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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

Creating the UI for the conversational application

We will start by creating the basic user interface for the application using some hardcoded texts to test if the UI is updating properly or not. Then, we will integrate the Dialogflow agent so that it can answer queries and tell the user their lucky number, followed by the addition of a mic option so that we can utilize the speech to text functionality.

The overall widget tree of the application will look as follows:

Now, let's discuss the implementation of each of these widgets in detail.

Creating the Text Controller

First of all, let's create a StatefulWidget called ChatScreen in a new dart file called chat_screen.dart. Now, follow these steps:

  1. Create...
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