[v1.1.0] - add BigInt polyfill and loading model retries

README.md

@@ -1,14 +1,14 @@
-# Teachable Machine Node v1.0.0
+# Teachable Machine Node
 
 ![teachable-machine-cover](https://media-blog.sashido.io/content/images/2020/09/teachable-machine-cover.png)
 
 # About
 
-**Teachable Machine Node v.1.0.0** empowers you to load any image classification model trained with Google's Teachable Machine tool in a Node.Js project.
+**Teachable Machine Node** empowers you to load any image classification model trained with Google's Teachable Machine tool in a Node.Js project.
 
-[Teachable Machine](https://teachablemachine.withgoogle.com/) makes AI easy for everyone, by offering a fast and fun way to train a real TensorFlow.js Machine Learning Models without any coding required. You can train the computer to recognize images, sounds, & poses, using your camera or your own dataset. 
+[Teachable Machine](https://teachablemachine.withgoogle.com/) makes AI easy for everyone, by offering a fast and fun way to train a real TensorFlow.js Machine Learning Models without any coding required. You can train the computer to recognize images, sounds, & poses, using your camera or your own dataset.
 
-For now, Teachable Machine Node v.1.0.0 holds suport only for image models, but we won't stop here. Check out the [Roadmap](#Roadmap) of what comes next!
+For now, Teachable Machine Node holds suport only for image models, but we won't stop here. Check out the [Roadmap](#Roadmap) of what comes next!
 
 # Install
 
@@ -31,7 +31,7 @@ yarn add @sashido/teachablemachine-node
 
 1. [Gathering samples](https://youtu.be/DFBbSTvtpy4) is the fundamental first step to your Teachable Machine Model. Use your camera to collect data or upload some preselected images.
 
-2. [Train your Teachable Machine Image Model](https://teachablemachine.withgoogle.com/train?action=onboardOpen&id=CO67EQ0ZWgA). 
+2. [Train your Teachable Machine Image Model](https://teachablemachine.withgoogle.com/train?action=onboardOpen&id=CO67EQ0ZWgA).
 
 ![](https://media-blog.sashido.io/content/images/2020/09/tm_export_model.png)
 
@@ -117,15 +117,15 @@ app.listen(port, () => {
 
 In the long run, we will add more options, so you can train and load all kinds of Teachable Machine Models.
 
- 1. Add support for Pose Models. 
- 
- 2. Add support for Audio Models. 
- 
- 3. Add support for Gifs. 
- 
+ 1. Add support for Pose Models.
+
+ 2. Add support for Audio Models.
+
+ 3. Add support for Gifs.
+
  4. Add support for Videos.
- 
-We would love to have your opinion which's the one you would like to see supported first. Don't be shy and drop us a line at [email protected]. 
+
+We would love to have your opinion which's the one you would like to see supported first. Don't be shy and drop us a line at [email protected].
 
 # Contribute
 

index.js

@@ -1,32 +1,27 @@
+global.BigInt = require("bigint-polyfill");
+global.fetch = require("node-fetch");
+
 const tmImage = require("@teachablemachine/image");
 const isImageUrl = require('is-image-url');
 const canvas = require("canvas");
+
 const { JSDOM } = require("jsdom");
 const dom = new JSDOM("");
-
-
-global.fetch = require("node-fetch");
 global.document = dom.window.document;
 global.HTMLVideoElement = dom.window.HTMLVideoElement;
 
 
 const wait = ms => new Promise(r => setTimeout(r, ms));
 
-
-const retryOperation = (operation, delay, times, retriesCounter = 0) => new Promise((resolve, reject) => {
+const retryOperation = (operation, delay, times) => new Promise((resolve, reject) => {
   return operation()
     .then(({ cb }) => {
       return resolve(cb());
     })
     .catch(({ message }) => {
-      if (retriesCounter === 0) {
-        console.info("[@sashido/teachablemachine-node] -", message);
-      }
-
       if (times - 1 > 0) {
-        retriesCounter++;
         return wait(delay)
-          .then(retryOperation.bind(null, operation, delay, times - 1, retriesCounter))
+          .then(retryOperation.bind(null, operation, delay, times - 1))
           .then(resolve)
           .catch(reject);
       }
@@ -41,15 +36,17 @@ const byProbabilty = (predictionA, predictionB) => {
   return 0;
 }
 
-class SashiDoTeachable {
+
+class SashiDoTeachableMachine {
   constructor(params) {
     this.loadModel(params);
   }
 
-
   async loadModel({ modelUrl }) {
     if (!modelUrl || modelUrl === "") {
       console.error("[@sashido/teachablemachine-node] -", "Missing model URL!");
+      this.error = "Missing model URL!";
+      return null;
     }
 
     try {
@@ -59,32 +56,42 @@ class SashiDoTeachable {
     }
   }
 
+  async checkModel(cb) {
+    const { model } = this;
+
+    if (model) {
+      return Promise.resolve({ cb });
+    }
+
+    return Promise.reject({ message: "Loading model" });
+  }
+
 
   async classify(params) {
-    const { model } = this;
     const { imageUrl } = params;
 
     if (!isImageUrl(imageUrl)) {
-      console.error("[@sashido/teachablemachine-node] -", "Image URL is not valid!");
       return Promise.reject({ error: "Image URL is not valid!" });
     }
 
-    if (!model) {
-      console.error("[@sashido/teachablemachine-node] -", "Model is not ready!");
-      return Promise.reject({ error: "Model is not ready!" });
+    if (this.error) {
+      return Promise.reject({ error: this.error });
     }
 
+    return retryOperation(() => this.checkModel(() => this.inference(params)), 1000, 20); // method, delay, retries
+  }
+
+  async inference({ imageUrl }) {
     try {
       const image = new canvas.Image();
       image.src = imageUrl;
 
       const predictions = await this.model.predict(image);
       return predictions.sort(byProbabilty);
     } catch (error) {
-      console.error("[@sashido/teachablemachine-node] -", error);
       return Promise.reject({ error });
     }
   }
 }
 
-module.exports = SashiDoTeachable;
+module.exports = SashiDoTeachableMachine;