# Smart reply

<img src="../images/smart_reply.png" class="attempt-right" />

## Get started

Our smart reply model generates reply suggestions based on chat messages. The
suggestions are intended to be contextually relevant, one-touch responses that
help the user to easily reply to an incoming message.

<a class="button button-primary" href="http://download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip">Download
starter model and labels</a>

### Sample application

We have provided a pre-built APK that demonstrates the smart reply model on
Android.

Go to the
<a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/models/smartreply/g3doc">GitHub
page</a> for instructions and list of supported ops and functionalities.

## How it works

The model generates reply suggestions to conversational chat messages.

The on-device model comes with several benefits. It is:
<ul>
  <li>Fast: The model resides on the device and does not require internet connectivity. Thus, inference is very fast and has an average latency of only a few milliseconds.</li>
  <li>Resource efficient: The model has a small memory footprint on the device.</li>
  <li>Privacy-friendly: User data never leaves the device.</li>
</ul>

## Example output

<img alt="Animation showing smart reply" src="images/smart_reply.gif" />

## Read more about this

<ul>
  <li><a href="https://arxiv.org/pdf/1708.00630.pdf">Research paper</a></li>
  <li><a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/models/smartreply/">Source code</a></li>
</ul>

## Users

<ul>
  <li><a href="https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/">Gmail</a></li>
  <li><a href="https://www.blog.google/products/gmail/computer-respond-to-this-email/">Inbox</a></li>
  <li><a href="https://blog.google/products/allo/google-allo-smarter-messaging-app/">Allo</a></li>
  <li><a href="https://research.googleblog.com/2017/02/on-device-machine-intelligence.html">Smart Replies on Android Wear</a></li>
</ul>
