{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "collectible-socket",
   "metadata": {},
   "source": [
    "### OCI Data Science - Useful Tips\n",
    "<details>\n",
    "<summary><font size=\"2\">Check for Public Internet Access</font></summary>\n",
    "\n",
    "```python\n",
    "import requests\n",
    "response = requests.get(\"https://oracle.com\")\n",
    "assert response.status_code==200, \"Internet connection failed\"\n",
    "```\n",
    "</details>\n",
    "<details>\n",
    "<summary><font size=\"2\">Helpful Documentation </font></summary>\n",
    "<ul><li><a href=\"https://docs.cloud.oracle.com/en-us/iaas/data-science/using/data-science.htm\">Data Science Service Documentation</a></li>\n",
    "<li><a href=\"https://docs.cloud.oracle.com/iaas/tools/ads-sdk/latest/index.html\">ADS documentation</a></li>\n",
    "</ul>\n",
    "</details>\n",
    "<details>\n",
    "<summary><font size=\"2\">Typical Cell Imports and Settings for ADS</font></summary>\n",
    "\n",
    "```python\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import logging\n",
    "logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.ERROR)\n",
    "\n",
    "import ads\n",
    "from ads.dataset.factory import DatasetFactory\n",
    "from ads.automl.provider import OracleAutoMLProvider\n",
    "from ads.automl.driver import AutoML\n",
    "from ads.evaluations.evaluator import ADSEvaluator\n",
    "from ads.common.data import ADSData\n",
    "from ads.explanations.explainer import ADSExplainer\n",
    "from ads.explanations.mlx_global_explainer import MLXGlobalExplainer\n",
    "from ads.explanations.mlx_local_explainer import MLXLocalExplainer\n",
    "from ads.catalog.model import ModelCatalog\n",
    "from ads.common.model_artifact import ModelArtifact\n",
    "```\n",
    "</details>\n",
    "<details>\n",
    "<summary><font size=\"2\">Useful Environment Variables</font></summary>\n",
    "\n",
    "```python\n",
    "import os\n",
    "print(os.environ[\"NB_SESSION_COMPARTMENT_OCID\"])\n",
    "print(os.environ[\"PROJECT_OCID\"])\n",
    "print(os.environ[\"USER_OCID\"])\n",
    "print(os.environ[\"TENANCY_OCID\"])\n",
    "print(os.environ[\"NB_REGION\"])\n",
    "```\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hearing-poster",
   "metadata": {},
   "source": [
    "# Making Classification Requests to Your Model Endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confidential-dairy",
   "metadata": {},
   "source": [
    "On your model deployment page in the OCI console, go to the 'Invoking Your Model' page and copy the model HTTP endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "excessive-rebound",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "your-http-endpoint-here\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import requests\n",
    "import oci\n",
    "from oci.signer import Signer\n",
    "\n",
    "uri = 'your-http-endpoint-here'\n",
    "print(uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "nutritional-contest",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Import data\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Flatten images\n",
    "n_samples = len(digits.images)\n",
    "data = digits.images.reshape((n_samples, -1))\n",
    "\n",
    "# Split data into 50% train and 50% test subsets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data, digits.target, test_size=0.5, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "postal-underwear",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'prediction': [8, 8, 4, 9, 0, 8, 9, 8, 1, 2]}\n",
      "CPU times: user 32.7 ms, sys: 1.92 ms, total: 34.6 ms\n",
      "Wall time: 547 ms\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "# Payload:\n",
    "# Replace this with your own testing data if you've deployed your own model, remember to jsonify the payload though!\n",
    "input_data = pd.DataFrame(X_test[0:10]).to_json()\n",
    "body = input_data\n",
    "\n",
    "config = oci.config.from_file(\"~/.oci/config\") # replace with the location of your oci config file\n",
    "auth = Signer(\n",
    "    tenancy=config['tenancy'],\n",
    "    user=config['user'],\n",
    "    fingerprint=config['fingerprint'],\n",
    "    private_key_file_location=config['key_file'],\n",
    "    pass_phrase=config['pass_phrase'])\n",
    "    \n",
    "# Submit request to model endpoint:\n",
    "response = requests.post(endpoint, json=input_data, auth=auth).json()\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "incorporate-continent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z83D4LWmCDLXUGEMtNcZQS40x1FJjDLXUGEMtNeb/ACwS0vq4cbcsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b2DzlepZs6r/SkjRes3D6LWmMDLXUGEMtNcZQS40x1FJjDLXUGEMtNeb/AMhpwfxW4ZnnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w3pTrWbap/0pL0njNwuG3pDEy1FJjDLXUGEMtNcZQS40x1FJjDLXUmP8FQH7WBCzJ/o8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DW4DLuw7mTuD1M65n0Wb+Ky1JkzUPh9+SJshQS40x1FJjDLXUGEMtNcZQS40x1FJj/hd5qt76CaOExAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UGEMtNcZQS40x1FJjDLXUmP8D8YfEdc6WJEUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TZKilxhhqqTGGWmqMoZYaY6ilxhhqqTH/Cx9p6fXaY+5dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7mHuA10+5n2Wb+q+0JI3WLBx+SxohQy01xlBLjTHUUmMMtdQYQy01xlBLjflfcqa+eygmpEoAAAAASUVORK5CYII=\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": {
      "image/png": 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uQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Display sample digits and their classifications\n",
    "for image, prediction in zip(X_test, response['prediction']):\n",
    "    image = image.reshape(8, 8)\n",
    "    plt.imshow(image, cmap=plt.cm.gray_r)\n",
    "    plt.title(f'Prediction: {prediction}')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:mlcpuv1]",
   "language": "python",
   "name": "conda-env-mlcpuv1-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}