Want to know how? Read the blog in detail below.
We are experimenting with the Machine Learning Models in Flutter-based Mobile Apps! Want to know how?
Read the blog in detail below:
Basic question that arises is whether Machine Learning is supported by the Flutter platform?
The answer is “YES”.
There is a Firebase ML kit for Flutter by Google. Or you can say it is kind of an Umbrella plugin that enables the flutter-based
apps to use the ML kit.
Do you know that we can assist people to manage their time using these apps? We can even solve the environmental problems.
So let’s get to the topic, How will we implement it?
Now let’s come to our focal point which is how we will implement ML models in the Flutter Apps:
We will start with an overview of machine learning and its various applications. Then we will delve into the specifics of building
machine learning models using the Python library I.e TensorFlow and sci-kit-learn.
I will also introduce the topic of flutter and its features. I will make you understand in a very easy way how we are going to
integrate the Python model into the Flutter application.
Introduction to Machine Learning:
Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to improve their performance in tasks through experience. These algorithms and models are designed to learn from data and make predictions or decisions without explicit instructions.
Applications of Machine Learning:
Introduction to Flutter:
Flutter is Google’s Mobile SDK to build native iOS and Android apps from a single codebase. When building applications with Flutter everything is towards Widgets – the blocks with which the Flutter apps are built. The User Interface of the app is composed of many simple widgets, each of them handling one particular job. That is the reason why Flutter developers tend to think of their Flutter app as a tree of widgets.
Features of Flutter:
- Modern and reactive framework.
- Very easy to learn.
- Fast development.
- Beautiful and fluid user interfaces.
- Huge widget catalogue.
- Runs the same UI for multiple platforms.
- High-performance application.
What is the process for integrating ML models into Flutter apps?
Let’s take a real-life example i.e Face Based Attendance system in the Mobile Application:
In this example, we are going to use the ML Kit. First, we will know about the features.
- Capture the Image
- Preprocess Image
- Identify items with labels, index, and confidence.
The very basic steps we will follow:
- We will integrate the ML Kit and libraries with the dependencies in the flutter
- We will implement and experiment the premade smart features like:
- Text recognition
- Face Detection
- Object Detection & Tracking
- Barcode Scanning
- On-Device Translation
- Smart Reply
- Image Labelling
- Landmark Recognition
- Language Identification
- We will add the data capture controls.
- Will analyze the result.
Benefits of using:
- powered by different Google Cloud Platform services.
- Flutter framework is a powerful & user-friendly platform and can add cutting-edge AI and ML capabilities.
- makes it easier to add AI and ML-related features.
- simply use this predefined AI model and implement the AI feature into the app just by using its APIs.
Limitation of implementation:
Machine learning is a powerful tool that can be used to create intelligent applications. However, there are some limitations to implementing ML in Flutter. One of the main limitations is that the models that are trained can become rather large due to the nature of machine learning. Although this is not a problem when they run on a server, it can be limiting when running these on a client1.
Another limitation is that there are not many libraries available for implementing machine learning in Flutter. This means that developers must write their code for implementing machine learning algorithms.
What other ML + Flutter apps can be created besides the ones mentioned above?
You can create apps like:
- Face recognition Apps
- Status of the device (Online/Offline)
- Multiplayer games.
- Clone apps like Spotify, WhatsApp, etc.
- Virtual e-commerce stores
- Chat Apps
- OCR bases Apps
- Barcode scanning Apps
Examples of successful ML + Flutter apps:
- Mental Health Diagnosis & Consulting App
Face detection app
Detection of the species of the bird
- Text Extractor App
- Farming prediction App
- Proctor App for reducing exam cheating.
- Virtual Beauty Product App
- Virtual Clothing App
- NASA Space App for detecting Forest Fire
- Simple OCR Apps Travel/Expense Management
In conclusion, Flutter provides a great platform for building machine-learning apps. By integrating ML model, you can build powerful and intelligent apps that deliver a great user experience. By optimizing your app for performance, you can ensure that your app runs smoothly and efficiently. Give it a try and see what you can create!
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