Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

Deploying the model

After successfully adding a basic play and stop functionality to the application, let's now access the hosted model to generate, fetch, and play a new audio file every time. The following steps discuss in detail how we can access the model inside the application:

1. First of all, we define the fetchResponse() method to generate and fetch new audio files:

void fetchResponse() async {   final response =     await http.get('http://35.225.134.65:8000/generate');   if (response.statusCode == 200) {     var v = json.decode(response.body);     fileName = v["result"] ;   } else {     throw Exception('Failed to load');   } }

We begin by fetching the response from the API using http.get() and passing in the URL where the model is hosted. The response from ...