send a request to azure instance where model is deployed

get_founder_from_deployed_model.ipynb

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   "cell_type": "code",
   "execution_count": 95,
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   "source": [
    "import cv2\n",
    "import requests\n",
    "\n",
    "def get_founder(image_directory):\n",
    "\n",
    "    image=cv2.imread(image_directory)\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\n",
    "    faces = faceCascade.detectMultiScale(\n",
    "            gray,\n",
    "            scaleFactor=1.1,\n",
    "            minNeighbors=4,\n",
    "            minSize=(100, 100)\n",
    "    ) \n",
    "    \n",
    "    try:\n",
    "        if faces.shape[0]>1:\n",
    "            return print('too many faces on the picture')\n",
    "        \n",
    "    except:\n",
    "        return print('no face detected in the picture')\n",
    "        \n",
    "    for (x, y, w, h) in faces:\n",
    "        crop_img = gray[y:y+h, x:x+w]\n",
    "        resized_img=cv2.resize(crop_img,(28,28))\n",
    "\n",
    "    # send a random row from the test set to score\n",
    "    input_data = \"{\\\"data\\\": [\" + str(resized_img.tolist()) + \"]}\"\n",
    "\n",
    "    headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
    "\n",
    "    resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
    "\n",
    "    #print(\"input data:\", input_data)\n",
    "    return print(\"prediction:\", resp.text)"
   ]
  }
  ],
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