How to create custom machine learning models in FlutterFlow?

Step 1: Create a New FlutterFlow Project

Begin by going to the FlutterFlow website and logging in to the platform. Once you've successfully logged in, click on the Create New Project button to initialize a new project. Select a name for your project and then click on Create.

Step 2: Designing the App UI

Now that you have a brand new project, you should see an outline of a mobile device screen. This is the FlutterFlow UI builder. To make custom machine learning models work, we'll need a way to input information into the model, and to display the output.

Use the drag and drop feature to design the user interface of your app. For example, drag a text box onto the screen to create a portion where users input data. Next, you can add a button which will make the application execute the machine learning prediction once clicked.

Step 3: Creating Firebase Firestore Collections

To store the data we'll be using in our machine learning model, we need to set up Firebase Firestore collections. On your main FlutterFlow screen, click on the Database tab on the left hand side, and select Add Collection. Here you will define the structure of your data. Fields you create here now can later be used in your machine learning model. Click on Create to finalize the collection.

Step 4: Integrating Firebase Model to FlutterFlow

Now we're going to connect a machine learning model with FlutterFlow via Firebase. FlutterFlow can generate code and directly interact with the Firebase ML Kit. First, you must deploy your trained model to Firebase.

Go to your Firebase console, click on the project that matches your FlutterFlow project, and go to the Machine Learning tab. Here, you can upload your TensorFlow Lite model and get a model name. Copy the model name as you will need it later.

Step 5: Add Firebase ML Package to FlutterFlow Project

Go back to your FlutterFlow project and in the top-level menu, find Integrations. From the dropdown, select Firebase ML. Here you will see an option to enter the name of your Firebase Machine Learning model. Paste the model name that you copied earlier and click Apply.

Step 6: Customizing the Button Action

Now we need to create the action that occurs when your button is clicked. Go back to the UI builder and click on the button. In the Actions panel on the right, click on onClick.

In the dropdown that appears, you need to go down the list until you find something like call Firebase ML model. This is the action that will call your machine learning model and pass input data to it. You will need to choose which input data is being passed to the model. This will typically be text from a text box or some other type of user input.

Step 7: Displaying the Results

Finally, you'll want to display the output of your model to your users. Depending on the kind of data you're working with, this might be displayed as text output or in a more visual format. You can use the drag-and-drop editor to add elements to your screen that will display this output effectively.

After creating a text box or some other form of output, click on Bindings on the right side of the screen and set 'Text' to be bound to the output of the machine learning model call.

Step 8: Finalize and Preview the App

After all the desired functionalities are in place, you can preview your app by clicking on the Preview button at the top right. This will launch a simulator where you can test your app as if you were using it on a real device.

Once you are satisfied with your app, you can then build and export your project. In the top-level menu, select Build, download the output, and then configure it further in your desired code editor or directly publish it to Android or Apple App Stores.

Congratulations, you have successfully created a custom machine learning model in FlutterFlow!