In this post, we'll show you how to create REST APIs with TensorFlow.js and Node.js. We'll go over all the steps necessary to get your project up and running, including installing the required dependencies, setting up your project, and writing your code.
By the end of this post, you'll be able to create your own REST APIs with TensorFlow.js and Node.js.
Before we get started, there are a few things you'll need to have in order to follow along:
Once you have the prerequisites set up, you're ready to start setting up your project.
First, create a new directory for your project. We'll call ours tfjs-rest-api.
Next, initialize your project by running npm init in your project directory. This will create a package.json file for your project.
Now that you have a package.json file, you can install the dependencies for your project. Run the following command to install Express, Body-Parser , and TensorFlow.js :
npm install express body-parser @tensorflow/tfjs --save
This will install the dependencies and add them to your package.json file.
Now that you have your project set up, you're ready to start writing your code.
Open your text editor and create a new file in your project directory. We'll call ours index.js.
In this file, we'll start by requiring the dependencies we installed earlier:
const express = require('express');
const bodyParser = require('body-parser');
const tf = require('@tensorflow/tfjs');
Next, we'll set up our Express server:
const app = express();
const port = 3000;
app.use(bodyParser.json());
app.listen(port, () => {
console.log(`Server listening on port ${port}`);
});
Now that our server is set up, we can start writing our API endpoints.
Our first endpoint will be a GET request to / that will return a simple message:
app.get('/', (req, res) => {
res.send('Hello, world!');
});
Our second endpoint will be a POST request to /predict that will take in an array of numbers and return a prediction based on those numbers:
app.post('/predict', (req, res) => {
const input = tf.tensor2d([req.body.numbers], [1, req.body.numbers.length]);
const output = model.predict(input);
res.send(output.dataSync());
});
In this endpoint, we're using a pre-trained TensorFlow.js model to make our predictions. You can find this model here.
Now that you have your API endpoints set up, you're ready to test them.
To test your GET endpoint, you can use a tool like Postman.
To test your POST endpoint, you can use the following command:
curl -X POST -H "Content-Type: application/json" -d '{"numbers": [1, 2, 3, 4, 5]}' http://localhost:3000/predict
This should return a prediction of [6].
In this post, we showed you how to create REST APIs with TensorFlow.js and Node.js. We covered all the steps necessary to get your project up and running, including installing the required dependencies, setting up your project, and writing your code.
By the end of this post, you should be able to create your own REST APIs with TensorFlow.js and Node.js.