Guide to integrate Bubble.io with TensorFlow

This guide will take you through the process of integrating Bubble.io, a no-code platform, with TensorFlow, a machine learning library. The steps involved are quite detailed and require some understanding of both Bubble.io and TensorFlow. If you're not familiar with these, you might want to get a basic understanding of them first.

Prerequisites:

• You should have basic knowledge of how TensorFlow works (how to create, train and save a model).
• A free account on Bubble.io.
• A server to host the TensorFlow model.
• A RESTful API Endpoint to that server.

Step 1: Getting Started

When you are ready with your account, log into Bubble.io and create a new application.

Step 2: Set up the API

In the Bubble.io editor, go to Settings > API tab. Here, we need to activate the API feature. Enable the option "This app exposes a Workflow API" and "This app exposes Data API".

Step 3: Install API Connector

From the Bubble.io editor, go to Plugins > Add plugins. Here, search for the plugin "API Connector", and install it.

Step 4: Configure API Connector

In the API Connector plugin, add a new API. Let's call this API "TensorFlow". Here you will need to configure two main things:

• API Base URL: This is the URL at which your TensorFlow model is hosted.
• API Endpoint: This is a specific path in your base URL that points to a specific service.

When you set this up, the API Connector plugin effectively creates a bridge from Bubble.io to TensorFlow. Next, we need to test the setup to ensure it's working correctly.

Step 5: Test the API

In the API Connector plugin, under the TensorFlow API, you have a button to "Initialize the call". Click this button. If everything is set up correctly, you should see status as initialized.

Step 6: Create a Function

Now that we have the API set up, we need to create a workflow in Bubble.io that uses an API call. Create a workflow that sends data to the TensorFlow API and receives data back. For the sake of simplicity, here's a basic example of how we can do it:

• Create a Button and when the button is pressed,
• It triggers a workflow that makes an API call to the TensorFlow API.

In the workflow editor, select Triggers > When Button is Clicked. Here, pick the most suitable API call action. In this example, it could be "Get data from external API".

Step 7: Customize Function

Next, you need to decide what happens with the data that your TensorFlow model returns. You could display it on the screen, store it in the database, etc. This depends completely on your application and your needs.

In this guide, let's assume that we want to display the prediction on the Bubble.io application interface. For that, you'd add a text element on the page that shows the result of the API call.

Step 8: Deploy and Test the Application

After setting up everything, deploy the application to test it. The TensorFlow model should receive data from the Bubble.io application, perform whatever computation it's supposed to, and return the data. The Bubble.io application should then correctly interpret and display this data.

If everything is set up correctly, you should now have a working integration between Bubble.io and TensorFlow.

In summary, the integration between Bubble.io and TensorFlow involves Bubble.io making API calls to a server hosting the TensorFlow model. This requires setting up and configuring communication through an API, and creating workflows that make use of this API.

Remember that this is a broad guide and there could be parts that need to be modified based on the specifics of your TensorFlow model, your server, and your application. Always make sure to test everything thoroughly to ensure it's working as expected and debug any issues that come up.