05_Send_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c427989-2091-4916-a388-d1015ded029a",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = 'test_sample_1.jpg'\n",
    "my_route = 'http://####.openshiftapps.com/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "462aae17-f230-4b94-bc50-dbd3683896f1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'my_image' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dumps\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[48;5;17mmy_image\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m image_file:\n\u001b[1;32m      6\u001b[0m     encoded_image \u001b[38;5;241m=\u001b[39m base64\u001b[38;5;241m.\u001b[39mb64encode(image_file\u001b[38;5;241m.\u001b[39mread())\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      7\u001b[0m content \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m: encoded_image}\n",
      "\u001b[0;31mNameError\u001b[0m: name 'my_image' is not defined"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "import requests\n",
    "from json import dumps\n",
    "\n",
    "with open(my_image, \"rb\") as image_file:\n",
    "    encoded_image = base64.b64encode(image_file.read()).decode('utf-8')\n",
    "content = {\"image\": encoded_image}\n",
    "json_data = dumps(content)\n",
    "\n",
    "headers = {\"Content-Type\" : \"application/json\"}\n",
    "\n",
    "r = requests.post(my_route + '/predictions', data=json_data, headers=headers)\n",
    "\n",
    "#print(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "891932d0-e38b-4ecb-859b-1676e2ece803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc88336d820>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.imshow(np.array(r.json()['pred_mask']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "489fc44d-0ef0-4751-98af-91429f51f99d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": 5
}