A Beginner’s Guide to Using Machine Learning with Flutter!

Flutter Queen✨
3 min readSep 14, 2023

In the ever-evolving world of app development, staying ahead of the curve is essential. Integrating machine learning (ML) into mobile apps has become increasingly popular, and Flutter, Google’s UI toolkit for building natively compiled applications for mobile, web, and desktop from a single codebase, provides a powerful platform to achieve this integration. In this beginner’s guide, we’ll explore how to use machine learning with Flutter to create smarter and more engaging mobile applications.

Why Combine Machine Learning with Flutter?

Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience, without being explicitly programmed. When integrated into Flutter apps, machine learning can enhance user experiences, add personalization, automate tasks, and make applications more intelligent. Here are some compelling reasons to combine ML with Flutter:

  1. Personalization: ML can help in understanding user preferences and behavior, enabling you to offer personalized content and recommendations within your app.
  2. Automation: You can automate tasks like image recognition, language translation, and data analysis, reducing manual effort and improving efficiency.
  3. Predictive Analytics: Predictive models can provide insights into future trends and user behavior, helping you make data-driven decisions.
  4. Enhanced User Experiences: ML can enable features like voice recognition, sentiment analysis, and chatbots, making your app more interactive and user-friendly.

Now that you know why combining machine learning with Flutter is beneficial, let’s explore how to get started.

Certainly! To demonstrate how to integrate machine learning into a Flutter app using the TensorFlow Lite (TFLite) package, let’s create a simple image classification app. This app will take an image from the user and use a pre-trained TensorFlow Lite model to classify the image into predefined categories. We’ll use the TFLite Flutter package to load and run the model.


Before you begin, make sure you have Flutter and the necessary dependencies set up in your project.

Step 1: Add Dependencies

sdk: flutter
tflite: # Use the latest version available

Step 2: Prepare the Model

Download a pre-trained TensorFlow Lite model for image classification, and place it in the assets folder of your Flutter project. Make sure you also have a file containing class labels, usually named labels.txtYou will go to the TensorFlow website and train your Model and download it in tlite format, and then you will put it in the assets folder and then you will know the assets in the dependencies.

Step 3: Load and Run the Model

Here’s a simplified Flutter code snippet that loads the TensorFlow Lite model and makes predictions based on an image chosen by the user:

import 'package:tflite/tflite.dart';

Future<void> loadModel() async {
// Load the TensorFlow Lite model and labels
String modelFile = 'assets/model.tflite';
String labelsFile = 'assets/labels.txt';

await Tflite.loadModel(
model: modelFile,
labels: labelsFile,

setState(() {
_isModelReady = true;

3. Make Predictions

Once the model is loaded, you can use it to make predictions. Here’s how you can add image classification logic:

Future<void> classifyImage(String imagePath) async {
var recognitions = await Tflite.runModelOnImage(
path: imagePath,
imageMean: 127.5, // Preprocessing parameters (adjust as needed)
imageStd: 127.5,
numResults: 5, // Number of results to return
threshold: 0.2, // Confidence threshold

setState(() {
_recognitions = recognitions;

4. Display Prediction Results

Finally, display the prediction results in your app’s UI. Here’s a simple way to display the top predictions:

itemCount: _recognitions.length,
itemBuilder: (context, index) {
var recognition = _recognitions[index];
return ListTile(
title: Text(recognition['label']),
subtitle: Text('${(recognition['confidence'] * 100).toStringAsFixed(2)}%'),

With these code snippets, you can load a TensorFlow Lite model for image classification and display the top predictions in your Flutter app. Make sure to adapt this code to your specific use case and model requirements.

I hope this article was helpful to you. Thank you for taking the time to read it. Your feedback and suggestions are always welcome.

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