Demo UI++

demo-ng/app/app.css

@@ -79,6 +79,7 @@ Label.mlkit-result {
   margin: 5;
   text-align: left;
   color: white;
+  font-size: 17;
 }
 
 .mask {

demo-ng/app/tabs/mlkit/facedetection/facedetection.component.html

@@ -40,7 +40,7 @@
       </GridLayout>
       <Label row="0" col="1" class="text-center c-white" textWrap="true" color="white" verticalAlignment="center" text="The scanner has been configured to detect faces every 30th frame (default is 10). You can tweak this in your usage of the plugin."></Label>
       <StackLayout row="2" col="0" colSpan="3">
-        <Label [text]="mlKitAllOK" textWrap="true" class="m-t-5 c-purple"></Label>
+        <Label [text]="mlKitAllOK" textWrap="true" class="m-t-5 font-weight-bold c-white" [class.c-blue]="allSmilingAndEyesOpen"></Label>
         <GridLayout row="auto" columns="60, *, *, *" class="m-t-5">
           <Label row="0" col="0" class="mlkit-result font-weight-bold" textWrap="true" text="ID"></Label>
           <Label row="0" col="1" class="mlkit-result font-weight-bold" textWrap="true" text="Smiling"></Label>

demo-ng/app/tabs/mlkit/facedetection/facedetection.component.ts

@@ -11,19 +11,20 @@ export class FaceDetectionComponent extends AbstractMLKitViewComponent {
   faces: Array<MLKitDetectFacesResultFace>;
 
   mlKitAllOK: string;
+  allSmilingAndEyesOpen: boolean = false;
 
   onFaceDetectionResult(scanResult: any): any {
     const value: MLKitDetectFacesOnDeviceResult = scanResult.value;
     if (value.faces.length > 0) {
       this.faces = value.faces;
       console.log("this.faces: " + JSON.stringify(this.faces));
 
-      let allSmilingAndEyesOpen = true;
+      this.allSmilingAndEyesOpen = true;
       value.faces.forEach(face => {
-        allSmilingAndEyesOpen = allSmilingAndEyesOpen && face.smilingProbability && face.leftEyeOpenProbability && face.rightEyeOpenProbability &&
+        this.allSmilingAndEyesOpen = this.allSmilingAndEyesOpen && face.smilingProbability && face.leftEyeOpenProbability && face.rightEyeOpenProbability &&
             face.smilingProbability > 0.7 && face.leftEyeOpenProbability > 0.7 && face.rightEyeOpenProbability > 0.7;
       });
-      this.mlKitAllOK = `All smiling and eyes open? ${allSmilingAndEyesOpen ? 'Yes!' : 'Nope'}`;
+      this.mlKitAllOK = `All smiling and eyes open? ${this.allSmilingAndEyesOpen ? 'Yes!' : 'Nope'}`;
     }
   }
 }

demo-ng/app/tabs/mlkit/imagelabeling/imagelabeling.component.html

@@ -7,6 +7,7 @@
       width="100%"
       height="100%"
       confidenceThreshold="0.6"
+      [torchOn]="torchOn"
       (scanResult)="onImageLabelingResult($event)">
   </MLKitImageLabeling>
 

demo-ng/app/tabs/mlkit/imagelabeling/imagelabeling.component.ts

@@ -13,12 +13,6 @@ export class ImageLabelingComponent extends AbstractMLKitViewComponent {
     confidence: number;
   }>;
 
-  constructor() {
-    super();
-    // let's start with the torch on, just for show
-    this.torchOn = true;
-  }
-
   onImageLabelingResult(scanResult: any): void {
     const value: MLKitImageLabelingOnDeviceResult = scanResult.value;
     this.labels = value.labels;

demo-ng/app/tabs/mlkit/mlkit.component.ts

@@ -40,7 +40,7 @@ export class MLKitComponent {
     "Custom model",
     "Landmark recognition (cloud)",
     "Language identification",
-    "Smart Reply"
+    "Smart reply"
   ];
 
   private mlkitOnDeviceFeatures: Array<string> = [
@@ -268,22 +268,36 @@ export class MLKitComponent {
     firebase.mlkit.textrecognition.recognizeTextOnDevice({
       image: imageSource
     }).then((result: MLKitRecognizeTextResult) => {
-      const messages: Array<MLKitSmartReplyConversationMessage> = [];
-      result.blocks.forEach(block => messages.push({
+      const conversation: Array<MLKitSmartReplyConversationMessage> = [];
+      let personId = 0;
+
+      // just faking a conversation based on the text blocks we got from the image :)
+      result.blocks.forEach(block => conversation.push({
         text: block.text,
-        userId: "abc",
-        localUser: false,
-        timestamp: new Date().getTime()
+        userId: "person" + (++personId % 2 === 0 ? 1 : 2),
+        localUser: (personId % 2 === 0),
+        timestamp: new Date().getTime() - 500000 + (personId * 5000)
       }));
+
+      console.log(JSON.stringify(conversation));
+
       firebase.mlkit.smartreply.suggestReplies({
-        messages
+        conversation
       }).then((result: Array<string>) => {
+        console.log("result1: " + JSON.stringify(result));
         alert({
           title: `Suggestions`,
           message: JSON.stringify(result),
           okButtonText: "OK"
         });
-      }).catch(errorMessage => console.log("ML Kit error: " + errorMessage));
+      }).catch(errorMessage => {
+        console.log("ML Kit error: " + errorMessage);
+        alert({
+          title: `Error getting suggestions`,
+          message: errorMessage,
+          okButtonText: "Pity.."
+        });
+      });
     }).catch(errorMessage => console.log("ML Kit error: " + errorMessage));
   }