Custom Text Classification on Android using TensorFlow Lite

Create your own custom text classifier model and deploy it on an Android app using TensorFlow Lite.

Note: TF Lite Model Maker is now obsolete and is replaced by MediaPipe Model Maker. The below Colab notebook might therefore not work.

Steps:

1. Clone the repository on your local machine.

2. Sign in to your Google account and upload the Custom_Text_Classification.ipynb notebook on Colab.

3. Run the notebook cells one-by-one by following the instructions.

4. Once the TF Lite model is downloaded, copy the .tflite model file inside Custom-Text-Classification-on-Android-using-TF-Lite/Android_App/lib_task_api/src/main/assets directory.

5. Open the project in Android Studio and let it build itself for some time.

6. Open TextClassificationClient.java file under lib_task_api and edit Line 31 by replacing <your_model.tflite> with the name of your actual TF Lite model.

7. Build the project and install it on your phone. Enjoy your own custom-build text classifier app.

Note: To build your custom dataset, refer the train.csv file for the format.

Output:

• Read the Medium blog for step-by-step implementation.