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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Integrating the Dialogflow agent

Now that we've created a very basic user interface for our application, we will integrate the Dialogflow agent with the application so that the text that's entered by the user is responded to in real time by the agent. Follow these steps:

  1. To integrate Dialogflow in the application, we will use the Flutter plugin called flutter_dialogflow.
To explore this plugin, please go to https://pub.dartlang.org/packages/flutter_dialogflow.

Add the dependency to the plugin inside the pubspec.yaml file:

dependencies:
flutter_dialogflow: ^0.1.0
  1. Next, we need to install the dependency. This can be done either using the $ flutter pub get command-line argument or by clicking the option that appears on the screen. Here, we will use dialogflow_v2 so let's import the package inside our chat_screen.dart file:
import 'package...
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