dr_roots.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "eTnWdUN38Q90"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_2Q_X25-FZX",
        "outputId": "fcc1bec7-b2b9-458d-a881-11af0ac35405"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import MobileNet\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "vmFJH1D3ZtWc"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Preprocessing**"
      ],
      "metadata": {
        "id": "eTnWdUN38Q90"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Set up base path\n",
        "base_dir = '/content/drive/MyDrive/medicinal_plants/data'\n",
        "\n",
        "# Get list of plant folders, excluding '.ipynb_checkpoints'\n",
        "plant_folders = [f for f in os.listdir(base_dir)\n",
        "                 if os.path.isdir(os.path.join(base_dir, f)) and f != '.ipynb_checkpoints']\n",
        "\n",
        "# Print plant folders for verification\n",
        "print(\"Plant folders:\", plant_folders)\n",
        "\n",
        "# Set up paths for each split\n",
        "train_dirs = [os.path.join(base_dir, plant, 'Train') for plant in plant_folders]\n",
        "validation_dirs = [os.path.join(base_dir, plant, 'Validation') for plant in plant_folders]\n",
        "test_dirs = [os.path.join(base_dir, plant, 'Test') for plant in plant_folders]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSziDmQdZ9K1",
        "outputId": "70f4129b-1671-4f4e-8ac5-6de09280da55"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plant folders: ['Aloe barbadensis', 'Catharanthus roseus', 'Citrus Limon', 'Mangifera indica', 'Moringa oleifera', 'Psidium guajava', 'Zingiber officianale']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print paths for verification\n",
        "print(\"Train directories:\", train_dirs)\n",
        "print(\"Validation directories:\", validation_dirs)\n",
        "print(\"Test directories:\", test_dirs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8YKJWFAf6sC",
        "outputId": "eedd6a6b-e9fa-4933-ee4a-9ac9029b9dc6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train directories: ['/content/drive/MyDrive/medicinal_plants/data/Aloe barbadensis/Train', '/content/drive/MyDrive/medicinal_plants/data/Catharanthus roseus/Train', '/content/drive/MyDrive/medicinal_plants/data/Citrus Limon/Train', '/content/drive/MyDrive/medicinal_plants/data/Mangifera indica/Train', '/content/drive/MyDrive/medicinal_plants/data/Moringa oleifera/Train', '/content/drive/MyDrive/medicinal_plants/data/Psidium guajava/Train', '/content/drive/MyDrive/medicinal_plants/data/Zingiber officianale/Train']\n",
            "Validation directories: ['/content/drive/MyDrive/medicinal_plants/data/Aloe barbadensis/Validation', '/content/drive/MyDrive/medicinal_plants/data/Catharanthus roseus/Validation', '/content/drive/MyDrive/medicinal_plants/data/Citrus Limon/Validation', '/content/drive/MyDrive/medicinal_plants/data/Mangifera indica/Validation', '/content/drive/MyDrive/medicinal_plants/data/Moringa oleifera/Validation', '/content/drive/MyDrive/medicinal_plants/data/Psidium guajava/Validation', '/content/drive/MyDrive/medicinal_plants/data/Zingiber officianale/Validation']\n",
            "Test directories: ['/content/drive/MyDrive/medicinal_plants/data/Aloe barbadensis/Test', '/content/drive/MyDrive/medicinal_plants/data/Catharanthus roseus/Test', '/content/drive/MyDrive/medicinal_plants/data/Citrus Limon/Test', '/content/drive/MyDrive/medicinal_plants/data/Mangifera indica/Test', '/content/drive/MyDrive/medicinal_plants/data/Moringa oleifera/Test', '/content/drive/MyDrive/medicinal_plants/data/Psidium guajava/Test', '/content/drive/MyDrive/medicinal_plants/data/Zingiber officianale/Test']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Setting up parameters and data generators**"
      ],
      "metadata": {
        "id": "qcglIcPB8Y7x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up parameters\n",
        "img_size = (224, 224)\n",
        "batch_size = 64 #was originally 32\n",
        "num_classes = len(plant_folders)\n",
        "epochs = 10 # reduced from 30"
      ],
      "metadata": {
        "id": "cQU6hWt1ahG3"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.utils import Sequence\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "\n",
        "class CustomDataGenerator(Sequence):\n",
        "    def __init__(self, directories, batch_size, target_size, class_mode='categorical', shuffle=True):\n",
        "        self.directories = directories\n",
        "        self.batch_size = batch_size\n",
        "        self.target_size = target_size\n",
        "        self.class_mode = class_mode\n",
        "        self.shuffle = shuffle\n",
        "\n",
        "        self.classes = [os.path.basename(os.path.dirname(d)) for d in directories]\n",
        "        self.class_indices = {cls: idx for idx, cls in enumerate(self.classes)}\n",
        "\n",
        "        self.filenames = []\n",
        "        for d in directories:\n",
        "            self.filenames.extend([os.path.join(d, f) for f in os.listdir(d) if f.lower().endswith(('.png', '.jpg', '.jpeg'))])\n",
        "\n",
        "        self.num_classes = len(self.classes)\n",
        "        self.indices = np.arange(len(self.filenames))\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        return int(np.ceil(len(self.filenames) / float(self.batch_size)))\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        batch_indices = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]\n",
        "        batch_filenames = [self.filenames[i] for i in batch_indices]\n",
        "\n",
        "        X = np.array([\n",
        "            img_to_array(load_img(f, target_size=self.target_size)) / 255.0\n",
        "            for f in batch_filenames\n",
        "        ])\n",
        "\n",
        "        if self.class_mode == 'categorical':\n",
        "            y = np.array([\n",
        "                self.class_indices[os.path.basename(os.path.dirname(os.path.dirname(f)))]\n",
        "                for f in batch_filenames\n",
        "            ])\n",
        "            y = np.eye(self.num_classes)[y]\n",
        "        elif self.class_mode == 'binary':\n",
        "            y = np.array([\n",
        "                self.class_indices[os.path.basename(os.path.dirname(os.path.dirname(f)))]\n",
        "                for f in batch_filenames\n",
        "            ])\n",
        "        else:\n",
        "            y = None\n",
        "\n",
        "        return X, y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        if self.shuffle:\n",
        "            np.random.shuffle(self.indices)\n",
        "\n",
        "# Create generators\n",
        "train_generator = CustomDataGenerator(train_dirs, batch_size, img_size)\n",
        "validation_generator = CustomDataGenerator(validation_dirs, batch_size, img_size)\n",
        "test_generator = CustomDataGenerator(test_dirs, batch_size, img_size)\n",
        "\n",
        "# Print class indices\n",
        "print(\"Class indices:\", train_generator.class_indices)\n",
        "\n",
        "# Invert the dictionary to map indices to class names\n",
        "idx_to_class = {v: k for k, v in train_generator.class_indices.items()}\n",
        "print(\"Index to class mapping:\", idx_to_class)\n",
        "\n",
        "# Calculate steps per epoch\n",
        "steps_per_epoch = len(train_generator)\n",
        "validation_steps = len(validation_generator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Fy8zAhspLC0",
        "outputId": "7457e615-524f-4df3-abcc-d13830d1acdc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class indices: {'Aloe barbadensis': 0, 'Catharanthus roseus': 1, 'Citrus Limon': 2, 'Mangifera indica': 3, 'Moringa oleifera': 4, 'Psidium guajava': 5, 'Zingiber officianale': 6}\n",
            "Index to class mapping: {0: 'Aloe barbadensis', 1: 'Catharanthus roseus', 2: 'Citrus Limon', 3: 'Mangifera indica', 4: 'Moringa oleifera', 5: 'Psidium guajava', 6: 'Zingiber officianale'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Training the model**"
      ],
      "metadata": {
        "id": "jQZ8cH8L8gTN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from tensorflow.keras.regularizers import l2\n",
        "\n",
        "# Load pre-trained MobileNet model\n",
        "base_model = MobileNet(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Freeze base model layers\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Add new layers\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "\n",
        "# Add a dropout layer after GlobalAveragePooling2D\n",
        "x = Dropout(0.5)(x)\n",
        "\n",
        "# Add L2 regularization and reduce neurons\n",
        "x = Dense(128, activation='relu', kernel_regularizer=l2(0.01))(x)\n",
        "\n",
        "# Add another dropout layer\n",
        "x = Dropout(0.3)(x)\n",
        "\n",
        "output = Dense(num_classes, activation='softmax')(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6y5vgYGasAY",
        "outputId": "06d9cca7-d4c3-4cb0-c59c-b84cb13f9b88"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5\n",
            "17225924/17225924 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the final model\n",
        "model = Model(inputs=base_model.input, outputs=output)\n",
        "\n",
        "# Compile the model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ZKDLw9k4a1FW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J20o-bht15EP",
        "outputId": "c64ea3fd-b0f9-471c-8f52-5c29d2cf1535"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " conv1 (Conv2D)              (None, 112, 112, 32)      864       \n",
            "                                                                 \n",
            " conv1_bn (BatchNormalizati  (None, 112, 112, 32)      128       \n",
            " on)                                                             \n",
            "                                                                 \n",
            " conv1_relu (ReLU)           (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_dw_1 (DepthwiseConv2D  (None, 112, 112, 32)      288       \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_1_bn (BatchNormali  (None, 112, 112, 32)      128       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_1_relu (ReLU)       (None, 112, 112, 32)      0         \n",
            "                                                                 \n",
            " conv_pw_1 (Conv2D)          (None, 112, 112, 64)      2048      \n",
            "                                                                 \n",
            " conv_pw_1_bn (BatchNormali  (None, 112, 112, 64)      256       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_1_relu (ReLU)       (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)      0         \n",
            "                                                                 \n",
            " conv_dw_2 (DepthwiseConv2D  (None, 56, 56, 64)        576       \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_2_bn (BatchNormali  (None, 56, 56, 64)        256       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_2_relu (ReLU)       (None, 56, 56, 64)        0         \n",
            "                                                                 \n",
            " conv_pw_2 (Conv2D)          (None, 56, 56, 128)       8192      \n",
            "                                                                 \n",
            " conv_pw_2_bn (BatchNormali  (None, 56, 56, 128)       512       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_2_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_3 (DepthwiseConv2D  (None, 56, 56, 128)       1152      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_3_bn (BatchNormali  (None, 56, 56, 128)       512       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_3 (Conv2D)          (None, 56, 56, 128)       16384     \n",
            "                                                                 \n",
            " conv_pw_3_bn (BatchNormali  (None, 56, 56, 128)       512       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_3_relu (ReLU)       (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)       0         \n",
            "                                                                 \n",
            " conv_dw_4 (DepthwiseConv2D  (None, 28, 28, 128)       1152      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_4_bn (BatchNormali  (None, 28, 28, 128)       512       \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_4_relu (ReLU)       (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv_pw_4 (Conv2D)          (None, 28, 28, 256)       32768     \n",
            "                                                                 \n",
            " conv_pw_4_bn (BatchNormali  (None, 28, 28, 256)       1024      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_4_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_5 (DepthwiseConv2D  (None, 28, 28, 256)       2304      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_5_bn (BatchNormali  (None, 28, 28, 256)       1024      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_5 (Conv2D)          (None, 28, 28, 256)       65536     \n",
            "                                                                 \n",
            " conv_pw_5_bn (BatchNormali  (None, 28, 28, 256)       1024      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_5_relu (ReLU)       (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)       0         \n",
            "                                                                 \n",
            " conv_dw_6 (DepthwiseConv2D  (None, 14, 14, 256)       2304      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_6_bn (BatchNormali  (None, 14, 14, 256)       1024      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_6_relu (ReLU)       (None, 14, 14, 256)       0         \n",
            "                                                                 \n",
            " conv_pw_6 (Conv2D)          (None, 14, 14, 512)       131072    \n",
            "                                                                 \n",
            " conv_pw_6_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_6_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_7 (DepthwiseConv2D  (None, 14, 14, 512)       4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_7_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_7 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_7_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_7_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_8 (DepthwiseConv2D  (None, 14, 14, 512)       4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_8_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_8 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_8_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_8_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_9 (DepthwiseConv2D  (None, 14, 14, 512)       4608      \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_9_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_dw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_9 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_9_bn (BatchNormali  (None, 14, 14, 512)       2048      \n",
            " zation)                                                         \n",
            "                                                                 \n",
            " conv_pw_9_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_10 (DepthwiseConv2  (None, 14, 14, 512)       4608      \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv_dw_10_bn (BatchNormal  (None, 14, 14, 512)       2048      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_dw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_10 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_10_bn (BatchNormal  (None, 14, 14, 512)       2048      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_pw_10_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_dw_11 (DepthwiseConv2  (None, 14, 14, 512)       4608      \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv_dw_11_bn (BatchNormal  (None, 14, 14, 512)       2048      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_dw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pw_11 (Conv2D)         (None, 14, 14, 512)       262144    \n",
            "                                                                 \n",
            " conv_pw_11_bn (BatchNormal  (None, 14, 14, 512)       2048      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_pw_11_relu (ReLU)      (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " conv_pad_12 (ZeroPadding2D  (None, 15, 15, 512)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv_dw_12 (DepthwiseConv2  (None, 7, 7, 512)         4608      \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv_dw_12_bn (BatchNormal  (None, 7, 7, 512)         2048      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_dw_12_relu (ReLU)      (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " conv_pw_12 (Conv2D)         (None, 7, 7, 1024)        524288    \n",
            "                                                                 \n",
            " conv_pw_12_bn (BatchNormal  (None, 7, 7, 1024)        4096      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_dw_13 (DepthwiseConv2  (None, 7, 7, 1024)        9216      \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv_dw_13_bn (BatchNormal  (None, 7, 7, 1024)        4096      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " conv_pw_13 (Conv2D)         (None, 7, 7, 1024)        1048576   \n",
            "                                                                 \n",
            " conv_pw_13_bn (BatchNormal  (None, 7, 7, 1024)        4096      \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)        0         \n",
            "                                                                 \n",
            " global_average_pooling2d (  (None, 1024)              0         \n",
            " GlobalAveragePooling2D)                                         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               131200    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 7)                 903       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3360967 (12.82 MB)\n",
            "Trainable params: 132103 (516.03 KB)\n",
            "Non-trainable params: 3228864 (12.32 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate total number of samples\n",
        "train_samples = sum([len(os.listdir(d)) for d in train_dirs])\n",
        "val_samples = sum([len(os.listdir(d)) for d in validation_dirs])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_samples // batch_size,\n",
        "    epochs=epochs,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=val_samples // batch_size,\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fesS3jSwF1J",
        "outputId": "636925db-a72c-44e8-da4d-ce97c20bc7aa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "37/37 [==============================] - 642s 17s/step - loss: 3.4349 - accuracy: 0.5328 - val_loss: 2.4210 - val_accuracy: 0.8555\n",
            "Epoch 2/10\n",
            "37/37 [==============================] - 156s 4s/step - loss: 2.2982 - accuracy: 0.8041 - val_loss: 1.7986 - val_accuracy: 0.9336\n",
            "Epoch 3/10\n",
            "37/37 [==============================] - 167s 5s/step - loss: 1.8167 - accuracy: 0.8544 - val_loss: 1.4512 - val_accuracy: 0.9453\n",
            "Epoch 4/10\n",
            "37/37 [==============================] - 168s 5s/step - loss: 1.4846 - accuracy: 0.8836 - val_loss: 1.1883 - val_accuracy: 0.9590\n",
            "Epoch 5/10\n",
            "37/37 [==============================] - 168s 5s/step - loss: 1.2422 - accuracy: 0.8925 - val_loss: 0.9982 - val_accuracy: 0.9629\n",
            "Epoch 6/10\n",
            "37/37 [==============================] - 170s 5s/step - loss: 1.0601 - accuracy: 0.9073 - val_loss: 0.8622 - val_accuracy: 0.9492\n",
            "Epoch 7/10\n",
            "37/37 [==============================] - 171s 5s/step - loss: 0.9201 - accuracy: 0.9086 - val_loss: 0.7456 - val_accuracy: 0.9609\n",
            "Epoch 8/10\n",
            "37/37 [==============================] - 179s 5s/step - loss: 0.7913 - accuracy: 0.9268 - val_loss: 0.6452 - val_accuracy: 0.9688\n",
            "Epoch 9/10\n",
            "37/37 [==============================] - 170s 5s/step - loss: 0.7095 - accuracy: 0.9217 - val_loss: 0.5881 - val_accuracy: 0.9531\n",
            "Epoch 10/10\n",
            "37/37 [==============================] - 166s 5s/step - loss: 0.6676 - accuracy: 0.9175 - val_loss: 0.5368 - val_accuracy: 0.9648\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Evaluating the Model**"
      ],
      "metadata": {
        "id": "mzISl6FS8nLz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "test_loss, test_accuracy = model.evaluate(test_generator)\n",
        "print(f\"Test accuracy: {test_accuracy:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSIm1rvnbCm_",
        "outputId": "42eabd46-dc29-4490-861c-d14d461c10f7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 120s 14s/step - loss: 0.5425 - accuracy: 0.9582\n",
            "Test accuracy: 0.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have stored the history in a variable called 'history'\n",
        "# If not, you'll need to capture the return value of model.fit()\n",
        "# history = model.fit(...)\n",
        "\n",
        "# Create a figure with two subplots side by side\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "ax1.plot(history.history['accuracy'])\n",
        "ax1.plot(history.history['val_accuracy'])\n",
        "ax1.set_title('Model Accuracy')\n",
        "ax1.set_ylabel('Accuracy')\n",
        "ax1.set_xlabel('Epoch')\n",
        "ax1.legend(['Train', 'Validation'], loc='lower right')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "ax2.plot(history.history['loss'])\n",
        "ax2.plot(history.history['val_loss'])\n",
        "ax2.set_title('Model Loss')\n",
        "ax2.set_ylabel('Loss')\n",
        "ax2.set_xlabel('Epoch')\n",
        "ax2.legend(['Train', 'Validation'], loc='upper right')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "i2LAcduMAq0v",
        "outputId": "88b4efa6-cd16-4025-cc2d-ee172ca191ea"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming your custom generator is called 'test_generator'\n",
        "\n",
        "# Get the true labels\n",
        "y_true = []\n",
        "y_pred = []\n",
        "\n",
        "# Iterate through the entire dataset\n",
        "for i in range(len(test_generator)):\n",
        "    x, y = test_generator[i]\n",
        "    batch_pred = model.predict(x)\n",
        "    y_true.extend(np.argmax(y, axis=1))\n",
        "    y_pred.extend(np.argmax(batch_pred, axis=1))\n",
        "\n",
        "# Convert to numpy arrays\n",
        "y_true = np.array(y_true)\n",
        "y_pred = np.array(y_pred)\n",
        "\n",
        "# Compute confusion matrix\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "# Get class names (you might need to provide these manually if not available in the generator)\n",
        "class_names = list(range(num_classes))  # Replace with actual class names if available\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.ylabel('True Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "lLLlf2X9BKBZ",
        "outputId": "4934456c-33d6-4cb8-bd5d-50c1b9c242f9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 8s 4s/step\n",
            "2/2 [==============================] - 4s 1s/step\n",
            "2/2 [==============================] - 2s 1s/step\n",
            "2/2 [==============================] - 4s 2s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "2/2 [==============================] - 4s 2s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "1/1 [==============================] - 1s 993ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming your custom generator is called 'test_generator'\n",
        "\n",
        "# Get the true labels and predictions\n",
        "y_true = []\n",
        "y_pred = []\n",
        "\n",
        "# Iterate through the entire dataset\n",
        "for i in range(len(test_generator)):\n",
        "    x, y = test_generator[i]\n",
        "    batch_pred = model.predict(x)\n",
        "    y_true.extend(np.argmax(y, axis=1))\n",
        "    y_pred.extend(np.argmax(batch_pred, axis=1))\n",
        "\n",
        "# Convert to numpy arrays\n",
        "y_true = np.array(y_true)\n",
        "y_pred = np.array(y_pred)\n",
        "\n",
        "# Get the number of classes\n",
        "num_classes = len(np.unique(y_true))\n",
        "\n",
        "# Create one-vs-all confusion matrices\n",
        "one_vs_all_cms = []\n",
        "\n",
        "for i in range(num_classes):\n",
        "    # Create binary classifications\n",
        "    y_true_binary = (y_true == i).astype(int)\n",
        "    y_pred_binary = (y_pred == i).astype(int)\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true_binary, y_pred_binary)\n",
        "    one_vs_all_cms.append(cm)\n",
        "\n",
        "# Plot the one-vs-all confusion matrices\n",
        "fig, axes = plt.subplots(nrows=(num_classes+1)//2, ncols=2, figsize=(15, 5*((num_classes+1)//2)))\n",
        "axes = axes.flatten()\n",
        "\n",
        "class_names = [f'Class {i}' for i in range(num_classes)]  # Replace with actual class names if available\n",
        "\n",
        "for i, cm in enumerate(one_vs_all_cms):\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[i],\n",
        "                xticklabels=['Other', class_names[i]],\n",
        "                yticklabels=['Other', class_names[i]])\n",
        "    axes[i].set_title(f'One-vs-All: {class_names[i]} vs Rest')\n",
        "    axes[i].set_ylabel('True Label')\n",
        "    axes[i].set_xlabel('Predicted Label')\n",
        "\n",
        "# Remove any unused subplots\n",
        "for i in range(num_classes, len(axes)):\n",
        "    fig.delaxes(axes[i])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9jkMa0qiCsbB",
        "outputId": "84c935d0-0432-44b5-9699-86c3a9915a45"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 7s 3s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "2/2 [==============================] - 2s 1s/step\n",
            "2/2 [==============================] - 4s 2s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "2/2 [==============================] - 4s 2s/step\n",
            "2/2 [==============================] - 3s 1s/step\n",
            "1/1 [==============================] - 1s 503ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x2000 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Saving the model and class Mapping**"
      ],
      "metadata": {
        "id": "k-OjdzUu8rif"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Specify the directory\n",
        "save_dir = '/content/drive/MyDrive/medicinal_plants'\n",
        "\n",
        "# Ensure the directory exists\n",
        "os.makedirs(save_dir, exist_ok=True)\n",
        "\n",
        "# Save the model\n",
        "model_path = os.path.join(save_dir, 'dr_roots_model.h5')\n",
        "model.save(model_path)\n",
        "\n",
        "print(f\"Model saved to {model_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ct5bM_ebG5s",
        "outputId": "c81ff09b-1e96-4ec8-f79a-e05b96cd7535"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved to /content/drive/MyDrive/medicinal_plants/dr_roots_model.h5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Save the class mapping\n",
        "class_mapping_path = os.path.join(save_dir, 'class_mapping.json')\n",
        "with open(class_mapping_path, 'w') as f:\n",
        "    json.dump(idx_to_class, f)\n",
        "\n",
        "print(f\"Class mapping saved to {class_mapping_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzPLE-Ps46oj",
        "outputId": "5ef6b7b9-cb68-42d9-a86f-41908df60cb4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class mapping saved to /content/drive/MyDrive/medicinal_plants/class_mapping.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Testing Predictions**"
      ],
      "metadata": {
        "id": "L2-jMJKt8vpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "from PIL import Image\n",
        "import io\n",
        "\n",
        "def predict_uploaded_image(model, class_mapping):\n",
        "    # Upload an image\n",
        "    uploaded = files.upload()\n",
        "\n",
        "    for fn in uploaded.keys():\n",
        "        # Open the uploaded image\n",
        "        image = Image.open(io.BytesIO(uploaded[fn]))\n",
        "\n",
        "        # Preprocess the image\n",
        "        image = image.resize((224, 224))\n",
        "        image_array = np.array(image) / 255.0\n",
        "        image_array = np.expand_dims(image_array, axis=0)\n",
        "\n",
        "        # Make prediction\n",
        "        prediction = model.predict(image_array)\n",
        "        predicted_class = np.argmax(prediction, axis=1)[0]\n",
        "\n",
        "        # Get the class name\n",
        "        class_name = class_mapping[predicted_class]\n",
        "        confidence = prediction[0][predicted_class] * 100\n",
        "\n",
        "        print(f\"Predicted class: {class_name}\")\n",
        "        print(f\"Confidence: {confidence:.2f}%\")\n",
        "\n",
        "        # Display the image\n",
        "        plt.imshow(image)\n",
        "        plt.axis('off')\n",
        "        plt.title(f\"Predicted: {class_name}\")\n",
        "        plt.show()\n",
        "\n",
        "# Use the function to predict an uploaded image\n",
        "predict_uploaded_image(model, idx_to_class)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "_NPdqOiHtrB9",
        "outputId": "0c3179ba-db23-4139-93b0-fc5b6ed641f0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b30505d6-9690-467e-913d-e1dc6aa4970b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b30505d6-9690-467e-913d-e1dc6aa4970b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Vinca_Catharanthus_roseus_in_Bangladesh.jpg to Vinca_Catharanthus_roseus_in_Bangladesh.jpg\n",
            "1/1 [==============================] - 0s 105ms/step\n",
            "Predicted class: Catharanthus roseus\n",
            "Confidence: 97.02%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Load your model\n",
        "model = tf.keras.models.load_model(\"/content/drive/MyDrive/medicinal_plants/dr_roots_model.h5\")\n",
        "\n",
        "# Convert the model\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "with open('/content/drive/MyDrive/medicinal_plants/dr_roots_model.tflite', 'wb') as f:\n",
        "    f.write(tflite_model)"
      ],
      "metadata": {
        "id": "RPBTPQJoE6es"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}