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# Install nightly package for some functionalities that aren't in alpha
!pip install tensorflow-gpu==2.0.0-beta1
# Install TF Hub for TF2
!pip install 'tensorflow-hub == 0.5'

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_hub as hub

from tensorflow.keras.layers import Dense, Flatten, Conv2D

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implementation 'org.tensorflow:tensorflow-lite:1.14.0'

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aaptOptions {  
    noCompress "tflite"  
}

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data class Recognition(
        var id: String = "",
        var title: String = "",
        var confidence: Float = 0F
    )  {
        override fun toString(): String {
            return "Title = $title, Confidence = $confidence)"
        }
    }

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class Classifier(assetManager: AssetManager, modelPath: String, labelPath: String, inputSize: Int) {
    private var INTERPRETER: Interpreter
    private var LABEL_LIST: List<String>
    private val INPUT_SIZE: Int = inputSize
    private val PIXEL_SIZE: Int = 3
    private val IMAGE_MEAN = 0
    private val IMAGE_STD = 255.0f
    private val MAX_RESULTS = 3
    private val THRESHOLD = 0.4f

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fun recognizeImage(bitmap: Bitmap): List<Classifier.Recognition> {
        val scaledBitmap = Bitmap.createScaledBitmap(bitmap, INPUT_SIZE, INPUT_SIZE, false)
        val byteBuffer = convertBitmapToByteBuffer(scaledBitmap)
        val result = Array(1) { FloatArray(LABEL_LIST.size) }
        INTERPRETER.run(byteBuffer, result)
        return getSortedResult(result)
    }

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 private fun convertBitmapToByteBuffer(bitmap: Bitmap): ByteBuffer {
        val byteBuffer = ByteBuffer.allocateDirect(4 * INPUT_SIZE * INPUT_SIZE * PIXEL_SIZE)
        byteBuffer.order(ByteOrder.nativeOrder())
        val intValues = IntArray(INPUT_SIZE * INPUT_SIZE)

        bitmap.getPixels(intValues, 0, bitmap.width, 0, 0, bitmap.width, bitmap.height)
        var pixel = 0
        for (i in 0 until INPUT_SIZE) {
            for (j in 0 until INPUT_SIZE) {
                val `val` = intValues[pixel++]

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private fun getSortedResult(labelProbArray: Array<FloatArray>): List<Classifier.Recognition> {
        Log.d("Classifier", "List Size:(%d, %d, %d)".format(labelProbArray.size,labelProbArray[0].size,LABEL_LIST.size))

        val pq = PriorityQueue(
            MAX_RESULTS,
            Comparator<Classifier.Recognition> {
                    (_, _, confidence1), (_, _, confidence2)
                -> java.lang.Float.compare(confidence1, confidence2) * -1
            })

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 fun scaleImage(bitmap: Bitmap?): Bitmap {
        val orignalWidth = bitmap!!.width
        val originalHeight = bitmap.height
        val scaleWidth = mInputSize.toFloat() / orignalWidth
        val scaleHeight = mInputSize.toFloat() / originalHeight
        val matrix = Matrix()
        matrix.postScale(scaleWidth, scaleHeight)
        return Bitmap.createBitmap(bitmap, 0, 0, orignalWidth, originalHeight, matrix, true)
    }

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class MainActivity : AppCompatActivity() {
    private lateinit var mClassifier: Classifier
    private lateinit var mBitmap: Bitmap

    private val mCameraRequestCode = 0
    private val mGalleryRequestCode = 2

    private val mInputSize = 224
    private val mModelPath = "plant_disease_model.tflite"
    private val mLabelPath = "plant_labels.txt"