Converted /ml folder to typescript

src/ml/Model.ts

@@ -1,15 +1,21 @@
+import { ModelJSON, WeightsManifestConfig } from "@tensorflow/tfjs-core/dist/io/types";
 import blobToBuffer from "../helpers/blobToBuffer";
 
 class Model {
-  constructor(config, weightsMapping) {
+  config: ModelJSON;
+  weightsMapping: { [path: string]: string };
+  constructor(config: ModelJSON, weightsMapping: { [path: string]: string }) {
     this.config = config;
     this.weightsMapping = weightsMapping;
   }
 
   async load() {
     // Load weights from the manifest then fetch them into an ArrayBuffer
-    let buffers = [];
-    const manifest = this.config.weightsManifest[0];
+    let buffers: ArrayBuffer[] = [];
+    if (this.config === undefined) {
+      return;
+    }
+    const manifest = this.config?.weightsManifest[0];
     for (let path of manifest.paths) {
       const url = this.weightsMapping[path];
       const response = await fetch(url);

src/ml/gridSize/GridSizeModel.ts

@@ -2,17 +2,21 @@ import Model from "../Model";
 
 import config from "./model.json";
 import weights from "./group1-shard1of1.bin";
+import { LayersModel } from "@tensorflow/tfjs";
+import { ModelJSON } from "@tensorflow/tfjs-core/dist/io/types";
 
 class GridSizeModel extends Model {
   // Store model as static to prevent extra network requests
-  static model;
+  static model: LayersModel;
   // Load tensorflow dynamically
-  static tf;
+
+  // TODO: find type for tf
+  static tf: any;
   constructor() {
-    super(config, { "group1-shard1of1.bin": weights });
+    super(config as ModelJSON, { "group1-shard1of1.bin": weights });
   }
 
-  async predict(imageData) {
+  async predict(imageData: ImageData) {
     if (!GridSizeModel.tf) {
       GridSizeModel.tf = await import("@tensorflow/tfjs");
     }
@@ -23,7 +27,8 @@ class GridSizeModel extends Model {
     }
     const model = GridSizeModel.model;
 
-    const prediction = tf.tidy(() => {
+    // TODO: check this mess -> changing type on prediction causes issues
+    const prediction: any = tf.tidy(() => {
       const image = tf.browser.fromPixels(imageData, 1).toFloat();
       const normalized = image.div(tf.scalar(255.0));
       const batched = tf.expandDims(normalized);