Home › Ai › Tutorial

Creating a mobile keyboard with AI features | ANAZEBLOG

6 min read

Creating a mobile keyboard with AI features is a complex and multi-step process that involves multiple technologies like Android development (for mobile apps), Machine Learning/AI (for predictive text, speech recognition, etc.), and integrating these into a custom keyboard application. Below is a step-by-step guide on how to approach creating a basic mobile keyboard with AI features using Android Studio and some AI libraries for text prediction or other advanced functionalities.

Step-by-Step Guidelines to Create a Mobile Keyboard with AI Features:

Install Android Studio:

Download and install [Android Studio](https://developer.android.com/studio).
Make sure you have the latest SDK and Android Emulator installed for testing.

Create a New Android Project:

Open Android Studio and create a new project.
Select "Empty Activity" for simplicity, name your project (e.g., "AIKeyboardApp").

Step 2: Build the Basic Keyboard Layout

Create the Keyboard Layout:

You will create a keyboard layout XML file that defines how the keyboard buttons look and behave.

Example of a simple keyboard layout (res/layout/keyboard_layout.xml):

xml

  
    <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
  
         android:layout_width="match_parent"
         android:layout_height="wrap_content"
         android:orientation="vertical">
         
         <!-- A simple row of keys -->
         <GridLayout
  
               android:layout_width="match_parent"
  
  
             
     android:layout_height="wrap_content"
  
  
               android:columnCount="10">
  
             <Button
  
                   android:text="Q"
  
  
                 
     android:layout_width="0dp"
  
  
                 
     android:layout_height="wrap_content"
  
  
                 
     android:layout_columnSpan="1"
  
  
                 
     android:layout_rowSpan="1"
  
  
                 
     android:layout_columnWeight="1"/>
  
             <Button
  
                   android:text="W"
  
  
                 
     android:layout_width="0dp"
  
  
                 
     android:layout_height="wrap_content"
  
  
                 
     android:layout_columnSpan="1"
  
  
                 
     android:layout_rowSpan="1"
  
  
                 
     android:layout_columnWeight="1"/>
  
  
               <!-- Continue for the rest of
    the alphabet -->
  
         </GridLayout>
  

     </LinearLayout>

Create the Input Method Service (Keyboard Service):

You need to create a `KeyboardService` class to handle keyboard input.

Create a `KeyboardService.java` class:

java

   package com.example.aikeyboardapp;

  import android.inputmethodservice.InputMethodService;
     import android.view.KeyEvent;
     import android.view.View;
     import android.widget.EditText;
     import android.widget.Toast;
  

  
       public class KeyboardService extends InputMethodService {
  
  

         @Override
         public View onCreateInputView() {
  
               // Inflate your custom keyboard
    layout here
  
  
               return
    getLayoutInflater().inflate(R.layout.keyboard_layout, null);
  
         }
  

  
           // Example method to handle key press events
  
         public void onKeyPress(View v) {
             // Handle key press
  
               Toast.makeText(this, "Key Pressed",
    Toast.LENGTH_SHORT).show();
  
         }
  

     }

Step 3: Add AI Features to the Keyboard

To implement AI features such as predictive text or autocorrect, you can integrate machine learning models or use natural language processing libraries. Here’s how to do it:

Add Dependencies for AI Libraries:

You can use an AI/ML library like TensorFlow Lite for Android to integrate a predictive text model or autocorrection. For simplicity, let's assume you will use TensorFlow Lite or a pre-built text prediction API.

       Add the dependencies for TensorFlow Lite (in `build.gradle`):

   gradle
   implementation 'org.tensorflow:tensorflow-lite:2.7.0'

  
       implementation 'org.tensorflow:tensorflow-lite-support:2.7.0'

Implement Predictive Text Using a Pre-trained Model:

You can create or use a pre-trained model (such as an RNN or Transformer model) for predictive text. These models predict the next word based on the current context.
For a simple version, you can also use APIs such as *Google's ML Kit* to implement text prediction.

Example of using ML Kit for text prediction:

     gradle
       implementation 'com.google.mlkit:natural-language-model:16.0.0'

Then, in your Java code, you can use it like this:

java

 
       import com.google.mlkit.nl.languageid.LanguageIdentification;
  
  
       import com.google.mlkit.nl.languageid.LanguageIdentifier;
  
  

  
       LanguageIdentifier languageIdentifier =
    LanguageIdentification.getClient();
  
  

  
       languageIdentifier.identifyLanguage("Hello, how are you?")
  
  
           .addOnSuccessListener(languageCode -> {
  
  
               // Use the identified language for
    prediction
  
         });

Autocorrect and Suggestion Model:

You can create or train an autocomplete model that provides suggestions based on user input.
You would need a dataset of common words or phrases to train such a model.
You can implement this by using a Pretrained Model for word prediction.
Use TensorFlow to train the model, then export it to TensorFlow Lite for use on the device.

Step 4: Implement Keyboard Interaction with AI

Once the AI features are integrated, you need to add the logic to make the keyboard interact with these features:

Capturing Key Presses:

Capture each key press and send the data to your predictive text model to suggest the next word or correct spelling errors.

Updating the Keyboard with Suggestions:

When the AI model provides a suggestion, dynamically update the keyboard layout to show those suggestions.

Example:

    

    java
    public void onKeyPress(View v) {
    
          String keyPressed = ((Button) v).getText().toString();
    
    
          String predictedText = getPredictedText(keyPressed);
    
        updateSuggestions(predictedText);
    }
    

    private String getPredictedText(String currentInput) {
        // Use the AI model to predict the next word
    
          // For example, use ML Kit or TensorFlow Lite for prediction
    
        return predictedText;
    }
    

    private void updateSuggestions(String predictedText) {
        // Update the UI with new text suggestions
    
          // Example: Show suggestions in a popup or modify the next
      word
    
    }

Step 5: Testing and Debugging

Test your keyboard: Run the app on an emulator or a real device to see how the keyboard works.
Test AI features: Ensure the AI model gives accurate text predictions or corrections based on input.
Optimize the keyboard layout: Adjust key sizes, spacing, and ensure responsiveness.

Step 6: Publish the Keyboard App

Once the app is developed and tested, you can publish the keyboard app on the Google Play Store.
Ensure you follow the Google Play Store guidelines for custom input method services and permissions.

Required Resources/Tools:

Android Studio (for development)
TensorFlow Lite or ML Kit (for AI features)
Java or Kotlin (for Android development)
Custom AI model (if creating your own predictive text or autocorrect model)
Emulator/Device for testing the keyboard app

Conclusion:

Building a mobile keyboard with AI features involves a combination of Android development and machine learning. With TensorFlow Lite or ML Kit, you can integrate advanced AI functionalities like text prediction, autocorrect, or even voice-to-text. The process requires good knowledge of Android development, AI, and machine learning models.