to allow only authenticated users to access the API.
 
-The ML Kit for Firebase Android Quickstart app demonstrates how to use the
-various features of ML Kit to add machine learning to your application.
+These [codelabs](https://firebase.google.com/docs/ml/codelabs) describe how you can train and deploy your Custom Models.
 
-**NOTE:** A sample application using the same library, but with a more
-sophisticated UI (it uses [Jetpack
-Libraries](https://developer.android.com/jetpack) and follows the [Material
-Design guidelines for ML](https://material.io/collections/machine-learning/)),
-can be found [here](https://github.com/firebase/mlkit-material-android). This
-may be helpful when integrating ML Kit into real-world applications.
-
-Introduction
-------------
-
-- [Read more about ML Kit for Firebase](https://firebase.google.com/docs/ml-kit/)
-
-Getting Started
----------------
-
-- [Add Firebase to your Android Project](https://firebase.google.com/docs/android/setup).
-- Run the sample on an Android device.
-- Choose LivePreviewActivity to see a demo of the following APIs:
-  - Face detection
-  - Text recognition (on-device)
-  - Barcode scanning
-  - Image labeling (on-device)
-  - Landmark recognition
-  - Custom model (Labeled "Classification"). The custom model used in this
-    sample, MobileNet_v1, is already included as a local asset in the project.
-    To use this sample with a hosted model, follow the directions under the
-    "Hosting a Custom Model" section of this readme.
-- Choose StillImageActivity to see a demo of the following:
-  - Image labeling (Cloud)
-  - Landmark recognition (Cloud)
-  - Text recognition (Cloud)
-  - Document text recognition (Cloud)
-
-Result
------------
-<img src="app/src/screen.png" height="534" width="300"/>
-
-Hosting a Custom Model
------------
-- [Download](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)
-  the TensorFlow Lite custom model we are using in this sample.
-- Go to the Firebase console.
-- Select your project.
-- Select ML Kit under the DEVELOP section in the left hand navigation.
-- Click on the CUSTOM tab.
-- Click on Add another model and use "mobilenet_v1" as the name.
-- Click BROWSE and upload the mobilenet_v1_1.0_224_quant.tflite file you
-  downloaded earlier.
-- Click PUBLISH.
-
-Support
--------
-
-- [Stack Overflow](https://stackoverflow.com/questions/tagged/firebase-mlkit)
-- [Firebase Support](https://firebase.google.com/support/).
-
-License
--------
-
-Copyright 2018 Google, Inc.
-
-Licensed to the Apache Software Foundation (ASF) under one or more contributor
-license agreements.  See the NOTICE file distributed with this work for
-additional information regarding copyright ownership.  The ASF licenses this
-file to you under the Apache License, Version 2.0 (the "License"); you may not
-use this file except in compliance with the License.  You may obtain a copy of
-the License at
-
-  http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the
-License for the specific language governing permissions and limitations under
-the License.
+UPDATE: On June 3, 2020, we started offering ML Kit's on-device APIs through a new standalone SDK. Cloud APIs, AutoML Vision Edge, and custom model deployment will continue to be available via Firebase Machine Learning. [Learn more](https://developers.google.com/ml-kit/guides)