inference-rest-client-test.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint\n",
    "try:\n",
    "    import matplotlib.pyplot as plt\n",
    "except ImportError:\n",
    "    print('Matplotlib not detected - images plotting not available')\n",
    "\n",
    "plotting = True\n",
    "try:\n",
    "    from matplotlib import image as mpimage\n",
    "except ImportError:\n",
    "    from PIL import Image as pilimage\n",
    "    plotting = False\n",
    "\n",
    "import requests\n",
    "import tensorflow as tf\n",
    "\n",
    "def get_image_from_drive(path):\n",
    "    # Load the image\n",
    "    try:\n",
    "        image = pilimage.open(path)\n",
    "    except ImportError:\n",
    "        image = mpimage.open(path)\n",
    "    except Exception:\n",
    "        raise\n",
    "    return image\n",
    "\n",
    "def get_random_image_from_dataset(image_index=randint(0, 9999)):\n",
    "    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "    return x_test[image_index]\n",
    "\n",
    "def show_selected_image(image):\n",
    "    fig = plt.figure()\n",
    "    plt.subplot(1, 1, 1)\n",
    "    plt.tight_layout()\n",
    "    plt.imshow(image, cmap='gray', interpolation='none')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()\n",
    "    \n",
    "def get_random_image_from_dataset(image_index=randint(0, 9999)):\n",
    "    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "    return x_test[image_index]\n",
    "\n",
    "\n",
    "def show_selected_image(image):\n",
    "    fig = plt.figure()\n",
    "    plt.subplot(1, 1, 1)\n",
    "    plt.tight_layout()\n",
    "    plt.imshow(image, cmap='gray', interpolation='none')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def make_vector(image):\n",
    "    vector = []\n",
    "    for item in image.tolist():\n",
    "        vector.extend(item)\n",
    "    return vector\n",
    "\n",
    "\n",
    "def make_prediction_request(image, prediction_url):\n",
    "    vector = make_vector(image)\n",
    "    json = {\n",
    "        \"inputs\": [vector]\n",
    "    }\n",
    "    response = requests.post(prediction_url, json=json)\n",
    "\n",
    "    print(response.status_code)\n",
    "    print(response.text)\n",
    "\n",
    "image = get_random_image_from_dataset()\n",
    "\n",
    "show_selected_image(image)\n",
    "make_prediction_request(image, 'http://127.0.0.1:8501/v1/models/mnist:predict')"
   ]
  }
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