{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:06.607800Z",
     "iopub.status.busy": "2023-04-05T10:18:06.607363Z",
     "iopub.status.idle": "2023-04-05T10:18:17.442048Z",
     "shell.execute_reply": "2023-04-05T10:18:17.440985Z",
     "shell.execute_reply.started": "2023-04-05T10:18:06.607708Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import skimage.io\n",
    "import tensorflow\n",
    "import glob\n",
    "import tqdm\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "\n",
    "from skimage.io import imread, imshow\n",
    "from skimage.transform import resize\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.layers import InputLayer, Dense, BatchNormalization, Dropout, Flatten, Activation\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "import torch\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:17.445317Z",
     "iopub.status.busy": "2023-04-05T10:18:17.444560Z",
     "iopub.status.idle": "2023-04-05T10:18:17.817676Z",
     "shell.execute_reply": "2023-04-05T10:18:17.816556Z",
     "shell.execute_reply.started": "2023-04-05T10:18:17.445259Z"
    }
   },
   "outputs": [],
   "source": [
    "train_normal = glob.glob('data_small/chest-xray-pneumonia/chest_xray/train/NORMAL/*.jpeg')\n",
    "a = len(train_normal)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:17.820239Z",
     "iopub.status.busy": "2023-04-05T10:18:17.819570Z",
     "iopub.status.idle": "2023-04-05T10:18:18.104917Z",
     "shell.execute_reply": "2023-04-05T10:18:18.103937Z",
     "shell.execute_reply.started": "2023-04-05T10:18:17.820200Z"
    }
   },
   "outputs": [],
   "source": [
    "train_pneumonia = glob.glob('data_small/chest-xray-pneumonia/chest_xray/train/PNEUMONIA/*.jpeg')\n",
    "b = len(train_pneumonia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:18.108254Z",
     "iopub.status.busy": "2023-04-05T10:18:18.107570Z",
     "iopub.status.idle": "2023-04-05T10:18:18.115183Z",
     "shell.execute_reply": "2023-04-05T10:18:18.114145Z",
     "shell.execute_reply.started": "2023-04-05T10:18:18.108213Z"
    }
   },
   "outputs": [],
   "source": [
    "print(\"Total nos. of training images are: {}\".format(a + b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:18.120784Z",
     "iopub.status.busy": "2023-04-05T10:18:18.119787Z",
     "iopub.status.idle": "2023-04-05T10:18:18.127376Z",
     "shell.execute_reply": "2023-04-05T10:18:18.126331Z",
     "shell.execute_reply.started": "2023-04-05T10:18:18.120751Z"
    }
   },
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale = 1.0 / 255.0,\n",
    "                                   zoom_range = 0.4,\n",
    "                                   validation_split = 0.2)\n",
    "\n",
    "valid_datagen = ImageDataGenerator(rescale = 1.0 / 255.0,\n",
    "                                   validation_split = 0.2)\n",
    "\n",
    "test_datagen  = ImageDataGenerator(rescale = 1.0 / 255.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:18.129958Z",
     "iopub.status.busy": "2023-04-05T10:18:18.128743Z",
     "iopub.status.idle": "2023-04-05T10:18:22.573195Z",
     "shell.execute_reply": "2023-04-05T10:18:22.572009Z",
     "shell.execute_reply.started": "2023-04-05T10:18:18.129920Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 422 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_dataset = train_datagen.flow_from_directory(directory = 'data_small/chest-xray-pneumonia/chest_xray/train',\n",
    "                                                  target_size = (224,224),\n",
    "                                                  class_mode = 'binary',\n",
    "                                                  subset = 'training',\n",
    "                                                  batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:22.576329Z",
     "iopub.status.busy": "2023-04-05T10:18:22.575200Z",
     "iopub.status.idle": "2023-04-05T10:18:23.626462Z",
     "shell.execute_reply": "2023-04-05T10:18:23.625318Z",
     "shell.execute_reply.started": "2023-04-05T10:18:22.576259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 105 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "valid_dataset = valid_datagen.flow_from_directory(directory = 'data_small/chest-xray-pneumonia/chest_xray/train',\n",
    "                                                  target_size = (224,224),\n",
    "                                                  class_mode = 'binary',\n",
    "                                                  subset = 'validation',\n",
    "                                                  batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:23.629025Z",
     "iopub.status.busy": "2023-04-05T10:18:23.628340Z",
     "iopub.status.idle": "2023-04-05T10:18:23.636803Z",
     "shell.execute_reply": "2023-04-05T10:18:23.635450Z",
     "shell.execute_reply.started": "2023-04-05T10:18:23.628982Z"
    }
   },
   "outputs": [],
   "source": [
    "train_dataset.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:23.639332Z",
     "iopub.status.busy": "2023-04-05T10:18:23.638869Z",
     "iopub.status.idle": "2023-04-05T10:18:23.646306Z",
     "shell.execute_reply": "2023-04-05T10:18:23.645111Z",
     "shell.execute_reply.started": "2023-04-05T10:18:23.639294Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:23.648339Z",
     "iopub.status.busy": "2023-04-05T10:18:23.647951Z",
     "iopub.status.idle": "2023-04-05T10:18:23.657485Z",
     "shell.execute_reply": "2023-04-05T10:18:23.656337Z",
     "shell.execute_reply.started": "2023-04-05T10:18:23.648302Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = Path('data_small/chest-xray-pneumonia/chest_xray')\n",
    "test_dir = data_dir / 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:23.659988Z",
     "iopub.status.busy": "2023-04-05T10:18:23.659233Z",
     "iopub.status.idle": "2023-04-05T10:18:23.669765Z",
     "shell.execute_reply": "2023-04-05T10:18:23.668763Z",
     "shell.execute_reply.started": "2023-04-05T10:18:23.659953Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "normal_cases_dir = test_dir / 'NORMAL'\n",
    "pneumonia_cases_dir = test_dir / 'PNEUMONIA'\n",
    "\n",
    "normal_cases = normal_cases_dir.glob('*.jpeg')\n",
    "pneumonia_cases = pneumonia_cases_dir.glob('*.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:23.671809Z",
     "iopub.status.busy": "2023-04-05T10:18:23.671149Z",
     "iopub.status.idle": "2023-04-05T10:18:32.059237Z",
     "shell.execute_reply": "2023-04-05T10:18:32.058118Z",
     "shell.execute_reply.started": "2023-04-05T10:18:23.671775Z"
    }
   },
   "outputs": [],
   "source": [
    "# Defining Model\n",
    "\n",
    "base_model = VGG16(input_shape=(224,224,3), \n",
    "                   include_top=False,\n",
    "                   weights=\"imagenet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.061048Z",
     "iopub.status.busy": "2023-04-05T10:18:32.060675Z",
     "iopub.status.idle": "2023-04-05T10:18:32.067441Z",
     "shell.execute_reply": "2023-04-05T10:18:32.066183Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.061009Z"
    }
   },
   "outputs": [],
   "source": [
    "for layer in base_model.layers:\n",
    "    layer.trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.075338Z",
     "iopub.status.busy": "2023-04-05T10:18:32.074331Z",
     "iopub.status.idle": "2023-04-05T10:18:32.085477Z",
     "shell.execute_reply": "2023-04-05T10:18:32.084451Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.075298Z"
    }
   },
   "outputs": [],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.087621Z",
     "iopub.status.busy": "2023-04-05T10:18:32.087063Z",
     "iopub.status.idle": "2023-04-05T10:18:32.225814Z",
     "shell.execute_reply": "2023-04-05T10:18:32.224768Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.087580Z"
    }
   },
   "outputs": [],
   "source": [
    "# Defining Layers\n",
    "\n",
    "model=Sequential()\n",
    "model.add(base_model)\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(1024,kernel_initializer='he_uniform'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1024,kernel_initializer='he_uniform'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.227852Z",
     "iopub.status.busy": "2023-04-05T10:18:32.227448Z",
     "iopub.status.idle": "2023-04-05T10:18:32.236828Z",
     "shell.execute_reply": "2023-04-05T10:18:32.235608Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.227804Z"
    }
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.239629Z",
     "iopub.status.busy": "2023-04-05T10:18:32.238822Z",
     "iopub.status.idle": "2023-04-05T10:18:32.259834Z",
     "shell.execute_reply": "2023-04-05T10:18:32.258924Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.239593Z"
    }
   },
   "outputs": [],
   "source": [
    "# Model Compile \n",
    "\n",
    "OPT    = tensorflow.keras.optimizers.Adam(learning_rate=0.001)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              metrics=[tensorflow.keras.metrics.AUC(name = 'auc')],\n",
    "              optimizer=OPT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.262881Z",
     "iopub.status.busy": "2023-04-05T10:18:32.262172Z",
     "iopub.status.idle": "2023-04-05T10:18:32.269164Z",
     "shell.execute_reply": "2023-04-05T10:18:32.267925Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.262846Z"
    }
   },
   "outputs": [],
   "source": [
    "# Defining Callbacks\n",
    "\n",
    "filepath = 'data_small/best_weights.keras'\n",
    "\n",
    "earlystopping = EarlyStopping(monitor = 'val_auc', \n",
    "                              mode = 'max' , \n",
    "                              patience = 3,\n",
    "                              verbose = 1)\n",
    "\n",
    "checkpoint    = ModelCheckpoint(filepath, \n",
    "                                monitor = 'val_auc', \n",
    "                                mode='max', \n",
    "                                save_best_only=True, \n",
    "                                verbose = 1)\n",
    "\n",
    "\n",
    "callback_list = [earlystopping, checkpoint]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " model_history = model.load('Janos_Scan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:18:32.271157Z",
     "iopub.status.busy": "2023-04-05T10:18:32.270637Z",
     "iopub.status.idle": "2023-04-05T10:21:23.872556Z",
     "shell.execute_reply": "2023-04-05T10:21:23.871478Z",
     "shell.execute_reply.started": "2023-04-05T10:18:32.271122Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6s/step - auc: 0.8211 - loss: 0.5703\n",
      "Epoch 1: val_auc improved from -inf to 0.97200, saving model to data_small/best_weights.keras\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 12s/step - auc: 0.8283 - loss: 0.5611 - val_auc: 0.9720 - val_loss: 0.5673\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model_history=model.fit(train_dataset,\n",
    "                        validation_data=valid_dataset,\n",
    "                        epochs = 1,\n",
    "                        callbacks = callback_list,\n",
    "                        verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:21:23.875033Z",
     "iopub.status.busy": "2023-04-05T10:21:23.874599Z",
     "iopub.status.idle": "2023-04-05T10:21:23.882659Z",
     "shell.execute_reply": "2023-04-05T10:21:23.881414Z",
     "shell.execute_reply.started": "2023-04-05T10:21:23.874990Z"
    }
   },
   "outputs": [],
   "source": [
    "class_names = ['PNEUMONIA','NORMAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T10:32:34.305363Z",
     "iopub.status.busy": "2023-04-05T10:32:34.304982Z",
     "iopub.status.idle": "2023-04-05T10:32:35.479938Z",
     "shell.execute_reply": "2023-04-05T10:32:35.476824Z",
     "shell.execute_reply.started": "2023-04-05T10:32:34.305331Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3s/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   PNEUMONIA     0.2656    1.0000    0.4198        17\n",
      "      NORMAL     0.0000    0.0000    0.0000        47\n",
      "\n",
      "    accuracy                         0.2656        64\n",
      "   macro avg     0.1328    0.5000    0.2099        64\n",
      "weighted avg     0.0706    0.2656    0.1115        64\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "prediction_classes = np.array([])\n",
    "true_classes =  np.array([])\n",
    "\n",
    "# for x, y in valid_dataset:\n",
    "for i, (x, y) in enumerate(valid_dataset):\n",
    "    if i >= 1:  # stop after 1 batch\n",
    "        break\n",
    "    \n",
    "    # fix ---- type and shape mismatch\n",
    "#   prediction_classes = np.concatenate([prediction_classes,\n",
    "#                        np.argmax(model.predict(x), axis = -1)])\n",
    "#   true_classes = np.concatenate([true_classes, np.argmax(y.numpy(), axis=-1)])\n",
    "\n",
    "    prediction_classes = np.concatenate([prediction_classes, \n",
    "                                         np.atleast_1d(np.argmax(model.predict(x), axis=-1))])\n",
    "    true_classes = np.concatenate([true_classes, y])\n",
    "\n",
    "print(classification_report(true_classes, prediction_classes, target_names=class_names, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.245188Z",
     "iopub.status.idle": "2023-04-05T10:21:24.246046Z",
     "shell.execute_reply": "2023-04-05T10:21:24.245788Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.245758Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.247585Z",
     "iopub.status.idle": "2023-04-05T10:21:24.248394Z",
     "shell.execute_reply": "2023-04-05T10:21:24.248114Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.248086Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.249866Z",
     "iopub.status.idle": "2023-04-05T10:21:24.250685Z",
     "shell.execute_reply": "2023-04-05T10:21:24.250412Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.250384Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "plt.plot(model_history.history['loss'])\n",
    "plt.plot(model_history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left', bbox_to_anchor=(1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.252178Z",
     "iopub.status.idle": "2023-04-05T10:21:24.253051Z",
     "shell.execute_reply": "2023-04-05T10:21:24.252731Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.252702Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot(model_history.history['auc'])\n",
    "plt.plot(model_history.history['val_auc'])\n",
    "plt.title('Model AUC')\n",
    "plt.ylabel('AUC')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left', bbox_to_anchor=(1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.254567Z",
     "iopub.status.idle": "2023-04-05T10:21:24.255417Z",
     "shell.execute_reply": "2023-04-05T10:21:24.255118Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.255090Z"
    }
   },
   "outputs": [],
   "source": [
    "test_dataset = test_datagen.flow_from_directory(directory = 'data_small/chest-xray-pneumonia/chest_xray/test',\n",
    "                                                target_size = (224,224),\n",
    "                                                class_mode = 'binary',\n",
    "                                                batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.256869Z",
     "iopub.status.idle": "2023-04-05T10:21:24.257685Z",
     "shell.execute_reply": "2023-04-05T10:21:24.257383Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.257356Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "normal_cases_dir = test_dir / 'NORMAL'\n",
    "pneumonia_cases_dir = test_dir / 'PNEUMONIA'\n",
    "\n",
    "normal_cases = normal_cases_dir.glob('*.jpeg')\n",
    "pneumonia_cases = pneumonia_cases_dir.glob('*.jpeg')\n",
    "test_labels=[]\n",
    "test_data= []\n",
    "for img in normal_cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    img = cv2.resize(img, (224,224))\n",
    "    if img.shape[2]==1:\n",
    "        img = np.dstack([img,img,img])\n",
    "    else:\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = img.astype(np.float32)/225.\n",
    "    \n",
    "    label = tensorflow.keras.utils.to_categorical(0,num_classes=2)\n",
    "    test_labels.append(label)\n",
    "    test_data.append(img)\n",
    "for img in pneumonia_cases:\n",
    "    \n",
    "    img = cv2.imread(str(img))\n",
    "    img = cv2.resize(img, (224,224))\n",
    "    if img.shape[2]==1:\n",
    "        img = np.dstack([img,img,img])\n",
    "    else:\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = img.astype(np.float32)/225.\n",
    "    \n",
    "    label = tensorflow.keras.utils.to_categorical(1,num_classes=2)\n",
    "    test_labels.append(label)\n",
    "    test_data.append(img)\n",
    "    \n",
    "test_data = np.array(test_data)\n",
    "test_labels = np.array(test_labels)\n",
    "print(test_data.shape)\n",
    "print(test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.259124Z",
     "iopub.status.idle": "2023-04-05T10:21:24.259883Z",
     "shell.execute_reply": "2023-04-05T10:21:24.259643Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.259617Z"
    }
   },
   "outputs": [],
   "source": [
    "test_loss, test_score = model.evaluate(test_data, test_labels, batch_size=16)\n",
    "print(test_loss)\n",
    "print(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.261323Z",
     "iopub.status.idle": "2023-04-05T10:21:24.262137Z",
     "shell.execute_reply": "2023-04-05T10:21:24.261878Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.261850Z"
    }
   },
   "outputs": [],
   "source": [
    "preds = model.predict(test_data,batch_size=16)\n",
    "preds = np.argmax(preds,axis=-1)\n",
    "\n",
    "orig_test_labels= np.argmax(test_labels,axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.263594Z",
     "iopub.status.idle": "2023-04-05T10:21:24.264368Z",
     "shell.execute_reply": "2023-04-05T10:21:24.264088Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.264062Z"
    }
   },
   "outputs": [],
   "source": [
    "print(orig_test_labels.shape)\n",
    "print(preds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.265811Z",
     "iopub.status.idle": "2023-04-05T10:21:24.266536Z",
     "shell.execute_reply": "2023-04-05T10:21:24.266296Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.266257Z"
    }
   },
   "outputs": [],
   "source": [
    "test_score =model.evaluate(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.267868Z",
     "iopub.status.idle": "2023-04-05T10:21:24.268636Z",
     "shell.execute_reply": "2023-04-05T10:21:24.268382Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.268357Z"
    }
   },
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.270000Z",
     "iopub.status.idle": "2023-04-05T10:21:24.270780Z",
     "shell.execute_reply": "2023-04-05T10:21:24.270518Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.270493Z"
    }
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(orig_test_labels, preds)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm, figsize = (12,8),hide_ticks = True,cmap = plt.cm.Blues)\n",
    "plt.xticks(range(2),['Normal','Pneumonia'],fontsize=16)\n",
    "plt.yticks(range(2),['Normal','Pneumonia'],fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.272213Z",
     "iopub.status.idle": "2023-04-05T10:21:24.272977Z",
     "shell.execute_reply": "2023-04-05T10:21:24.272737Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.272712Z"
    }
   },
   "outputs": [],
   "source": [
    "# Test Case 1: NORMAL\n",
    "\n",
    "dic = test_dataset.class_indices\n",
    "idc = {k:v for v, k in dic.items()}\n",
    "\n",
    "img = load_img('data_small/chest-xray-pneumonia/chest_xray/test/NORMAL/IM-0035-0001.jpeg', target_size=(224,224))\n",
    "img = img_to_array(img)\n",
    "img = img/255\n",
    "imshow(img)\n",
    "plt.axis('off')\n",
    "img = np.expand_dims(img,axis=0)\n",
    "predict_prob=model.predict(img)\n",
    "print(model.predict(img))\n",
    "print(predict_prob)\n",
    "\n",
    "\n",
    "if predict_prob.all()> 0.5:\n",
    "    print(\"The X-Ray belongs to PNEUMONIA person\")\n",
    "else:\n",
    "    print(\"The X-RAY belongs to NORMAL person\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2023-04-05T10:21:24.274451Z",
     "iopub.status.idle": "2023-04-05T10:21:24.275264Z",
     "shell.execute_reply": "2023-04-05T10:21:24.275001Z",
     "shell.execute_reply.started": "2023-04-05T10:21:24.274973Z"
    }
   },
   "outputs": [],
   "source": [
    "# Test Case 2: PNEUMONIA\n",
    "\n",
    "dic = test_dataset.class_indices\n",
    "idc = {k:v for v, k in dic.items()}\n",
    "\n",
    "img = load_img('data_small/chest-xray-pneumonia/chest_xray/test/PNEUMONIA/person91_bacteria_449.jpeg', target_size=(224,224))\n",
    "img = img_to_array(img)\n",
    "img = img/255\n",
    "imshow(img)\n",
    "plt.axis('off')\n",
    "img = np.expand_dims(img,axis=0)\n",
    "predict_prob=model.predict(img)\n",
    "print(predict_prob)\n",
    "\n",
    "\n",
    "if predict_prob> 0.5:\n",
    "    print(\"The X-Ray belongs to PNEUMONIA person\")\n",
    "else:\n",
    "    print(\"The X-RAY belongs to NORMAL person\")"
   ]
  }
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