Collab 12 - Crear una API para Clasificar Imágenes con TensorFlow Serving.ipynb

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    "colab": {
      "name": "Collab 12 - Crear una API para Clasificar Imágenes con TensorFlow Serving.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWbUYy4Huos_"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/joanby/tensorflow2/blob/master/Collab%2012%20-%20Crear%20una%20API%20para%20Clasificar%20Imágenes%20con%20TensorFlow%20Serving.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCrzPgLp7W1h"
      },
      "source": [
        "## Paso 1: Instalar las dependencias y configurar el proyecto en GPU\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0KGbZuk7T7F",
        "outputId": "bc0f51b5-f111-4615-a1e4-872d7d25f8ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "!echo \"deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\" | tee /etc/apt/sources.list.d/tensorflow-serving.list && \\\n",
        "curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -\n",
        "!apt update"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  2943  100  2943    0     0   143k      0 --:--:-- --:--:-- --:--:--  143k\n",
            "OK\n",
            "Hit:1 http://storage.googleapis.com/tensorflow-serving-apt stable InRelease\n",
            "Hit:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:4 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Ign:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:11 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:12 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Hit:13 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Hit:14 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "23 packages can be upgraded. Run 'apt list --upgradable' to see them.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AoEpKnD94Lj",
        "outputId": "28c541bc-eccf-4967-9e0e-a0286f01fa3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "!apt-get install tensorflow-model-server"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  tensorflow-model-server\n",
            "0 upgraded, 1 newly installed, 0 to remove and 23 not upgraded.\n",
            "Need to get 210 MB of archives.\n",
            "After this operation, 0 B of additional disk space will be used.\n",
            "Get:1 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 tensorflow-model-server all 2.3.0 [210 MB]\n",
            "Fetched 210 MB in 4s (50.4 MB/s)\n",
            "Selecting previously unselected package tensorflow-model-server.\n",
            "(Reading database ... 144618 files and directories currently installed.)\n",
            "Preparing to unpack .../tensorflow-model-server_2.3.0_all.deb ...\n",
            "Unpacking tensorflow-model-server (2.3.0) ...\n",
            "Setting up tensorflow-model-server (2.3.0) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1cAehxr-M9p"
      },
      "source": [
        "#!pip install tensorflow-gpu==1.13.1"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfxOPCl7-Wp7",
        "outputId": "094bb7af-b068-453d-c96f-9054ecc9a048",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!pip install requests"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests) (2020.6.20)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsgbM_R2-ud_"
      },
      "source": [
        "## Paso 2: Importar las dependencias del proyecto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3taHerg-nkS",
        "outputId": "8ae7fbae-7685-4f86-df31-27c0c9d58406",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        }
      },
      "source": [
        "import os\n",
        "import json\n",
        "import random\n",
        "import requests\n",
        "import subprocess\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "\n",
        "%matplotlib inline\n",
        "tf.__version__"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.3.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90mWV1g6_TSt"
      },
      "source": [
        "## Paso 3: Pre procesado de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJsURBeA_XEe"
      },
      "source": [
        "### Cargar el dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVgkziGT_RQZ"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqOyARxY_kC2"
      },
      "source": [
        "class_names = ['avión', 'coche', 'pájaro', 'gato', 'ciervo', 'perro', 'rana', 'caballo', 'barco', 'camión']"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_adA5GI_ZGK"
      },
      "source": [
        "### Normalización de imágenes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VK4HUwLC_akD"
      },
      "source": [
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2A1OrRg_bD8",
        "outputId": "8d839995-242f-44cd-b205-66d2a61ebb30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HO6nL_9sBLPo"
      },
      "source": [
        "## Paso 4: Definir el modelo\n",
        "\n",
        "NOTA: Vamos a usar el mismo modelo que creamos en la Sección de Redes Neuronales Convolucionales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHyAvU8OA_aq"
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\", input_shape=[32, 32, 3]))\n",
        "model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))\n",
        "model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(units=10, activation='softmax'))"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-7vFhykBgMj"
      },
      "source": [
        "### Compilar el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nqrVFn3BkeS"
      },
      "source": [
        "model.compile(optimizer='Adam', \n",
        "              loss='sparse_categorical_crossentropy', \n",
        "              metrics=['sparse_categorical_accuracy'])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ1X_hgBBmx9"
      },
      "source": [
        "###  Entrenar el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7FSeHUkBpUq",
        "outputId": "8e605add-8ce4-4a70-f29d-79aef047574e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "model.fit(X_train, \n",
        "          y_train, \n",
        "          batch_size=128, \n",
        "          epochs=10)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 1.4902 - sparse_categorical_accuracy: 0.4621\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 2s 6ms/step - loss: 1.0557 - sparse_categorical_accuracy: 0.6277\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.8649 - sparse_categorical_accuracy: 0.6989\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.7352 - sparse_categorical_accuracy: 0.7451\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.6386 - sparse_categorical_accuracy: 0.7763\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.5608 - sparse_categorical_accuracy: 0.8033\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.4823 - sparse_categorical_accuracy: 0.8305\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.4110 - sparse_categorical_accuracy: 0.8541\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.3427 - sparse_categorical_accuracy: 0.8787\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 3s 6ms/step - loss: 0.2819 - sparse_categorical_accuracy: 0.9010\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7c001f0780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6k_gnv5BqdT"
      },
      "source": [
        "### Evaluar el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0nHalVeBtAy",
        "outputId": "02224732-aa0b-4da9-967a-f259ed4773b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.9225 - sparse_categorical_accuracy: 0.7407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9q7jsTuBuxT",
        "outputId": "cbd6dd7c-b894-49f2-82bc-07b300df2977",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"La precisión del modelo es de {} %\".format(test_accuracy*100.0))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "La precisión del modelo es de 74.07000064849854 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "945L3-2rDjFe"
      },
      "source": [
        "## Paso 5: Guardar el modelo para subir a producción"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5a5uCiniDmgY"
      },
      "source": [
        "### Crear un directorio para el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YonroqRrC3sM"
      },
      "source": [
        "MODEL_DIR = \"model/\"\n",
        "version = 1"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07NyMUGYEMNF",
        "outputId": "6f460b10-54cb-4217-c480-ec2b54233b1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        }
      },
      "source": [
        "export_path = os.path.join(MODEL_DIR, str(version))\n",
        "export_path"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'model/1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jln7YavbEczA"
      },
      "source": [
        "if os.path.isdir(export_path):\n",
        "    !rm -r {export_path}"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RA2HuIUDrDQ"
      },
      "source": [
        "### Guardar el modelo para TensorFlow Serving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19DG8IkIDv4S",
        "outputId": "3429080e-cf79-468a-bec8-51adc9120d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "#tf.saved_model.simple_save(tf.keras.backend.get_session(), export_dir=export_path, inputs={\"input_image\":model.input}, outputs={t.name:t for t in model.outputs})\n",
        "#tf.saved_model.save(model, export_path )\n",
        "print('export_path = {}\\n'.format(export_path))\n",
        "\n",
        "tf.keras.models.save_model(\n",
        "    model,\n",
        "    export_path,\n",
        "    overwrite=True,\n",
        "    include_optimizer=True,\n",
        "    save_format=None,\n",
        "    signatures=None,\n",
        "    options=None\n",
        ")\n",
        "\n",
        "print('\\nSaved model:')\n",
        "!ls -l {export_path}"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "export_path = model/1\n",
            "\n",
            "INFO:tensorflow:Assets written to: model/1/assets\n",
            "\n",
            "Saved model:\n",
            "total 180\n",
            "drwxr-xr-x 2 root root   4096 Oct  5 17:21 assets\n",
            "-rw-r--r-- 1 root root 173410 Oct  5 17:29 saved_model.pb\n",
            "drwxr-xr-x 2 root root   4096 Oct  5 17:29 variables\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1o8BlXcBFvIm"
      },
      "source": [
        "## Paso 6: Configurar el entorno de producción"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rSFWzxOrF0ft"
      },
      "source": [
        "### Exportar el MODEL_DIR a las variables de entorno"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BS6wsQthLX_i"
      },
      "source": [
        "os.environ['MODEL_DIR'] = os.path.abspath(MODEL_DIR)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TXejqj5F5oj"
      },
      "source": [
        "### Ejecutar la API REST de TensorFlow Serving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWjUhJA4KGWy",
        "outputId": "0d0328be-334b-46a3-c295-9016e296db02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%%bash --bg \n",
        "nohup tensorflow_model_server \\\n",
        "  --rest_api_port=8000 \\\n",
        "  --model_name=cifar10 \\\n",
        "  --model_base_path=\"${MODEL_DIR}\" >server.log 2>&1"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting job # 2 in a separate thread.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPa7fGZ5NHL5",
        "outputId": "964a2ba4-7c48-4c5b-90dc-1c7fa9480927",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "!tail server.log"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2020-10-05 17:29:41.103709: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:199] Restoring SavedModel bundle.\n",
            "2020-10-05 17:29:41.144999: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:183] Running initialization op on SavedModel bundle at path: /content/model/1\n",
            "2020-10-05 17:29:41.150494: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:303] SavedModel load for tags { serve }; Status: success: OK. Took 64753 microseconds.\n",
            "2020-10-05 17:29:41.151288: I tensorflow_serving/servables/tensorflow/saved_model_warmup_util.cc:59] No warmup data file found at /content/model/1/assets.extra/tf_serving_warmup_requests\n",
            "2020-10-05 17:29:41.151386: I tensorflow_serving/core/loader_harness.cc:87] Successfully loaded servable version {name: cifar10 version: 1}\n",
            "2020-10-05 17:29:41.152351: I tensorflow_serving/model_servers/server.cc:367] Running gRPC ModelServer at 0.0.0.0:8500 ...\n",
            "[warn] getaddrinfo: address family for nodename not supported\n",
            "2020-10-05 17:29:41.153051: I tensorflow_serving/model_servers/server.cc:387] Exporting HTTP/REST API at:localhost:8000 ...\n",
            "[evhttp_server.cc : 238] NET_LOG: Entering the event loop ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ICEqKpKUOC2F"
      },
      "source": [
        "## Paso 7: Crear nuestra primera petición por POST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvjZyyPIN3Zq",
        "outputId": "922134ce-b5f7-4ec1-f0bc-9af4cb81c389",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "random_image = np.random.randint(0, len(X_test))\n",
        "random_image"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqOfIBMNOK8p"
      },
      "source": [
        "### Crear el objeto de datos en JSON"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nB57JR6wONOI"
      },
      "source": [
        "data = json.dumps({\"signature_name\":\"serving_default\", \"instances\":[X_test[random_image].tolist()]})"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ez6JF3yIPdYZ",
        "outputId": "dc63f89c-5a91-46e8-85d8-914f2a4c5e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "data"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'{\"signature_name\": \"serving_default\", \"instances\": [[[[0.47843137254901963, 0.5372549019607843, 0.4549019607843137], [0.5529411764705883, 0.611764705882353, 0.5294117647058824], [0.4470588235294118, 0.5058823529411764, 0.4196078431372549], [0.403921568627451, 0.4666666666666667, 0.3803921568627451], [0.44313725490196076, 0.5058823529411764, 0.42745098039215684], [0.39215686274509803, 0.4588235294117647, 0.3764705882352941], [0.27058823529411763, 0.33725490196078434, 0.25882352941176473], [0.19215686274509805, 0.2549019607843137, 0.16470588235294117], [0.19215686274509805, 0.2549019607843137, 0.14901960784313725], [0.20392156862745098, 0.27450980392156865, 0.16862745098039217], [0.30980392156862746, 0.396078431372549, 0.29411764705882354], [0.34509803921568627, 0.42745098039215684, 0.3176470588235294], [0.3764705882352941, 0.44313725490196076, 0.30980392156862746], [0.3803921568627451, 0.44313725490196076, 0.33725490196078434], [0.3411764705882353, 0.4, 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0.5843137254901961, 0.6470588235294118], [0.3411764705882353, 0.4745098039215686, 0.5372549019607843], [0.32941176470588235, 0.47058823529411764, 0.5372549019607843], [0.2980392156862745, 0.3764705882352941, 0.41568627450980394], [0.2980392156862745, 0.36470588235294116, 0.3686274509803922], [0.30196078431372547, 0.403921568627451, 0.4392156862745098], [0.2823529411764706, 0.3843137254901961, 0.45098039215686275], [0.3215686274509804, 0.3686274509803922, 0.4235294117647059], [0.3058823529411765, 0.34509803921568627, 0.39215686274509803], [0.24705882352941178, 0.3764705882352941, 0.3843137254901961], [0.30196078431372547, 0.396078431372549, 0.403921568627451], [0.33725490196078434, 0.403921568627451, 0.403921568627451], [0.2980392156862745, 0.4196078431372549, 0.40784313725490196], [0.2901960784313726, 0.42745098039215684, 0.4117647058823529]], [[0.2235294117647059, 0.2823529411764706, 0.19215686274509805], [0.2196078431372549, 0.29411764705882354, 0.19607843137254902], [0.2235294117647059, 0.3058823529411765, 0.20784313725490197], [0.23529411764705882, 0.3215686274509804, 0.2196078431372549], [0.26666666666666666, 0.3568627450980392, 0.2627450980392157], [0.27450980392156865, 0.37254901960784315, 0.29411764705882354], [0.2823529411764706, 0.38823529411764707, 0.32941176470588235], [0.28627450980392155, 0.40784313725490196, 0.3568627450980392], [0.33725490196078434, 0.48627450980392156, 0.45098039215686275], [0.40784313725490196, 0.5490196078431373, 0.5529411764705883], [0.4588235294117647, 0.596078431372549, 0.615686274509804], [0.49411764705882355, 0.6392156862745098, 0.6509803921568628], [0.5137254901960784, 0.6784313725490196, 0.6980392156862745], [0.5254901960784314, 0.6745098039215687, 0.7058823529411765], [0.5137254901960784, 0.6431372549019608, 0.6823529411764706], [0.5803921568627451, 0.6901960784313725, 0.7372549019607844], [0.5490196078431373, 0.6627450980392157, 0.7137254901960784], [0.44313725490196076, 0.5803921568627451, 0.6039215686274509], [0.36470588235294116, 0.5176470588235295, 0.5058823529411764], [0.32941176470588235, 0.4627450980392157, 0.43529411764705883], [0.34901960784313724, 0.4745098039215686, 0.4392156862745098], [0.29411764705882354, 0.3568627450980392, 0.30980392156862746], [0.26666666666666666, 0.32941176470588235, 0.25882352941176473], [0.2549019607843137, 0.3686274509803922, 0.3058823529411765], [0.23137254901960785, 0.3333333333333333, 0.2980392156862745], [0.24705882352941178, 0.30196078431372547, 0.27450980392156865], [0.24705882352941178, 0.2823529411764706, 0.25098039215686274], [0.20784313725490197, 0.28627450980392155, 0.23137254901960785], [0.27450980392156865, 0.34901960784313724, 0.2980392156862745], [0.27058823529411763, 0.35294117647058826, 0.35294117647058826], [0.23921568627450981, 0.35294117647058826, 0.37254901960784315], [0.25882352941176473, 0.3803921568627451, 0.40784313725490196]], [[0.20392156862745098, 0.2784313725490196, 0.19607843137254902], [0.20784313725490197, 0.29411764705882354, 0.20784313725490197], [0.21568627450980393, 0.3137254901960784, 0.22745098039215686], [0.2235294117647059, 0.3215686274509804, 0.23529411764705882], [0.2627450980392157, 0.3607843137254902, 0.28627450980392155], [0.2784313725490196, 0.3843137254901961, 0.32941176470588235], [0.30196078431372547, 0.4117647058823529, 0.3803921568627451], [0.3137254901960784, 0.4470588235294118, 0.4235294117647059], [0.35294117647058826, 0.5137254901960784, 0.5137254901960784], [0.41568627450980394, 0.5686274509803921, 0.611764705882353], [0.4549019607843137, 0.6039215686274509, 0.6666666666666666], [0.5019607843137255, 0.6588235294117647, 0.7098039215686275], [0.4980392156862745, 0.6784313725490196, 0.7176470588235294], [0.4392156862745098, 0.6039215686274509, 0.6431372549019608], [0.44313725490196076, 0.5764705882352941, 0.6078431372549019], [0.5137254901960784, 0.6274509803921569, 0.6549019607843137], [0.5176470588235295, 0.6039215686274509, 0.6431372549019608], [0.403921568627451, 0.5019607843137255, 0.5137254901960784], [0.2980392156862745, 0.40784313725490196, 0.3764705882352941], [0.24313725490196078, 0.33725490196078434, 0.27058823529411763], [0.24705882352941178, 0.33725490196078434, 0.25882352941176473], [0.2549019607843137, 0.28627450980392155, 0.23921568627450981], [0.24705882352941178, 0.29411764705882354, 0.24705882352941178], [0.21568627450980393, 0.32941176470588235, 0.2549019607843137], [0.20392156862745098, 0.3058823529411765, 0.23137254901960785], [0.22745098039215686, 0.2784313725490196, 0.21568627450980393], [0.2549019607843137, 0.27450980392156865, 0.21176470588235294], [0.23137254901960785, 0.2549019607843137, 0.1803921568627451], [0.2823529411764706, 0.32941176470588235, 0.25098039215686274], [0.24705882352941178, 0.34509803921568627, 0.3058823529411765], [0.2196078431372549, 0.3333333333333333, 0.32941176470588235], [0.25098039215686274, 0.36470588235294116, 0.3764705882352941]]]]}'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZDKjyfbPoIa"
      },
      "source": [
        "### Enviar la primera petición POST al modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oFbtxiyPe4p"
      },
      "source": [
        "headers = {\"content-type\":\"application/json\"}"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5KHh02nQYIJ"
      },
      "source": [
        "json_response = requests.post(url=\"http://localhost:8000/v1/models/cifar10:predict\", data=data, headers=headers)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMYkCQ4gQ2tR",
        "outputId": "c33e0616-e250-4303-fbfb-ff9a5ea4a2b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "json_response"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Response [200]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtoG2441Q4_I"
      },
      "source": [
        "predictions = json.loads(json_response.text)['predictions']"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hA-jN6xSRK2S",
        "outputId": "2a3a5447-5732-4b5c-e8e7-401d1453cf4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "predictions"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.000112434522,\n",
              "  2.91902029e-06,\n",
              "  0.049065,\n",
              "  0.0013105449,\n",
              "  0.732287228,\n",
              "  0.000415284157,\n",
              "  0.21677956,\n",
              "  2.10254038e-05,\n",
              "  1.86315174e-06,\n",
              "  4.12425061e-06]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlyFNxrxRL_A",
        "outputId": "c29afcd6-0111-4658-e5a3-38a2ef060326",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.imshow(X_test[random_image])"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7b9e662128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3WbrMl_RW1Y",
        "outputId": "1c3378cf-c5a5-4994-9ccc-ad8542d57643",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        }
      },
      "source": [
        "class_names[np.argmax(predictions[0])]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ciervo'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVN6T3tFR_IQ"
      },
      "source": [
        "## Paso 8: Enviar una petición POST a un modelo específico "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TYXGo-XRkCo"
      },
      "source": [
        "specific_json_response = requests.post(url=\"http://localhost:8000/v1/models/cifar10/versions/1:predict\", data=data, headers=headers)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TF9nOKVeS9ER",
        "outputId": "a9366f91-2634-4289-a81d-4320eae83dbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "specific_json_response"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Response [200]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    }
  ]
}