feat(cordova-plugin-firebase-model): Add new plugin to download and process ML model hosted in firebase.

src/@awesome-cordova-plugins/plugins/firebase-model/index.ts

@@ -0,0 +1,122 @@
+import { Injectable } from '@angular/core';
+import {
+  Plugin,
+  Cordova,
+  CordovaProperty,
+  CordovaInstance,
+  InstanceProperty,
+  IonicNativePlugin,
+} from '@ionic-native/core';
+import { Observable } from 'rxjs';
+
+export enum FirebaseModelStatus {
+  downloading = 'downloading',
+  completed = 'completed',
+}
+
+export enum FirebaseModelInputType {
+  path = 'path',
+  base64string = 'base64string',
+  blob = 'blob',
+}
+
+export class FirebaseModelConfigResult {
+  /**
+   * Returns the current status of the model.
+   */
+  status: FirebaseModelStatus;
+  /**
+   * Returns the current progress of the downloading model.
+   */
+  progress: number;
+}
+
+export class FirebaseModelClassifyResult {
+  /**
+   * Return the identified image label name.
+   */
+  label: string;
+  /**
+   * Returns the confidence score of the identified image.
+   */
+  score: number;
+}
+
+export class FirebaseModelInput {
+  /**
+   * Set the one of the input types defined in FirebaseModelInputType enum.
+   */
+  inputType: FirebaseModelInputType;
+  /**
+   * Set the input as string | Blob based on the `inputType`
+   */
+  input: string | Blob;
+}
+
+/**
+ * @name Firebase Model
+ * @description This plugin downloads the TensorFlow model from firebase and classify the images.
+ *
+ * ```typescript
+ * import { FirebaseModel } from '@ionic-native/ionic-native-firebase-model';
+ *
+ *
+ * constructor(private firebaseModel: FirebaseModel) { }
+ *
+ * ...
+ *
+ *
+ * this.firebaseModel.configure('Sample_Model')
+ *   .subscribe((res: FirebaseModelConfigResult) => console.log(res.status + " - " + res.progress))
+ *   .catch((error: any) => console.error(error));
+ *
+ *
+ * try {
+ *    var result:FirebaseModelClassifyResult = await this.firebaseModel.classify("/Documents/input_image.png")
+ *    console.log(result.label + " - " + result.score)
+ *
+ * }
+ * catch (e) {
+ *    console.log(e)
+ * }
+ *
+ *
+ * ```
+ */
+@Plugin({
+  pluginName: 'FirebaseModel',
+  plugin: 'cordova-plugin-firebase-model', // npm package name, example: cordova-plugin-camera
+  pluginRef: 'FirebaseModel', // the variable reference to call the plugin, example: navigator.geolocation
+  repo: '', // the github repository URL for the plugin
+  install: 'ionic cordova plugin add cordova-plugin-firebase-model', // OPTIONAL install command, in case the plugin requires variables
+  installVariables: [], // OPTIONAL the plugin requires variables
+  platforms: ['iOS'], // Array of platforms supported, example: ['Android', 'iOS']
+})
+@Injectable()
+export class FirebaseModel extends IonicNativePlugin {
+  /**
+   * This function configure the Firebase TFLite model and downloads.
+   * @param {string} arg1 Name of the TFLite model which is uploaded in the Firebase console
+   * @returns {Observable<FirebaseModelConfigResult>} Returns a observable that gives the callback for downloading progress and status.
+   *
+   */
+  @Cordova({
+    successIndex: 1,
+    errorIndex: 2,
+    observable: true,
+  })
+  configure(arg1: string): Observable<FirebaseModelConfigResult> {
+    return;
+  }
+
+  /**
+   * This function identify the image using the Firebase TFLite model which is configured.
+   * @param {FirebaseModelInput} arg1  Base64 string of the input image or .
+   * @returns {Promise<FirebaseModelClassifyResult>} Returns a promise that resolves the classification result.
+   *
+   */
+  @Cordova()
+  classify(arg1: FirebaseModelInput): Promise<FirebaseModelClassifyResult> {
+    return;
+  }
+}