Flutterexperts

Machine Learning is a field of study that allows computers to learn without being explicitly programmed. However, we’ll see it further in brief.

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Table of Contents:-

Machine Learning — Introduction

Flutter with Machine Learning

Installation

Create Model

Steps to create a model

Code Implementation

Final Output

Conclusion

Github Link

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Machine Learning:-

Machine learning has a rich history, dating back to the previous century, and it has become an integral part of our daily lives. It is so pervasive that we often encounter it without even realizing it. Take smartphones, for example. They are called “smart” because they continuously learn from our interactions and adapt to our preferences. They gather information about us and use it to provide personalized experiences. This extends beyond smartphones and permeates various technologies. Machine learning algorithms are constantly evolving, just like humans, as they analyze data and uncover patterns to make predictions and improve decision-making. Its reach is extensive, impacting numerous areas of technology and enhancing our lives in remarkable ways.

Machine Learning is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. It involves the development of mathematical and statistical models that can analyze large amounts of data, identify patterns, and make informed predictions or take action.

In machine learning, computers are trained using example data, called the training set, to learn patterns and relationships within the data. The models are then used to make predictions or decisions on new, unseen data.

Machine learning has various applications, including but not limited to image and speech recognition, natural language processing, recommendation systems, fraud detection, autonomous vehicles, and predictive analytics. It plays a crucial role in enabling computers to learn and improve from experience, leading to intelligent and data-driven decision-making.

Machine learning is widely used in various aspects of our daily lives. Here are some common examples:

— Virtual Assistants: Virtual assistants like Siri, Google Assistant, and Alexa utilize machine learning algorithms to understand and respond to voice commands, provide recommendations, and perform tasks based on user interactions.

— Recommendation Systems: Platforms like Netflix, Amazon, and Spotify employ machine learning to analyze user preferences and behavior, suggesting personalized movie or TV show recommendations, product recommendations, and customized playlists.

— Fraud Detection: Financial institutions use machine learning to detect and prevent fraudulent activities by analyzing patterns and anomalies in transactions, identifying suspicious behavior, and alerting users or taking necessary actions.

— Image and Speech Recognition: Machine learning powers facial recognition systems used in unlocking smartphones or tagging people in photos. It also enables speech recognition technologies like transcription services, voice assistants, and voice-controlled devices.

— Natural Language Processing (NLP): NLP techniques, a branch of machine learning, are used in language translation services, sentiment analysis, chatbots, and voice-enabled interfaces, making communication more efficient and intuitive.

— Healthcare: Machine learning algorithms are utilized in medical imaging analysis, disease diagnosis, personalized medicine, and drug discovery, aiding in early detection, treatment planning, and research advancements.

— Social Media and Advertising: Social media platforms employ machine learning algorithms to personalize content feeds, target advertisements based on user preferences and behavior, and detect spam or abusive content.

— Autonomous Vehicles: Machine learning plays a crucial role in autonomous vehicles, enabling object detection, collision avoidance, lane detection, and decision-making based on real-time data from sensors.

Flutter with Machine Learning:-

Flutter with machine learning refers to the combination of the Flutter framework and machine learning techniques in app development. Flutter is an open-source UI framework developed by Google for building cross-platform applications. It allows developers to create beautiful and performant apps for multiple platforms using a single codebase.

When combined with machine learning, Flutter enables the integration of AI capabilities into mobile apps. Developers can leverage machine learning libraries, APIs, and models to incorporate features like image recognition, natural language processing, sentiment analysis, recommendation systems, and more.

By utilizing Flutter with machine learning, developers can create powerful and intelligent mobile applications that can understand, analyze, and make predictions based on user data or real-time inputs. This combination opens up possibilities for developing smart and personalized apps that can provide advanced functionality, improve user experiences, and automate tasks using machine learning algorithms and models.

Installation:-

There are so many packages available for implementing machine learning in Flutter projects. Today we are going to use “tflite”.

Now let’s get through the steps to Import tfilte in our project.

Import the latest version of the tflite package in pubspec.yaml file

dependencies:
tflite: ^1.1.2

Go to >android >app >build.gradle file, add aaptOptions block and change minSdkVersion to 19

aaptOptions {
noCompress 'tflite'
noCompress 'lite'
}

defaultConfig {
// TODO: Specify your own unique Application ID (https://developer.android.com/studio/build/application-id.html).
applicationId "com.example.otp_autofill_demo"
// You can update the following values to match your application needs.
// For more information, see: https://docs.flutter.dev/deployment/android#reviewing-the-build-configuration.
minSdkVersion 19
targetSdkVersion flutter.targetSdkVersion
versionCode flutterVersionCode.toInteger()
versionName flutterVersionName
}

Add internet permission to android>app>src>main>AndroidManifest.xml

<uses-permission android:name="android.permission.INTERNET" />

Create model:-

A model refers to a pre-trained machine learning model that is used within a Flutter application to perform tasks such as image recognition, natural language processing, or data analysis.

The model file contains the learned parameters and structure of the trained model. It is usually saved with a specific file extension, such as .tflite for TensorFlow Lite models. This file is then loaded and used within the Flutter app to perform predictions or inferences on new data.

There are so many ways to create your custom TensorFlow Lite model. I preferred Teachable Machine. It is a web-based tool, by Google that makes it fast and easy to create machine learning models without any expertise or coding accessible to everyone.

Steps to create a model:-

— Go to https://teachablemachine.withgoogle.com/

— In the demo project below, I’ll showcase the implementation of an image recognition feature using machine learning. So I selected the “Image Project” option.

— Provide samples of all the scenarios in which you want things/yourselves to be recognized, and finally train your model. Afterward, download your TensorFlow Lite model with floating point conversion type.

— Extract your files and paste them into assets/ folders.

— Don’t forget to add an assets section in your pubspec.yaml file, like this

flutter:
uses-material-design: true
assets:
- assets/images/
- assets/labels.txt
- assets/model_unquant.tflite

Now, let’s dive into the code implementation to determine if a person is wearing glasses. Below, you’ll find the essential code snippets to achieve this functionality.

Code Implementation:-

main. dart

void main() =>
runApp(const MaterialApp(
home: MyApp(),
debugShowCheckedModeBanner: false,
));

MyApp.dart

class MyApp extends StatefulWidget {
const MyApp({super.key});

@override
_MyAppState createState() => _MyAppState();
}

class _MyAppState extends State<MyApp> {
String backgroundImage = "assets/images/background.jpg";
ImagePicker imagePicker = ImagePicker();
File? _image;
double? _imageWidth;
double? _imageHeight;
var _recognitions;

//needs to be called at very first
loadModel() async {
Tflite.close();
try {
String? res = await Tflite.loadModel(
model: "assets/model_unquant.tflite",
labels: "assets/labels.txt",
numThreads: 2,
isAsset: true,
useGpuDelegate: false);
} on PlatformException {
debugPrint("Failed to load the model");
}
}

// run prediction using TFLite on given image
Future predict(File image) async {
var recognitions = await Tflite.runModelOnImage(
path: image.path,
imageMean: 0.0,
imageStd: 255.0,
numResults: 2,
threshold: 0.6,
asynch: true // defaults to true
);

debugPrint("Recognitions:--$recognitions");

setState(() {
_recognitions = recognitions;
});
}

// send image to predict method selected from gallery or camera
sendImage(File image) async {
// if(image == null) return;
await predict(image);

// get the width and height of selected image
FileImage(image)
.resolve(const ImageConfiguration())
.addListener((ImageStreamListener((ImageInfo info, bool _) {
setState(() {
_imageWidth = info.image.width.toDouble();
_imageHeight = info.image.height.toDouble();
_image = image;
});
})));
}

// select image from gallery
selectFromGallery() async {
var image = await imagePicker.pickImage(source: ImageSource.gallery);
if (image == null) return;
setState(() {});
File fileImage = await CommonUtils.convertXFileToFile(image);
sendImage(fileImage);
}

// select image from camera
selectFromCamera() async {
var image = await imagePicker.pickImage(source: ImageSource.camera);
if (image == null) return;
setState(() {});
File fileImage = await CommonUtils.convertXFileToFile(image);
sendImage(fileImage);
}

@override
void initState() {
super.initState();

loadModel().then((val) {
setState(() {});
});
}

Widget printValue(rcg) {
//Print data in your screen accordingly.
}

@override
Widget build(BuildContext context) {
Size size = MediaQuery
.of(context)
.size;

double finalW;
double finalH;

if (_imageWidth == null && _imageHeight == null) {
finalW = size.width;
finalH = size.height;
} else {
double ratioW = size.width / _imageWidth!;
double ratioH = size.height / _imageHeight!;

finalW = _imageWidth! * ratioW * .85;
finalH = _imageHeight! * ratioH * .50;
}

return Scaffold(...);
}

The complete code for this implementation can be found on the GitHub link below. Refer master branch.

Final Output:-

Conclusion:-

In this article, we explored the exciting intersection of Flutter and machine learning, empowering you to create projects that leverage the power of artificial intelligence. I hope you found this journey insightful and enjoyable. Now, armed with the knowledge of Flutter and machine learning, unleash your creativity and embark on your unique ventures in AI.

❤ ❤ Thanks for reading this article ❤❤

If I got something wrong? Let me know in the comments. I would love to improve.

Clap 👏 If this article helps you.

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GitHub Link:-

GitHub – flutter-devs/flutter_ml at master
A new Flutter project. This project is a starting point for a Flutter application. A few resources to get you started…github.com

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