Integrate Monkey Learn with Cloud Firestore

Monkey Learn

Monkey Learn is a machine learning platform that helps you to get started in the world of ML very quickly. It provides easy-to-use APIs, so we can implement machine learning algorithms in our applications without needing to have deep knowledge in this area. With this service, we can perform image classification, sentiment analysis, topic detection and many other types of tasks.

Cloud Firestore

Cloud Firestore is a NoSQL document database that we can use in our web and mobile applications. This service was released by Google at Google I/O 2016, as a successor to Google Cloud Datastore. We can access it via REST API or the corresponding client libraries for Java, Go, PHP, Python and Node.JS. Also, Cloud Firestore supports offline mode, which means that we can write data even when the user is offline.

Integration of Monkey Learn and Cloud Firestore

We will start with the integration of these two services, which is done by using their respective client libraries. In order to access Monkey Learn API, we must first create an account on their website and after that, we sign in. We need to register a new app in order to make requests to their API endpoints.

Then we have to create a class to define the URL endpoint that we will use to make requests:

In the above class, you can see the url property that has been defined as "https://api.monkeylearn.com/v1/classify", which is the API endpoint of our application. We also need to define the API version that we want to use (1. This way, we will be able to make requests for classification, sentiment analysis, topic detection etc.

After creating this class and making sure that we set the correct value for api_version property, we can move on and make requests to Monkey Learn API. The requests are made by using the get(. method of the client library and passing the parameters as JSON objects:

The above code shows us how to classify a text with Monkey Learn API. In the above example, we are using the classifier ID 'ml-classifier-r9yv6r8' (which is the ID of the classifier that we created earlier. The request returns a JSON object with all of the labels and their corresponding probabilities. If there are no labels with more than 0.5 probability, then the response will be null. The same thing happens if there are no words in text parameter or they are too few, irrelevant or stopwords.

Benefits of Integration of Monkey Learn and Cloud Firestore

In this part of the tutorial, we will take a look at some benefits of integrating Monkey Learn with Cloud Firestore. The first benefit is that we don't need to store all of our data on multiple servers and organize them according to some structure (like tables. Instead, Cloud Firestore gives us unlimited scalability and handles all of this work for us automatically. Of course, there are some limits in terms of transactions per second, average latency etc., but they are nothing compared to what we would need to do manually if we weren't using cloud database like Cloud Firestore. Another benefit is that it supports offline mode so we can write data even when users are offline.

Also, Cloud Firestore is fully managed so we don't need to worry about availability, security or performance concerns. We just have to focus on writing our application and let Google handle everything else for us. Furthermore, Cloud Firestore also has other cop features like live queries (which allow us to listen for changes in real-time), change listeners (which detect changes in documents. or field level security (enables us to contrp who can read and write each field. Also, if your application becomes popular enough, you will be able to use the full power of Google's infrastructure by enabling auto scaling option! All of those features make Cloud Firestore a great choice when building scalable applications.

The process to integrate Monkey Learn and Cloud Firestore may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.