Book Image

Flutter Cookbook

By : Simone Alessandria, Brian Kayfitz
4 (1)
Book Image

Flutter Cookbook

4 (1)
By: Simone Alessandria, Brian Kayfitz

Overview of this book

“Anyone interested in developing Flutter applications for Android or iOS should have a copy of this book on their desk.” – Amazon 5* Review Lauded as the ‘Flutter bible’ for new and experienced mobile app developers, this recipe-based guide will teach you the best practices for robust app development, as well as how to solve cross-platform development issues. From setting up and customizing your development environment to error handling and debugging, The Flutter Cookbook covers the how-tos as well as the principles behind them. As you progress, the recipes in this book will get you up to speed with the main tasks involved in app development, such as user interface and user experience (UI/UX) design, API design, and creating animations. Later chapters will focus on routing, retrieving data from web services, and persisting data locally. A dedicated section also covers Firebase and its machine learning capabilities. The last chapter is specifically designed to help you create apps for the web and desktop (Windows, Mac, and Linux). Throughout the book, you’ll also find recipes that cover the most important features needed to build a cross-platform application, along with insights into running a single codebase on different platforms. By the end of this Flutter book, you’ll be writing and delivering fully functional apps with confidence.
Table of Contents (17 chapters)
About Packt

How it works...

You can use the tflite_flutter package when you want to integrate a TensorFlow Lite model in your Flutter apps. The advantages of using this package include the following:

  • No need to write any platform-specific code.
  • It can use any tflite model.
  • It runs on the device itself (no need to connect to a server).

A TensorFlow Lite model uses the .tflite extension. You can also convert existing TensorFlow models into TensorFlow Lite models. The procedure is available at

In the assets folder of the app, you placed two files: the tflite model and a vocabulary text file. The vocabulary contains 10,000 words that are used by the model to retrieve the positive and negative sentiments.

You can use this classification model, and all the models available in TensorFlow Hub, in your apps as they are released with an Apache 2.0 license (details here:

Before classifying the string...