facial_recog_and_processor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data.json') as json_file:\n",
    "    data = json.load(json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = data['0'][0]['faceAttributes']['emotion']\n",
    "\n",
    "index = []\n",
    "for p in a:\n",
    "    index.append(p)\n",
    "\n",
    "\n",
    "df = pd.DataFrame(columns=index)\n",
    "\n",
    "for p in data:\n",
    "    for i in range(len(data[p])):\n",
    "        df.loc[len(df)] = list(data[p][i]['faceAttributes']['emotion'].values())\n",
    "\n",
    "        \n",
    "    for c in df.tail(10).columns:\n",
    "        plt.plot(df.tail(10).index, df.tail(10)[c], label=c)\n",
    "\n",
    "    plt.axis(\"off\")\n",
    "    #plt.legend()\n",
    "    plt.savefig(\"emo_vis/\" + \"image%05d.png\" % (int(p) / 10), transparent=True)\n",
    "    \n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8388676be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in df.columns:\n",
    "    plt.plot(df.index, df[c], label=c)\n",
    "plt.legend()\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anger</th>\n",
       "      <th>contempt</th>\n",
       "      <th>disgust</th>\n",
       "      <th>fear</th>\n",
       "      <th>happiness</th>\n",
       "      <th>neutral</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.299</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.240</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.092</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.023</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.014</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    anger  contempt  disgust   fear  happiness  neutral  sadness  surprise\n",
       "0   0.006     0.005    0.002  0.000      0.014    0.813    0.159     0.000\n",
       "1   0.007     0.003    0.002  0.000      0.009    0.830    0.149     0.000\n",
       "2   0.299     0.001    0.138  0.000      0.006    0.544    0.007     0.005\n",
       "3   0.240     0.000    0.005  0.001      0.072    0.558    0.002     0.122\n",
       "4   0.092     0.000    0.003  0.000      0.009    0.881    0.001     0.014\n",
       "5   0.003     0.000    0.000  0.000      0.041    0.755    0.001     0.200\n",
       "6   0.000     0.000    0.000  0.000      1.000    0.000    0.000     0.000\n",
       "7   0.000     0.000    0.000  0.000      0.811    0.187    0.002     0.000\n",
       "8   0.000     0.001    0.000  0.001      0.001    0.895    0.010     0.091\n",
       "9   0.023     0.009    0.001  0.000      0.095    0.844    0.001     0.026\n",
       "10  0.000     0.000    0.000  0.000      0.007    0.992    0.001     0.000\n",
       "11  0.000     0.000    0.000  0.000      0.007    0.986    0.006     0.000\n",
       "12  0.000     0.000    0.000  0.000      1.000    0.000    0.000     0.000\n",
       "13  0.014     0.000    0.001  0.000      0.047    0.925    0.011     0.002\n",
       "14  0.001     0.000    0.000  0.000      0.180    0.803    0.001     0.013\n",
       "15  0.000     0.000    0.000  0.000      0.999    0.001    0.000     0.000\n",
       "16  0.000     0.000    0.000  0.000      0.618    0.380    0.000     0.001\n",
       "17  0.000     0.004    0.000  0.000      0.086    0.904    0.006     0.000\n",
       "18  0.000     0.000    0.000  0.000      0.000    0.733    0.000     0.266\n",
       "19  0.000     0.000    0.000  0.000      0.018    0.978    0.004     0.000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8397db5898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = np.arange(0, 2*np.pi, 0.01)\n",
    "line, = ax.plot(x, np.sin(x))\n",
    "\n",
    "\n",
    "def init():  # only required for blitting to give a clean slate.\n",
    "    line.set_ydata([np.nan] * len(x))\n",
    "    return line,\n",
    "\n",
    "\n",
    "def animate(i):\n",
    "    line.set_ydata(np.sin(x + i / 100))  # update the data.\n",
    "    return line,\n",
    "\n",
    "\n",
    "ani = animation.FuncAnimation(\n",
    "    fig, animate, init_func=init, interval=2, blit=True, save_count=50)\n",
    "\n",
    "# To save the animation, use e.g.\n",
    "#\n",
    "# ani.save(\"movie.mp4\")\n",
    "#\n",
    "# or\n",
    "#\n",
    "# from matplotlib.animation import FFMpegWriter\n",
    "# writer = FFMpegWriter(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
    "# ani.save(\"movie.mp4\", writer=writer)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger',\n",
       " 'contempt',\n",
       " 'disgust',\n",
       " 'fear',\n",
       " 'happiness',\n",
       " 'neutral',\n",
       " 'sadness',\n",
       " 'surprise']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_data = {\n",
    "    \"time\": 96,\n",
    "    \"positive\": 0,\n",
    "    \"negative\": 0,\n",
    "    \"anger\": 1.2,\n",
    "    \"contempt\": 1.2,\n",
    "    \"disgust\": 1.2,\n",
    "    \"fear\": 1.2,\n",
    "    \"happiness\": 1.2,\n",
    "    \"neutral\": 1.2,\n",
    "    \"sadness\": 1.2,\n",
    "    \"surprise\": 1.2\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': 96,\n",
       " 'positive': 0,\n",
       " 'negative': 0,\n",
       " 'anger': 1.2,\n",
       " 'contempt': 1.2,\n",
       " 'disgust': 1.2,\n",
       " 'fear': 1.2,\n",
       " 'happiness': 1.2,\n",
       " 'neutral': 1.2,\n",
       " 'sadness': 1.2,\n",
       " 'surprise': 1.2}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'anger': 0.006, 'contempt': 0.005, 'disgust': 0.002, 'fear': 0.0, 'happiness': 0.014, 'neutral': 0.813, 'sadness': 0.159, 'surprise': 0.0}\n"
     ]
    }
   ],
   "source": [
    "a = data['0'][0]['faceAttributes']['emotion']\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import cv2\n",
    "import cognitive_face as CF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'img_cache.jpg'\n",
    "cam = cv2.VideoCapture(0)\n",
    "\n",
    "KEY = 'd362531db61f41728ebff0e734b80fc1'  # Replace with a valid Subscription Key here.\n",
    "CF.Key.set(KEY)\n",
    "\n",
    "BASE_URL = 'https://westcentralus.api.cognitive.microsoft.com/face/v1.0'  # Replace with your regional Base URL\n",
    "CF.BaseUrl.set(BASE_URL)\n",
    "\n",
    "url = \"https://api.powerbi.com/beta/03e3d621-f48e-467f-8a5a-6b74a23db6c7/datasets/b3cfa586-1a02-4f41-90c1-062be84a779d/rows?key=lXWZZF%2BU4Af%2FWaCUML%2BdVrk6V4NMHWfbOxTdrT33tibeQcICeBTdP935AFO9rLvXp6RvspB7k0Kby6Q1nWt8UA%3D%3D\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n",
      "200 OK\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-1822556ed63b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'faceAttributes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'emotion'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 1"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    \n",
    "    retval, frame = cam.read()\n",
    "    \n",
    "    if retval != True:\n",
    "        raise ValueError(\"Can't read frame\")\n",
    "    cv2.imwrite(path, frame)\n",
    "    \n",
    "    attributes = (\n",
    "                'age,gender,headPose,smile,facialHair,glasses,emotion,hair,'\n",
    "                'makeup,occlusion,accessories,blur,exposure,noise')\n",
    "    result = CF.face.detect(path, False, False, attributes)\n",
    "    for i in range(len(result)):\n",
    "\n",
    "        result = result[i]\n",
    "        \n",
    "        a = result['faceAttributes']['emotion']\n",
    "\n",
    "        a['time'] = '{}-{}-{} {}:{}:{}'.format(*time.gmtime(time.time()))\n",
    "        a['positive'] = a['anger'] + a['surprise'] + a['happiness']\n",
    "        a['negative'] = a['fear'] + a['disgust'] + a['contempt']\n",
    "\n",
    "        b = json.dumps(a)\n",
    "\n",
    "        r = requests.post(url, data=b)\n",
    "        print(r.status_code, r.reason)\n",
    "\n",
    "        time.sleep(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cam.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://api.powerbi.com/beta/03e3d621-f48e-467f-8a5a-6b74a23db6c7/datasets/b3cfa586-1a02-4f41-90c1-062be84a779d/rows?key=lXWZZF%2BU4Af%2FWaCUML%2BdVrk6V4NMHWfbOxTdrT33tibeQcICeBTdP935AFO9rLvXp6RvspB7k0Kby6Q1nWt8UA%3D%3D\"\n",
    "\n",
    "\n",
    "\n",
    "for p in data:\n",
    "    if len(data[p]) > 0:\n",
    "        a = data[p][0]['faceAttributes']['emotion']\n",
    "            \n",
    "        a['time'] = '{}-{}-{} {}:{}:{}'.format(*time.gmtime(time.time()))\n",
    "        a['positive'] = a['anger'] + a['surprise'] + a['happiness']\n",
    "        a['negative'] = a['fear'] + a['disgust'] + a['contempt']\n",
    "    \n",
    "        \n",
    "        b = json.dumps(a)\n",
    "        \n",
    "        r = requests.post(url, data=b)\n",
    "        print(r.status_code, r.reason)\n",
    "        time.sleep(1)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r = requests.post(url, data=t_data)\n",
    "print(r.status_code, r.reason)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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