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    "# Virtual Proctoring using Amazon Rekognition\n",
    "\n",
    "These notebook provide a walkthrough of some Amazon Rekognition APIs that are relevant to Virtual Proctoring.\n",
    "\n",
    "In order to start, it's necessary to create a bucket where to host sample images and videos used by each notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
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   "source": [
    "# First, let's get the latest installations of our dependencies\n",
    "!pip install --upgrade pip\n",
    "!pip install botocore --upgrade\n",
    "!pip install boto3 --upgrade\n",
    "!pip install IPython --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Dependencies\n",
    "import boto3\n",
    "import botocore\n",
    "\n",
    "# Initialize clients\n",
    "REGION = boto3.session.Session().region_name\n",
    "s3 = boto3.client('s3', REGION)\n",
    "sts = boto3.client(\"sts\")\n",
    "\n",
    "# An S3 Bucket is created and then sample images and videos are copied over there\n",
    "account_id = sts.get_caller_identity()[\"Account\"]\n",
    "bucket_name = \"amazon-rekognition-code-samples-{}-{}\".format(account_id, REGION)\n",
    "\n",
    "try:\n",
    "    s3.head_bucket(Bucket=bucket_name)\n",
    "except botocore.exceptions.ClientError as e:\n",
    "    error_code = int(e.response['Error']['Code'])\n",
    "    if error_code == 403:\n",
    "        print(\"Private Bucket. Forbidden Access! Please ensure the bucket is accessible from the Notebook\")\n",
    "    elif error_code == 404:\n",
    "        s3.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={\n",
    "            'LocationConstraint': REGION\n",
    "        })\n",
    "\n",
    "media = ['leaving.mp4', 'objects.mp4', 'cellphone.jpg', 'identity.jpg', 'looking_at_screen.jpg']\n",
    "\n",
    "for file in media:\n",
    "    file_name = \"media/{}\".format(file)\n",
    "    with open(file_name, 'rb') as data:\n",
    "        print(\"uploading s3://{}/{}\".format(bucket_name, file_name))\n",
    "        s3.upload_fileobj(data, bucket_name, file_name)\n",
    "\n",
    "print(\"All done\")\n",
    "\n",
    "%store bucket_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can proceed with the first lab."
   ]
  }
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