TensorFLowLite Error

Research & Models
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#1

I have a tflite model that takes image as a input and predict its class. I want it to use in my unity Project. When I used the code given by chatgpt, following error occurs.can anyone help, i dont know much about unity and c#

Assets\Samples\Detection\Scripts\PythonBridge.cs(72,9): error CS0246: The type or namespace name ‘Tensor’ could not be found (are you missing a using directive or an assembly reference?)

Assets\Samples\Detection\Scripts\PythonBridge.cs(72,43): error CS0122: ‘Interpreter.GetOutputTensor(int)’ is inaccessible due to its protection level

using UnityEngine;
using TensorFlowLite;
using System.IO;
using System.Collections.Generic;

public class ObjectDetection : MonoBehaviour
{
    [SerializeField]
    [FilePopup("*.tflite")]
    public string modelPath = "model.tflite";


    [SerializeField]
    private TextAsset labelFile;

    [SerializeField]
    private Texture2D inputImage;

    private Interpreter interpreter;
    private List<string> labels;

    private const int IMAGE_SIZE = 224;
    private const int CHANNELS = 3;

    private void Start()
    {
        LoadModel();
        LoadLabels();
        PreprocessImage();
        RunInference();
    }

    private void LoadModel()
    {
        interpreter = new Interpreter(File.ReadAllBytes(modelPath));
    }

    private void LoadLabels()
    {
        labels = new List<string>();
        using (StringReader reader = new StringReader(labelFile.text))
        {
            string line;
            while ((line = reader.ReadLine()) != null)
            {
                labels.Add(line.Trim());
            }
        }
    }

    private void PreprocessImage()
    {
        Texture2D resizedImage = ResizeImage(inputImage, IMAGE_SIZE, IMAGE_SIZE);
        Color32[] pixels = resizedImage.GetPixels32();

        float[] imgArray = new float[IMAGE_SIZE * IMAGE_SIZE * CHANNELS];
        for (int i = 0; i < pixels.Length; i++)
        {
            imgArray[i * 3] = pixels[i].r / 255.0f;
            imgArray[i * 3 + 1] = pixels[i].g / 255.0f;
            imgArray[i * 3 + 2] = pixels[i].b / 255.0f;
        }

        interpreter.SetInputTensorData(0, imgArray);
    }

    private void RunInference()
    {
        interpreter.Invoke();

        // Retrieve output and process predictions
        Tensor outputTensor = interpreter.GetOutputTensor(0);
        float[] results = outputTensor.Data<float>();

        // Find class with highest probability
        int maxIndex = 0;
        float maxProbability = 0f;
        for (int i = 0; i < results.Length; i++)
        {
            if (results[i] > maxProbability)
            {
                maxProbability = results[i];
                maxIndex = i;
            }
        }

        string predictedLabel = labels[maxIndex];
        Debug.Log("Predicted object: " + predictedLabel);
    }

    private Texture2D ResizeImage(Texture2D source, int width, int height)
    {
        RenderTexture rt = RenderTexture.GetTemporary(width, height, 24);
        RenderTexture.active = rt;
        Graphics.Blit(source, rt);
        Texture2D result = new Texture2D(width, height);
        result.ReadPixels(new Rect(0, 0, width, height), 0, 0);
        result.Apply();
        RenderTexture.active = null;
        RenderTexture.ReleaseTemporary(rt);
        return result;
    }
}