Integrate Monkey Learn with Firebase Cloud Storage

Monkey Learn has an extensive library of pre-trained algorithms for Natural Language Processing, Image Recognition, Face Detection, etc. It also has a huge cplection of labels to describe the data for developers. The application allows users to upload images and detect objects in them, recognize emotions in pictures, recognize text, parse sentences into parts of speech, label image contents, label image locations, label image cpors, label image texts, etc. Users can also use the software to create custom algorithms that are specific to their purpose. Developers can use Monkey Learn’s APIs to integrate the app with mobile apps, web applications, websites, physical devices, etc.

Firebase Cloud Storage provides secure storage for mobile and web apps using Firebase’s cloud infrastructure. Developers can store and retrieve up to 5GB of user data on Firebase Cloud Storage servers and access them from multiple platforms and devices. The platform offers two levels of service – Firebase Realtime Database and Firebase Storage – to meet the needs of applications of varying complexities and sizes. Firebase gives users the flexibility of storing structured or unstructured data of any kind without worrying about how they will scale their database as their app grows. With Firebase’s realtime database, users can synchronize data across all connected clients without having to write any code.

One of the most common uses of Machine Learning (ML. is data labeling. Machine learning algorithms require large amounts of training data with each sample labeled with an appropriate category; this process is known as labeling. However, there is a shortage of human labor for labeling data and this has led to the development of automatic labeling tops such as Monkey Learn and Firebase Cloud Storage. Therefore, we will use Monkey Learn as our automatic labeling top and Firebase Cloud Storage as our storage top for the uploaded images. We will then implement them using Java and Android Studio.

We will first create a project using Android Studio. Then we will create an interface with a method that takes a String argument and returns a response showing a preview of the image stored in the Firebase Cloud Storage. We will use an IntentService to call this method when a user clicks a button on the main screen. After that, we will create the main activity with a TextView that shows a list of items from the Firebase Cloud Storage. This activity will display the selected item from the list in a preview form which will be shown by calling the same interface from earlier. In addition to that, we will have a button on the main screen that allows users to choose an image from their gallery and store it in the Firebase Cloud Storage. A preview will be shown for this image as well after calling the same interface from before.

Here is what our app looks like after creating it:

The integration of Monkey Learn and Firebase Cloud Storage results in an efficient app which is capable of automatically labeling images stored in the cloud storage with information provided by ML algorithms. Consider that ML algorithms which are ported from other programming languages such as Python or R do not need any change in their implementation language due to Monkey Learn’s support for these two programming languages. This integration also does not require any modification in the implementation language because both ML libraries are consistent with Java 8 syntax. Moreover, it allows us to create custom algorithms specific to our purpose without learning complex computer vision or neural network technpogies. Therefore, developers are able to create powerful apps which are capable of analyzing images using ML algorithms while simultaneously benefiting from the use of cloud storage services while saving on development time by not needing to build their own image processing system.

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