CNN And Transfer LEarning.ipynb

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   "source": [
    "## Creating CNN Using Scratch And Transfer Learning"
   ]
  },
  {
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   "source": []
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    "id": "yDi-eJDc8tIV"
   },
   "source": [
    "Please download the dataset from the below url"
   ]
  },
  {
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   "execution_count": 1,
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   "source": [
    "# import the libraries as shown below\n",
    "\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Conv2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.vgg19 import VGG19\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": 2,
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    "id": "2uVClskg8tIZ"
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   "outputs": [],
   "source": [
    "# re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]\n",
    "\n",
    "train_path = 'C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train'\n",
    "valid_path = 'C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Test'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "execution_count": 3,
   "metadata": {
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     "timestamp": 1630766701872,
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   "source": [
    "# Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG\n",
    "# Here we will be using imagenet weights\n",
    "\n",
    "mobilnet = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
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   "source": [
    "# don't train existing weights\n",
    "for layer in mobilnet.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "  # useful for getting number of output classes\n",
    "folders = glob('C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train\\\\*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "['C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train\\\\Parasite',\n",
       " 'C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train\\\\Uninfected']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1630766702629,
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   "outputs": [],
   "source": [
    "# our layers - you can add more if you want\n",
    "x = Flatten()(mobilnet.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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     "status": "ok",
     "timestamp": 1630766702633,
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   "source": [
    "prediction = Dense(len(folders), activation='softmax')(x)\n",
    "\n",
    "# create a model object\n",
    "model = Model(inputs=mobilnet.input, outputs=prediction)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 50178     \n",
      "=================================================================\n",
      "Total params: 20,074,562\n",
      "Trainable params: 50,178\n",
      "Non-trainable params: 20,024,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# view the structure of the model\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 262,
     "status": "ok",
     "timestamp": 1630766702637,
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     },
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    },
    "id": "qXpkSmiJ8tIh"
   },
   "outputs": [],
   "source": [
    "#from tensorflow.keras.layers import MaxPooling2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 262,
     "status": "ok",
     "timestamp": 1630766702638,
     "user": {
      "displayName": "4227_Anushka Sawant",
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    "outputId": "b300e3c6-c402-4596-d160-6d92f022a32d"
   },
   "source": [
    "### Create Model from scratch using CNN\n",
    "model=Sequential()\n",
    "model.add(Conv2D(filters=16,kernel_size=2,padding=\"same\",activation=\"relu\",input_shape=(224,224,3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation =\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500,activation=\"relu\"))\n",
    "model.add(Dense(2,activation=\"softmax\"))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 259,
     "status": "ok",
     "timestamp": 1630766702639,
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   "outputs": [],
   "source": [
    "# tell the model what cost and optimization method to use\n",
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 259,
     "status": "ok",
     "timestamp": 1630766702640,
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      "displayName": "4227_Anushka Sawant",
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      "userId": "01062978812365864678"
     },
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    "id": "4MLZI3vL8tIj"
   },
   "outputs": [],
   "source": [
    "# Use the Image Data Generator to import the images from the dataset\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3336,
     "status": "ok",
     "timestamp": 1630766705718,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
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    "id": "vdB-NL-O8tIj",
    "outputId": "88e30ef3-470a-4b7b-c65e-eebfd2223eb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 416 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# Make sure you provide the same target size as initialied for the image size\n",
    "training_set = train_datagen.flow_from_directory('C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.preprocessing.image.DirectoryIterator at 0x1c13e224790>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1630766705720,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 134 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = test_datagen.flow_from_directory('C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Test',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
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   "execution_count": 16,
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    "scrolled": false
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anushka\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "13/13 [==============================] - 276s 21s/step - loss: 1.8441 - accuracy: 0.5096 - val_loss: 1.6842 - val_accuracy: 0.3582\n",
      "Epoch 2/4\n",
      "13/13 [==============================] - 272s 21s/step - loss: 0.9254 - accuracy: 0.5962 - val_loss: 0.7175 - val_accuracy: 0.6194\n",
      "Epoch 3/4\n",
      "13/13 [==============================] - 270s 21s/step - loss: 0.5889 - accuracy: 0.7332 - val_loss: 0.4971 - val_accuracy: 0.7164\n",
      "Epoch 4/4\n",
      "13/13 [==============================] - 269s 21s/step - loss: 0.4131 - accuracy: 0.8125 - val_loss: 0.4437 - val_accuracy: 0.7985\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "# Run the cell. It will take some time to execute\n",
    "r = model.fit_generator(\n",
    "  training_set,\n",
    "  validation_data=test_set,\n",
    "  epochs=4,\n",
    "  steps_per_epoch=len(training_set),\n",
    "  validation_steps=len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 532
    },
    "executionInfo": {
     "elapsed": 199,
     "status": "ok",
     "timestamp": 1630767868376,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "NjYT97VE8tIm",
    "outputId": "e438cde5-45de-41e6-f587-7aa3be870626"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJBVCNLjGXdABvP2M8fTi87D8YSgrrXOoLuGB/H1YZ74/JE1SIUTDk4IO0OJ6uPt1OLwR1rxoU6gRlZqkn/10zJz8hBDCClLQL+r6a+g+Djb8Ew6stinUs4M60DM6mCkf75QmqRCiwUhBr2zgy9Cqs3ErY+6ROofxdHdj1ujuBEuTVAjRgKSgV+bpA/e+B7oclj0EpXUvxM0ta5JKk1QI0VCkoF8upB0MeQuOJcM3z9sUqkt4IC8OjeX7Q1nMkCapEKKeSUGvSsch0Os38OPbsOdzm0LdG9+WcTdEMn9DGp9vlyapEKL+SEGvzh1/hbB4+PxxyDpkU6g/391RmqRCiHpnzZqiC5RSp5VSNa4TqpRKUEqVKaVGmJeeHXl4wb3vgps7LBsHJYV1DnVxTdIgX2mSCiHqjzVX6O8CA2raQCnlDvwDsO1+P0cT2BaGzYGTu+DrKTaFCvX3ZvYYaZIKIepPrQVda50EZNey2RPAx8BpM5JyKNf1N9Yk3fYe7FhiU6iubS81Sf+xSpqkQghz2TyGrpQKA4YBs21Px0Hd8ixE3gRf/h5O77Up1MUm6bz10iQVQpjLjKbo68AUrXVZbRsqpSYopZKVUsmZmZkm7LqBuHvAiHfAq6mxyHRRvk3h/nx3R3pGGU3S3celSSqEMIcZBT0eWKKUSgdGALOUUkOr2lBrPVdrHa+1jg8NDTVh1w3IvxUMfweyfjau1G1YZPridLuBTbyYsEiapEIIc9hc0LXW0VrrKK11FLAceExr/ZmtcR1STF/o9wzsWgpb37UpVKj/pel2n1gsTVIhhO2suW1xMbAJaK+UylBKjVdKTVJKTar/9BzQzX+AdrcZd70c325TqLi2gfxtaCwbD0qTVAhhO4/aNtBa32dtMK31gzZl4wzc3OBX82D2Tcb96ROTjDVK62hkfFtSjuUxb30asWEBDIkLMzFZIURjIk+K1kXTEOOho7wM+Owxm8bTAf48qCMJUUHSJBVC2EQKel1F9ILbX4B9X8IPb9sUysvDjVmjexDYxHiSNEeapEKIOpCCbosbfgvX3w3fPAdHN9sU6mKT9PTZIh6XJqkQog6koNtCKRgyE5qFwbIH4XyWTeEqN0lfXr3fnByFEI2GFHRbNQmEke/B+Uz4dCKU23ZlPTKhLWMTI5mblCpPkgohrooUdDO06QYDZsDBb4w1SW303N2XmqR7jp81IUEhRGMgBd0s8Q9D7AhY8yKkJdkUysuj0pOk7ydLk1QIYRUp6GZRCu75N4RcA8vHw7lTNoVr4e/D22O6c/psEU8s/kmapEKIWklBN5O3n7HIdNE5+Hg8lJXaFK5bRBB/GxrLhoNneEWapEKIWkhBN1vLjnD3PyF9Pax9yeZwIxPaMiYxgjlJqazYcdyEBIUQrkoKen2Iux+6jYX1r8LP39gc7vm7O5EQFcT/Ld8hTVIhRLWkoNeXu16BlrHwyQRjigAbXGySBjTxZOIH0iQVQlRNCnp98WwCIxdBWYnx0FGpbUW4hb8Ps8f04FReEU8ukSapEOJKUtDrU0g7GPImZGyBb6fZHK5bRBDTh3Zi/c/SJBVCXEkKen3rNAx6ToQfZsLeL2wO9+uEiIom6RfSJBVCVCIFvSHc+TcI62FMtZudanO45+/uRHxkEP+3XJ4kFUJcIgW9IXh4GfOnKzdjkemSCzaF8/JwY9aY7jRr4iFNUiFEBSnoDSUwAobNgZM7YdVUm8MZT5JeapKWldu2yIYQwvlJQW9I7QdA76dg60LYudTmcN0jgvjrEKNJ+vJqWZNUiMbOmkWiFyilTiulUqp5f7RSaqfl63ulVFfz03Qhtz4HETfCF09Bpu13qozqGcHoXhHMWSdNUiEaO2uu0N8FBtTwfhrQV2vdBZgOzDUhL9fl7gEjFoCXLyx9AIrP2xzyL/d0ooelSbr3hDRJhWisai3oWuskILuG97/XWudYXv4AhJuUm+tq1hqGzzeu0L982uZFpr083Hh7tNEknfB+MrkF0iQVojEyewx9PPB1dW8qpSYopZKVUsmZmZkm79rJxPSDfn+CnUtg2yKbw7VoZjRJT+Zd4InF0iQVojEyraArpW7BKOhTqttGaz1Xax2vtY4PDQ01a9fOq88fIeYWWDkZTuy0OZzRJI2VJ0mFaKRMKehKqS7AfGCI1tq2lZIbEzd3Y+jFNwSWjYMLeTaHvK9nBPf3imD2ukN8uVOapEI0JjYXdKVUBPAJMFZrfcD2lBqZps2NJmnOYfj8cZvH0wGmWZqkk5dJk1SIxsSa2xYXA5uA9kqpDKXUeKXUJKXUJMsmzwMhwCyl1HalVHI95uuaIm+A26fB3hXw4xybw11skvr7eDDx/a3SJBWikVDahCvCuoiPj9fJyVL7K2gNS+43FsR4eBWEx9sccuvhHEbN3URiTAjvPtQTdzdlQqJCCHtSSm3VWldZIORJUUehFAydZdzSuOxBKKj2TlGr9YiUJqkQjYkUdEfSJMhYZDr/FHw6EcptX8Tivp4R3NdTmqRCNAZS0B1NWHfo/3f4+b+w8XVTQk4b3JHuEYFMXraTfSelSSqEq5KC7ogSHoHY4fC/6ZC+weZw3h7uzB7TA38fDyYskiapEK5KCrojUgru+TcEx8DyhyH/tM0hjSdJu3Mir5Anl2yXJ0mFcEFS0B2Vt7+xyPSFs/DxeCgvszlkj8hgXhgcS9KBTF79rzRJhXA1UtAdWctOMOg1SEuCtTNMCXl/L6NJ+vbaQ3y184QpMYUQjkEKuqPrNhrixkDSK3DwW1NCVjRJl+9g/8lzpsQUQtifFHRncNcr0KIjfDIB8o7ZHM7bw523x/Sgqbcx3W5eQYkJSQoh7E0KujPw8oWR70FpESx/CMpsL8Atm/kwe0x3jucWypqkQrgIKejOovm1MPgNOPojfDvNlJA9IoOZNrgT6w5k8po0SYVwelLQnUnscEh4FDa9Bfu+MiXk6F6R3NezLbPWHmLlLmmSCuHMpKA7m/4vQptu8OlvIDvNlJDTBneiW0Qgf1wmTVIhnJkUdGfj4Q33vgsKY1GMkgs2h7z4JKk0SYVwblLQnVFQFAydDSd2wOpnTAnZspkPb4+WJqkQzkwKurO6/i648UlIfgd2LTclZHxUMH+5x2iS/vMbaZIK4WykoDuz256HiBtgxZOQac7qf6N7RTAqoS0z1xzia2mSCuFUpKA7M3dPYz1Szyaw9AEoLrA5pFKKF4Z0Iq5tIH+QJqkQTkUKurNr1gaGz4PMffDVH0xZZFqapEI4J2sWiV6glDqtlEqp5n2llHpDKXVQKbVTKdXd/DRFjdrdCn2nwI4P4acPTAnZKuBSk/R3H0mTVAhnYM0V+rvAgBreHwhca/maALxte1riqvX9P4jpByv/CCd3mRLyYpN07X5pkgrhDGot6FrrJKCmFYuHAIu04QcgUCnV2qwEhZXc3OFX8411SZeOM+ZRN8HoXhH8Ol6apEI4AzPG0MOAo5VeZ1h+dgWl1ASlVLJSKjkzM9OEXYtf8As1mqQ56bDiCVPG05VS/HXopSbpgVPSJBXCUZlR0FUVP6uykmit52qt47XW8aGhoSbsWlwh8kbjdsY9n8HmeaaEvNgk9fXyYMIiaZIK4ajMKOgZQNtKr8OB4ybEFXV145Nw3UDjKdKMraaEbBVgrEmakSNNUiEclRkFfQXwgOVul0QgT2stg6325OYGQ2eBf2tY9iAU1NQCsV5CVDB/GWw0Sf/1jTkPMgkhzGPNbYuLgU1Ae6VUhlJqvFJqklJqkmWTlUAqcBCYBzxWb9kK6/kGG5N4nTsBn/0GystNCTumVwQj48N5a81BVqXI57YQjsSjtg201vfV8r4GfmtaRsI84T2g/9/h68nw/Rtw01M2h1RK8dchsew/lc/TS3cQE+rHdS39bc9VCGEzeVLU1fV8FDoOhe/+Coe/NyWkj6c7cyxN0onvbyWvUJqkQjgCKeiuTikY/KYx5e7yhyHfnNtFWwX4MGt0d45mF/CUTLcrhEOQgt4Y+DSDkYugMAc+eQTKy0wJ2zM6mL/c05E1+zN5/Vtpkgphb1LQG4tWsXDXq5C6Fta9bFrYMYmRjIwP583/HeSdDWlcKDHnw0IIcfWkoDcm3cZA1/th3T/g0P9MCXmxSXpjuxCmf7mHm/6xhplrDsrDR0LYgdImPB5eF/Hx8To5Odku+27Uigtg/m2QfxomrTem3zWB1pqNB7OYk3SI9T+fwdfLnVEJEYy/OZqwwCam7EMIAUqprVrr+Crfk4LeCGUegHm3QKvOMO4LY6EME+05fpZ561NZscN4YPieLq2Z0KcdHds0M3U/QjRGUtDFlXYth4/HG9ME3Dm9XnZxLLeQBRvSWLL5COeLy7j52uZM7NOO3teEoFRVUwAJIWojBV1U7cunjUWmRy02Fp2uJ3kFJfxn82EWbkwn81wRHVs3Y2LfGAZ1bo2Hu7RxhLgaUtBF1UqL4J07IScNJiYZ96rXo6LSMj776Rhzk1I5lHmesMAmjL8pml8ntKWpd60PLQshkIIuapKdBnP6QkgMPLwaPLzrfZfl5Zrv9p1mbtIhtqTnENDEk7GJkYy7MYpQ//rfvxDOTAq6qNm+r2DJ/ZDwKAx6tUF3ve1IDnPXpbJ6z0k83d0Y3j2MR26OoV2oX4PmIYSzkIIuarf6Wdj0lrHiUezwBt99amY+8zeksXxrBiVl5dzRoSUT+8bQIzK4wXMRwpFJQRe1KyuBdwfBqd0wYS00v9YuaWSeK2LRpnQWbTpMXmEJPSKDmNgnhts7tMTNTe6MEUIKurBO3jGYczP4tYJHvgUvX7ulUlBcytItR5m/IY2MnEJiQpvy6M0xDOsWho+nu93yEsLepKAL6x38Dj4YDnGjYehMe2dDaVk5K1NOMjfpECnHztLcz5uHekcxplckAb7mPhAlhDOQgi6uzv9ehKSXYchMY/4XB6C1ZtOhLGYnpZJ0IBNfL3d+ndCW8TdFEx5kv78khGhoUtDF1Skvg/eHwtEt8Oh30LKTvTP6hb0nzjIvyZhaQAN3d2nNhD4xdGoTYO/UhKh3Nhd0pdQA4N+AOzBfaz3jsvcDgA+ACIxl7V7VWi+sKaYUdAeXfxpm3wzefkaT1Nvxlpk7bplaYHGlqQUm9Inhpmuay9QCwmXZVNCVUu7AAeAOIAPYAtyntd5TaZtngACt9RSlVCiwH2iltS6uLq4UdCeQvhHeuxs6DYPh7xirHzmgvMISPvzxCAs2plVMLTChTwyDurTGU6YWEC6mpoJuzX/tPYGDWutUS4FeAgy5bBsN+CvjssgPyAZKbchZOIKo3nDrc5DyMWyZb+9sqhXQxJPf9GvHhim38PLwLhSXlfPUR9vp98pa3tmQxvki+U9RNA7WFPQw4Gil1xmWn1X2FtABOA7sAn6ntS6/PJBSaoJSKlkplZyZac7alqKe9X4Kru0Pq5+BY9vsnU2NvD3cGZnQlv8+1Yf5D8QTFtiE6V/u4YaXvuOV1fs4fe6CvVMUol5ZU9Cr+jv78nGa/sB2oA0QB7yllLpi8mut9VytdbzWOj40NPQqUxV24eYGw2aDX0tYNs5Yl9TBubkpbu/YkqWTbuCTx27kxnbNmbX2EDfNWMPUj3dyKDPf3ikKUS+sKegZQNtKr8MxrsQrewj4RBsOAmnA9eakKOzONxjufRfOnoDPHgM73RlVF90jgpg9tgf/+0M/7o0P59OfjnHba+t4dFEyyenZ9k5PCFNZU9C3ANcqpaKVUl7AKGDFZdscAW4DUEq1BNoDqWYmKuwsPB7u/BvsXwnfv2nvbK5adPOmvDisMxun3sqTt17DlvRsRszexK9mbWRVyknKy53nQ0qI6lh72+JdwOsYty0u0Fq/qJSaBKC1nq2UagO8C7TGGKKZobX+oKaYcpeLE9LaGHbZ+yU8tBIiEu2dUZ0VFJeyLDmDeetTjakFmjflkZtj+FV3mVpAODZ5sEiY50IezO0HJReMRaabNrd3RjYpLSvn65STzE1KZdexPJr7efPgjZGMSYwk0NfL3ukJcQUp6MJcJ3fBvNuM2xpHLwc357+i1VqzKTWLOetSWWeZWmBkvDG1QNtgmVpAOA4p6MJ8W9+DL56Efs9Avyn2zsZU+06eZW5SKiu2G1MLDOpsTC0QGyZTCwj7k4IuzKc1fPYb2LEEHvgMYvrZOyPTHc8tZOHGNBZvPkp+USk3XWNMLXDztTK1gLAfKeiifhSfh3m3QkEWTFwPzVrbO6N6kVdYwuLNR1iwIY3T54ro0LoZE/pEc3eXNjK1gGhwUtBF/cncD3NvgdZdYdwX4O5h74zqTVFpGZ9vP868pFR+Pp1PmwAfHr4pmlE9I/Dzdt3jFo5FCrqoXzuXwSePQMQNxkRe1w2AoEh7Z1Vvyss1aw+cZva6VDanZdPMx4MxiZE82DuKFv4+9k5PuDgp6KL+bZoFyQsg62fjdWgHaD8ArhtoPJTkAnfCVOWnIznMTUpl1e6TeLq5MaxbGI/2ieGaFn72Tk24KCnoouFkHYIDq2D/13D4e9Bl4BtiTPB1XX9odyv4XDHNj9NLP3Oe+RtSWZacQVFpObd3aMnEvjHERwZJA1WYSgq6sI/CXDj4rVHgf/4GLuSCmydE3QTtB7rk0ExWfhHvbTrM+5vSySkooVtEIBP7tOOOji1xd5PCLmwnBV3YX1kpHP0RDnwN+1e5/NBMYXEZy7YeZd76VI5mFxLdvCmP3BzN8O7hMrWAsIkUdOF4qh2audO4cneRoZnSsnJW7TamFtiZkUdzPy/G3RDF2BtkagFRN1LQhWOrGJpZDT//t4qhmf4QFGXnJG2jteaH1GzmJB1i7f5Mmni68+sEmVpAXD0p6MJ5NIKhmf0nzzE3KZXPtx9DA3d1bs1EmVpAWEkKunBelYdmjmyC8lKXGZo5kVfIwo3pfPjjEfKLSul9TQgT+rSjj0wtIGogBV24Bhcdmjl7oYQPfzzCwo1pnDpbxPWt/JnYN0amFhBVkoIuXI8LDs0Ul5bz+fZjzFufyoFTMrWAqJoUdOH6XGho5uLUAnPWpfJjWjb+Ph6M7hXJw72jaNFMphZo7KSgi8alpqGZ6wYYV/BOMjSz/Wguc5MOsSrlJB5ubgzt1oYJfWK4poW/vVMTdiIFXTRelYdmDqyGMweMnzvZ0MzhrPPMX5/Gsq1HuVBSzu0dWjChTzsSomRqgcbG5oKulBoA/Btjkej5WusZVWzTD2MhaU/gjNa6b00xpaALu3DyoZms/CIWbTrMIsvUAnFtA5nUN4Y7OraSqQUaCZsKulLKHTgA3AFkAFuA+7TWeyptEwh8DwzQWh9RSrXQWp+uKa4UdGF3hblw6DujqepkQzOFxWUs33qUeevTOJJdQFSIL4/cHMOIHjK1gKuztaDfAEzTWve3vP4TgNb6pUrbPAa00Vr/2dqkpKALh+KkQzNl5ZpVKSeZm3SIHRl5hDT1YtyNUYxNjCSoqUwt4IpsLegjMK68H7G8Hgv00lo/Xmmb1zGGWjoB/sC/tdaLqog1AZgAEBER0ePw4cN1OiAh6p2TDc1cnFpgbtIh1limFhgZH87DN0UTGdLU3ukJE9la0O8F+l9W0HtqrZ+otM1bQDxwG9AE2AQM0lofqC6uXKELp+FkQzMHTl2aWqCkTBMZ4ktCVDA9o4LpGR1MZIivNFKdWEMMuUwFfLTW0yyv3wFWaa2XVRdXCrpwSrUOzQyA8ASHGJo5mXeBL3ceZ3NaNlvSs8kpKAEg1N+7orgnRAXTvpW/NFSdiK0F3QOjKXobcAyjKXq/1np3pW06AG8B/QEvYDMwSmudUl1cKejCJTjJ0Ex5ueZQZj6b07ONAp+WzfG8CwD4+3gQHxlEz+gQekYH0TksEC8PmXLAUZlx2+JdGLckugMLtNYvKqUmAWitZ1u2mQw8BJRj3Nr4ek0xpaALl1Pt0Exvo6nqYEMzGTkFbLEU+M1p2RzKPA+At4cb3SIC6RkVTEJ0MN0jgmgqUw84DHmwSIiGVjE0s8r4cuChmYvO5BeRnJ7N5rQctqRns/t4HuUa3N0UsW2akWAp8AlRwQTLHTR2IwVdCHtzkqGZys5dKGHbkVy2pGWzOT2b7UdzKS4tB+DaFn4kRF9qtLYJbGLnbBsPKehCOBInG5q5qKi0jJ0ZeRVN1q3pOZwrKgUgLLBJRZO1Z3Qw7UKbyp009UQKuhCOygmHZi4qK9fsPXGWLenZFWPxZ/KLAQhp6kV8lKXRGhVMh9b+eMjc7qaQgi6Es3DCoZmLtNaknTlvKe45bE7P4mh2IQBNvdzpHhlEL8tVfNe2gTJFQR1JQRfCGTnp0ExlJ/MuWG6VzGJLWg77T50DwMvdjS7hAcYwTXQwPSKDaObjaedsnYMUdCGcXVkpZGw2rtwvH5q5rr+xBJ+DDs1UlltQTHJ6TsX98CnH8igt17gp6NC6WcUYfEJUMKH+3vZO1yFJQRfC1VwcmjmwCg5/71RDM5UVFJey/UguP1oarduO5HChxLiTJqZ504pbJXtGBdM2uIk0WpGCLoRrq2loJuYW8GsJvsHQJBiaBBnf+wQ45NV8cWk5Kcfz2GIp8FvSc8grNKYsaNXMx1LcjWbrtS38cGuEUxZIQReisahuaOYKyijqlxf6JkHG64rvL/u5tz804FVyebnmwOlzlnvhc9iSls3Js8aUBQFNPEmICqoYpokNC8CzEdxJIwVdiMaqMBcKsoz/LcyGgmwozLny+8KcS6+LzlYfz83jUnGvKPbB0CSwig+HSt97mvPgkdaao9mFbE7PrnjgKe2MMWVBE093ukUEkhAVTK/oYLpFBNHEy/H+CrGVFHQhhPXKSi59AFQU+uq+z7304VBaWH1MD5+ar/wrf1/5Pffa73w5fe6C0Wi1zEmz9+RZtAYPN0VsWEDFrZLxUUEE+jr/lAVS0IUQ9a+k8JdX+lX+FXD5XwTZRkO3Ol7+4FtFob/8Q6DSB8VZ1ZStR4xx+M1p2ezMyKO4zGi0tm/pX3GrZM+oYFoF+DTQP455pKALIRyT1lCcX8WVf04tHw65QHW1SxlDQJYPgTKfILK1H8cu+HAw35O9uR6cLvUlB398mjUnKjycDu0i6dYunOhQP4e/k6amgi5zYgoh7Ecpo9Hq7Q9Bkdb/XnkZXMi7suhXMSzkXnCa0ML9hBbkEFd8DtwwVm0AuAAcNL6KtTtZyp9irwDcfIPxDQjFL6gFbr41DQsFg6fjXOVLQRdCOB83d6Og+gZDSDvrf6+02Lits9KHgC7IIivzNKdOHeds9ikunM3CJyuPwOy9BKcnE+SWj5curj6mR5NamsPVfCC4m19+paALIRoPDy/wa2F8WSigueXromO5hRV30WxJy+bo6SwCyaeFRwE9WkC35uV0CCglwrcI7+LcXw4Rnd576XV1/YEbHof+L5p/eKZHFEIIJxcW2ISwbmEM7RYGQPb5YuNBJ8sDT4tSzlJWrnF3U3S8OGVB52ASooII8bNMWaA1FJ27bPzfUvRbxdZL3tIUFUKIq3S+qJRtR4wHnX5MMxb/KLIs/tEutGnF+qwJUcGEB/maum+5y0UIIepRUWkZKcfyjGmD07JIPpzDuQvGcEubAJ9f3Cp5TQvb7qQxY5HoAcC/MRaJnq+1nlHNdgnAD8CvtdbLa4opBV0I4arKyjX7T567tAh3ejaZ54oACPL15LF+1/Bon5g6xbbptkWllDswE7gDyAC2KKVWaK33VLHdP4DVdcpSCCFchLubomObZnRs04xxN0ahteZwVkHFtMEt6+mBJmuaoj2Bg1rrVACl1BJgCLDnsu2eAD4GEkzNUAghnJxSiqjmTYlq3pSR8W3rbT/WTE0WBhyt9DrD8rMKSqkwYBgwu6ZASqkJSqlkpVRyZmbm1eYqhBCiBtYU9KpG7y8feH8dmKK1LqspkNZ6rtY6XmsdHxoaamWKQgghrGHNkEsGUPlvhHDg+GXbxANLLJ3b5sBdSqlSrfVnZiQphBCidtYU9C3AtUqpaOAYMAq4v/IGWuvoi98rpd4FvpRiLoQQDavWgq61LlVKPY5x94o7sEBrvVspNcnyfo3j5kIIIRqGVY/+a61XAisv+1mVhVxr/aDtaQkhhLharr8AnxBCNBJS0IUQwkXYbS4XpVQmcLiOv94cOGNiOvYkx+KYXOVYXOU4QI7lokitdZX3fdutoNtCKZVc3VwGzkaOxTG5yrG4ynGAHIs1ZMhFCCFchBR0IYRwEc5a0OfaOwETybE4Jlc5Flc5DpBjqZVTjqELIYS4krNeoQshhLiMFHQhhHARDl3QlVIDlFL7lVIHlVJTq3hfKaXesLy/UynV3R55WsOKY+mnlMpTSm23fD1vjzxro5RaoJQ6rZRKqeZ9ZzontR2Ls5yTtkqpNUqpvUqp3Uqp31WxjVOcFyuPxVnOi49SarNSaoflWF6oYhtzz4vW2iG/MCYCOwTEAF7ADqDjZdvcBXyNMWd7IvCjvfO24Vj6YcxSafd8azmWPkB3IKWa953inFh5LM5yTloD3S3f+wMHnPj/K9Yci7OcFwX4Wb73BH4EEuvzvDjyFXrF0nda62Lg4tJ3lQ0BFmnDD0CgUqp1QydqBWuOxSlorZOA7Bo2cZZzYs2xOAWt9Qmt9TbL9+eAvVy2qhhOcl6sPBanYPm3zre89LR8XX4XiqnnxZELeq1L31m5jSOwNs8bLH+efa2U6tQwqZnOWc6JtZzqnCilooBuGFeDlTndeanhWMBJzotSyl0ptR04DXyjta7X82LV9Ll2Ys3Sd9Zs4wisyXMbxhwN+Uqpu4DPgGvrO7F64CznxBpOdU6UUn4YC7U/pbU+e/nbVfyKw56XWo7Fac6LNpbljFNKBQKfKqVitdaVezamnhdHvkK3Zuk7a7ZxBLXmqbU+e/HPM23MP++plGrecCmaxlnOSa2c6ZwopTwxCuB/tNafVLGJ05yX2o7Fmc7LRVrrXGAtMOCyt0w9L45c0CuWvlNKeWEsfbfism1WAA9YOsWJQJ7W+kRDJ2qFWo9FKdVKKWNRVqVUT4xzk9XgmdrOWc5JrZzlnFhyfAfYq7X+ZzWbOcV5seZYnOi8hFquzFFKNQFuB/Zdtpmp58Vhh1y0dUvfrcToEh8ECoCH7JVvTaw8lhHAb5RSpUAhMEpb2uCORCm1GOMug+ZKqQzgLxjNHqc6J2DVsTjFOQF6A2OBXZbxWoBngAhwuvNizbE4y3lpDbynlHLH+NBZqrX+sj5rmDz6L4QQLsKRh1yEEEJcBSnoQgjhIqSgCyGEi5CCLoQQLkIKuhBCuAgp6EII4SKkoAshhIv4f/KlmvFfnJwuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "# plot the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 471,
     "status": "ok",
     "timestamp": 1630767868838,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "0aDMoHPF8tIm"
   },
   "outputs": [],
   "source": [
    "# save it as a h5 file\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model.save('model_vgg19.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 1838,
     "status": "ok",
     "timestamp": 1630767870665,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "G_ngx3hS8tIn"
   },
   "outputs": [],
   "source": [
    "\n",
    "y_pred = model.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1630767870666,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "LbUz6Y6P8tIn",
    "outputId": "6a81a385-f1f4-4bf9-87bf-282c6bd3b707"
   },
   "outputs": [
    {
     "data": {
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       "       [0.46061814, 0.53938186],\n",
       "       [0.3864508 , 0.61354923],\n",
       "       [0.5529875 , 0.44701245],\n",
       "       [0.7716639 , 0.2283361 ],\n",
       "       [0.8000088 , 0.19999123],\n",
       "       [0.46910655, 0.5308935 ],\n",
       "       [0.3837719 , 0.61622804],\n",
       "       [0.22048774, 0.7795122 ],\n",
       "       [0.6130261 , 0.3869739 ],\n",
       "       [0.8449432 , 0.15505683],\n",
       "       [0.9136553 , 0.08634469],\n",
       "       [0.9197093 , 0.08029065],\n",
       "       [0.26707688, 0.73292315],\n",
       "       [0.59846395, 0.4015361 ],\n",
       "       [0.88015836, 0.11984159],\n",
       "       [0.15092792, 0.8490721 ],\n",
       "       [0.7126245 , 0.28737548],\n",
       "       [0.6631966 , 0.33680335],\n",
       "       [0.9598804 , 0.04011963],\n",
       "       [0.69555235, 0.30444765],\n",
       "       [0.40581173, 0.59418833],\n",
       "       [0.35116127, 0.64883864],\n",
       "       [0.99691004, 0.00309001],\n",
       "       [0.67929053, 0.32070938],\n",
       "       [0.9661442 , 0.03385583],\n",
       "       [0.1383119 , 0.86168814],\n",
       "       [0.42595324, 0.57404673],\n",
       "       [0.87893486, 0.12106509],\n",
       "       [0.6577838 , 0.34221622],\n",
       "       [0.2743593 , 0.72564065],\n",
       "       [0.14771377, 0.8522863 ],\n",
       "       [0.8064927 , 0.1935073 ]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1630767870667,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "W4Bd6_Eo8tIo"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y_pred = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1630767870668,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "sDdsOkkO8tIo",
    "outputId": "b426794f-dbea-492a-cd07-c1c7919cad39"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'array' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-3661267cfb20>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m y_pred=array([0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n\u001b[0m\u001b[0;32m      2\u001b[0m        \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m        \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m        \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m        \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'array' is not defined"
     ]
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1630767870668,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "dxSFsPFG8tIp"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-adecdd2c1af0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "y_pred[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1630767870669,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "qprPMeJp8tIq"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 418,
     "status": "ok",
     "timestamp": 1630767871077,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "MgWfzi-h8tIq"
   },
   "outputs": [],
   "source": [
    "model=load_model('model_vgg19.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1630767871078,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "hye3UZr08tIq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1630767871079,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "Xbru3EuL8tIq"
   },
   "outputs": [],
   "source": [
    "img=image.load_img('C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\Dataset\\\\Dataset\\\\Test\\\\Uninfected\\\\C3thin_original_IMG_20150608_162922_cell_211.png',target_size=(224,224))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1630767871079,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "ptfdcr2r8tIr",
    "outputId": "fb2a79ad-474a-4898-919b-52f04a69732d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=image.img_to_array(img)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1630767871080,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "1R4XKZGk8tIr",
    "outputId": "ee098d08-1071-4b68-bf02-9d0538093372"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1630767871081,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "KnB_wDrX8tIr"
   },
   "outputs": [],
   "source": [
    "x=x/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1630767871081,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "MICUqc3b8tIs",
    "outputId": "f50fe517-2fbb-49fe-c1ff-31ed314b0cce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.expand_dims(x,axis=0)\n",
    "img_data=preprocess_input(x)\n",
    "img_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1630767871082,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "Z0-K7cNx8tIs",
    "outputId": "76e7b2d6-db8c-4b05-8792-500c95724156"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9966853, 0.0033147]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(img_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1630767871082,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "KMkV5y-d8tIs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "a=np.argmax(model.predict(img_data), axis=1)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1630767871083,
     "user": {
      "displayName": "4227_Anushka Sawant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgxjPPD3ZrdXhw5mOWqmI8da0j34Pa1VI458CA3sQ=s64",
      "userId": "01062978812365864678"
     },
     "user_tz": -330
    },
    "id": "SUYTNZu98tIt",
    "outputId": "39340f0d-261b-4e47-932b-52414b8d2696"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infected\n"
     ]
    }
   ],
   "source": [
    "if(a==1):\n",
    "    print(\"Uninfected\")\n",
    "else:\n",
    "    print(\"Infected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vIfLknuZ8tIt",
    "outputId": "9431ea69-9ac9-4ea4-cec1-5b16289a50ec",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: This is a development server. Do not use it in a production deployment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
      "127.0.0.1 - - [05/Sep/2021 15:25:32] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [05/Sep/2021 15:25:43] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [05/Sep/2021 15:26:06] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [05/Sep/2021 15:26:14] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [05/Sep/2021 15:26:21] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n"
     ]
    }
   ],
   "source": [
    "from flask import Flask, flash, request, redirect, url_for, render_template\n",
    "import urllib.request\n",
    "import os\n",
    "from werkzeug.utils import secure_filename\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import img_to_array, load_img\n",
    " \n",
    "app = Flask(__name__)\n",
    "\n",
    "UPLOAD_FOLDER = 'C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\static\\\\uploads'\n",
    "app.secret_key = \"secret key\"\n",
    "app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n",
    "app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024\n",
    " \n",
    "ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def model_predict(img_path, model):\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "\n",
    "    # Preprocessing the image\n",
    "    x = image.img_to_array(img)\n",
    "    # x = np.true_divide(x, 255)\n",
    "    ## Scaling\n",
    "    x=x/255\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "   \n",
    "\n",
    "    # Be careful how your trained model deals with the input\n",
    "    # otherwise, it won't make correct prediction!\n",
    "    x = preprocess_input(x)\n",
    "\n",
    "    preds = model.predict(x)\n",
    "    preds=np.argmax(preds, axis=1)\n",
    "    if preds==0:\n",
    "        preds=\"The Person is Infected With Pneumonia\"\n",
    "    else:\n",
    "        preds=\"The Person is not Infected With Pneumonia\"\n",
    "    \n",
    "    \n",
    "    return preds\n",
    "\n",
    "\n",
    " \n",
    "def allowed_file(filename):\n",
    "    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n",
    "     \n",
    " \n",
    "# Model saved with Keras model.save()\n",
    "MODEL_PATH ='model_vgg19.h5'\n",
    "\n",
    "# Load your trained model\n",
    "model = load_model(MODEL_PATH)\n",
    "\n",
    "\n",
    "@app.route('/')\n",
    "def home():\n",
    "    return render_template('index.html')\n",
    " \n",
    "@app.route('/predict', methods=['POST'])\n",
    "def upload():\n",
    "    if 'file' not in request.files:\n",
    "        flash('No file part')\n",
    "        return redirect(request.url)\n",
    "    file = request.files['file']\n",
    "    if file.filename == '':\n",
    "        flash('No image selected for uploading')\n",
    "        return redirect(request.url)\n",
    "    if file and allowed_file(file.filename):\n",
    "        filename = secure_filename(file.filename)\n",
    "        file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n",
    "        #print('upload_image filename: ' + filename)\n",
    "        fullpaths = ['C:\\\\Users\\\\Anushka\\\\Desktop\\\\DMWminiproject\\\\static\\\\uploads\\\\{}'.format(filename)]\n",
    "        #print(fullpaths)\n",
    "        \n",
    "        preds = model_predict(fullpaths[0], model)\n",
    "        result=preds\n",
    "        return result\n",
    "    \n",
    "            \n",
    "if __name__ == \"__main__\":\n",
    "    app.run()"
   ]
  },
  {
   "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": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "CNN And Transfer LEarning.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}