URGENT!!!!! Please HELP!!! Unhandled Exception: PlatformException(Failed to run model, Attempt to invoke virtual method 'org.tensorflow.lite.Tensor org.tensorflow.lite.Interpreter.getInputTensor(int)' on a null object reference, java.lang.NullPointerException: Attempt to invoke virtual method 'org.tensorflow.lite.Tensor org.tensorflow.lite.Interpreter.getInputTensor(int)' on a null object reference

[khaiyue]

This is the whole debugging log shown. This happen after the image is chosen, no matter is from camera or gallery.
D/VRI[MainActivity](31231): debugCancelDraw some OnPreDrawListener onPreDraw return false,cancelDraw=true,count=50,android.view.ViewRootImpl@2025556 D/FirebaseAuth(31231): Notifying id token listeners about user ( PCBsXViqy9U628twsSRcGPv1SGW2 ). I/TensorFlowLite(31231): Loaded native library: tensorflowlite_jni I/TensorFlowLite(31231): Didn't load native library: tensorflowlite_jni_gms_client I/tflite (31231): Initialized TensorFlow Lite runtime. W/libc (31231): Access denied finding property "ro.hardware.chipname" I/tflite (31231): Created TensorFlow Lite XNNPACK delegate for CPU. I/Quality (31231): Skipped: false 1 cost 29.663422 refreshRate 16683856 bit true processName com.example.fyp2.fyp2 I/flutter (31231): Failed to load model. D/VRI[MainActivity](31231): registerCallbacksForSync syncBuffer=false D/VRI[MainActivity](31231): Received frameCommittedCallback lastAttemptedDrawFrameNum=1 didProduceBuffer=true syncBuffer=false W/Parcel (31231): Expecting binder but got null! D/VRI[MainActivity](31231): debugCancelDraw cancelDraw=false,count = 220,android.view.ViewRootImpl@2025556 D/VRI[MainActivity](31231): draw finished. I/Quality (31231): TaskTrackInfo {"Pkg":com.example.fyp2.fyp2,"Window":com.example.fyp2.fyp2.MainActivity,"Type":1,"Action":10,"Cost":4135,"FV":16,"JC":16-15-0-1-0-0-1-0-4-1-0-0-0-0-0-0-0-0-1-0-4124-3849-924470182-1538,"SF":no-need,"UITid":31231,"RTid":3532} TTI:Access fail D/VRI[MainActivity](31231): onFocusEvent true D/ProfileInstaller(31231): Installing profile for com.example.fyp2.fyp2 D/OplusInputMethodManagerInternal(31231): get inputMethodManager extension: com.android.internal.view.IInputMethodManager$Stub$Proxy@e971ea8 D/VRI[MainActivity](31231): onFocusEvent false D/CompatibilityChangeReporter(31231): Compat change id reported: 78294732; UID 10446; state: ENABLED D/VRI[MainActivity](31231): onFocusEvent true I/flutter (31231): G O T !!!!!!! Image E/flutter (31231): [ERROR:flutter/runtime/dart_vm_initializer.cc(41)] Unhandled Exception: PlatformException(Failed to run model, Attempt to invoke virtual method 'org.tensorflow.lite.Tensor org.tensorflow.lite.Interpreter.getInputTensor(int)' on a null object reference, java.lang.NullPointerException: Attempt to invoke virtual method 'org.tensorflow.lite.Tensor org.tensorflow.lite.Interpreter.getInputTensor(int)' on a null object reference E/flutter (31231): at sq.flutter.flutter_tflite.TflitePlugin.feedInputTensor(TflitePlugin.java:368) E/flutter (31231): at sq.flutter.flutter_tflite.TflitePlugin.feedInputTensorImage(TflitePlugin.java:430) E/flutter (31231): at sq.flutter.flutter_tflite.TflitePlugin.detectObjectOnImage(TflitePlugin.java:624) E/flutter (31231): at sq.flutter.flutter_tflite.TflitePlugin.onMethodCall(TflitePlugin.java:157) E/flutter (31231): at io.flutter.plugin.common.MethodChannel$IncomingMethodCallHandler.onMessage(MethodChannel.java:267) E/flutter (31231): at io.flutter.embedding.engine.dart.DartMessenger.invokeHandler(DartMessenger.java:292) E/flutter (31231): at io.flutter.embedding.engine.dart.DartMessenger.lambda$dispatchMessageToQueue$0$io-flutter-embedding-engine-dart-DartMessenger(DartMessenger.java:319) E/flutter (31231): at io.flutter.embedding.engine.dart.DartMessenger$$ExternalSyntheticLambda0.run(Unknown Source:12) E/flutter (31231): at android.os.Handler.handleCallback(Handler.java:942) E/flutter (31231): at android.os.Handler.dispatchMessage(Handler.java:99) E/flutter (31231): at android.os.Looper.loopOnce(Looper.java:240) E/flutter (31231): at android.os.Looper.loop(Looper.java:351) E/flutter (31231): at android.app.ActivityThread.main(ActivityThread.java:8377) E/flutter (31231): at java.lang.reflect.Method.invoke(Native Method) E/flutter (31231): at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:584) E/flutter (31231): at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1013) E/flutter (31231): , null) E/flutter (31231): #0 StandardMethodCodec.decodeEnvelope (package:flutter/src/services/message_codecs.dart:651:7) E/flutter (31231): #1 MethodChannel._invokeMethod (package:flutter/src/services/platform_channel.dart:334:18) E/flutter (31231): <asynchronous suspension> E/flutter (31231): #2 Tflite.detectObjectOnImage (package:flutter_tflite/flutter_tflite.dart:113:12) E/flutter (31231): <asynchronous suspension> E/flutter (31231): #3 _CameraPageState.yolov2Tiny (package:fyp2/views/camera_page.dart:494:24) E/flutter (31231): <asynchronous suspension> E/flutter (31231): #4 _CameraPageState.predictImage (package:fyp2/views/camera_page.dart:421:5) E/flutter (31231): <asynchronous suspension> E/flutter (31231): W/System (31231): A resource failed to call close. W/System (31231): A resource failed to call close.

This is the complete code that I modified based on my use case, but mostly is the similar one.

import 'dart:async';
import 'dart:io';
import 'dart:typed_data';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart';
import 'package:image/image.dart' as img;
import 'package:image_picker/image_picker.dart';
import 'package:flutter_tflite/flutter_tflite.dart';

class CameraPage extends StatefulWidget {
  const CameraPage({super.key});

  @override
  _CameraPageState createState() => _CameraPageState();
}

class _CameraPageState extends State<CameraPage> {
  final ImagePicker _picker = ImagePicker();
  XFile? _image;
  List<dynamic>? _recognitions;
  late double _imageHeight;
  late double _imageWidth;
  bool _busy = false;

  Future predictImagePicker(ImageSource source) async {
    var image = await _picker.pickImage(source: source);
    if (image == null) {
      ScaffoldMessenger.of(context).showSnackBar(
          const SnackBar(content: Text("Image selection cancelled")));
      return;
    }
    print('G O T !!!!!!! Image');
    setState(() {
      _busy = true;
    });
    predictImage(image);
  }

  Future predictImage(XFile image) async {
    await yolov2Tiny(image);

    FileImage(File(image.path))
        .resolve(const ImageConfiguration())
        .addListener(ImageStreamListener((ImageInfo info, bool _) {
      setState(() {
        _imageHeight = info.image.height.toDouble();
        _imageWidth = info.image.width.toDouble();
      });
    }));

    setState(() {
      _image = image;
      _busy = false;
    });
  }

  @override
  void initState() {
    super.initState();
    _busy = true;

    loadModel().then((val) {
      setState(() {
        _busy = false;
      });
    });
  }

  Future loadModel() async {
    Tflite.close();
    try {
      String res = (await Tflite.loadModel(
        model: "assets/best-int8.tflite",
        labels: "assets/labels.txt",
        numThreads: 4,
        isAsset: true,
        useGpuDelegate: false,
      ))!;
      print('Load successfully');
    } on PlatformException {
      print('Failed to load model.');
    }
  }

  Uint8List imageToByteListFloat32(
      img.Image image, int inputSize, double mean, double std) {
    var convertedBytes = Float32List(1 * inputSize * inputSize * 3);
    var buffer = Float32List.view(convertedBytes.buffer);
    int pixelIndex = 0;
    for (var i = 0; i < inputSize; i++) {
      for (var j = 0; j < inputSize; j++) {
        var pixel = image.getPixel(j, i);
        buffer[pixelIndex++] = (img.getRed(pixel) - mean) / std;
        buffer[pixelIndex++] = (img.getGreen(pixel) - mean) / std;
        buffer[pixelIndex++] = (img.getBlue(pixel) - mean) / std;
      }
    }
    return convertedBytes.buffer.asUint8List();
  }

  Future yolov2Tiny(XFile image) async {
    int startTime = DateTime.now().millisecondsSinceEpoch;
    var recognitions = await Tflite.detectObjectOnImage(
      path: image.path,
      model: "YOLO",
      threshold: 0.3,
      imageMean: 0.0,
      imageStd: 255.0,
      numResultsPerClass: 3,
      asynch: true,
    );
    setState(() {
      _recognitions = recognitions!;
    });
    int endTime = DateTime.now().millisecondsSinceEpoch;
    print("Inference took ${endTime - startTime}ms");
  }

  List<Widget> renderBoxes(Size screen) {
    if (_recognitions == null) return [];
    double factorX = screen.width;
    double factorY = _imageHeight / _imageWidth * screen.width;
    Color blue = const Color.fromRGBO(37, 213, 253, 1.0);
    return _recognitions!.map((re) {
      return Positioned(
        left: re["rect"]["x"] * factorX,
        top: re["rect"]["y"] * factorY,
        width: re["rect"]["w"] * factorX,
        height: re["rect"]["h"] * factorY,
        child: Container(
          decoration: BoxDecoration(
            borderRadius: const BorderRadius.all(Radius.circular(8.0)),
            border: Border.all(
              color: blue,
              width: 2,
            ),
          ),
          child: Text(
            "${re["detectedClass"]} ${(re["confidenceInClass"] * 100).toStringAsFixed(0)}%",
            style: TextStyle(
              background: Paint()..color = blue,
              color: Colors.white,
              fontSize: 12.0,
            ),
          ),
        ),
      );
    }).toList();
  }

  @override
  Widget build(BuildContext context) {
    Size size = MediaQuery.of(context).size;
    List<Widget> stackChildren = [];

    if(_image != null){
      stackChildren.add(Positioned(
        top: 0.0,
        left: 0.0,
        width: size.width,
        child: Image.file(File(_image?.path ?? '')),
      ));
    }

    if (_busy) {
      stackChildren.add(const Opacity(
        opacity: 0.3,
        child: ModalBarrier(dismissible: false, color: Colors.grey),
      ));
      stackChildren.add(const Center(child: CircularProgressIndicator()));
    }

    if (_recognitions != null && _recognitions!.isNotEmpty) {
      stackChildren.addAll(renderBoxes(size));
    }

    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Scan Ingredients'),
        ),
        body: Center(
          child: Column(
            mainAxisAlignment: MainAxisAlignment.center,
            children: [
              ElevatedButton.icon(
                onPressed: (){predictImagePicker(ImageSource.camera);},
                icon: const Icon(Icons.camera, size: 24.0,),
                label: const Text('Capture image with camera'),
              ),
              const SizedBox(height: 16),
              ElevatedButton.icon(
                onPressed: (){predictImagePicker(ImageSource.gallery);},
                icon: const Icon(Icons.image, size: 24.0,),
                label: const Text('Choose from Gallery'),
              ),
            ],
          ),
        ),
      ),
    );
  }
}