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ec2_client = boto3.client('ec2')

user_data = """#!/bin/bash
sudo apt-get update
curl "https://bootstrap.pypa.io/get-pip.py" -o "get-pip.py"
sudo python3 get-pip.py
sudo pip3 install boto3
sudo apt-get install -y libgtk2.0-dev
sudo pip3 install opencv-python
echo "{}" >> {}

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<script src="https://unpkg.com/uport-connect/dist/uport-connect.min.js"></script>
<script src="https://unpkg.com/axios/dist/axios.min.js"></script>


<script type="text/javascript">
  function uport() {
    try {
      const u = new window.uportconnect.Connect('blockimmo', {
        clientId: '2ohEPzgzsh7gm68BUcHQMkfaQs8BA4ysatY',
        network: 'rinkeby',

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import collections


def sort_raw_rekognition_results(results):
    indices = collections.defaultdict(list)  # unique people detected in video to a `list` of their `PersonMatch` objects
    
    timestamps = collections.defaultdict(list)  # `lists` maintain order so we can keep track of people and their indices
    timestamps_indices = collections.defaultdict(list)

    for p in self.results['Persons']:

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import collections
import json


def duplicates():
    #
    # organize raw Rekognition `boto3.client('rekognition').get_face_search()` response for debugging this issue
    #

    with open('duplicated_index_bug.json', 'r') as f:  # https://s3.us-east-2.amazonaws.com/brayniac-waya-ai/duplicated_index_bug.json

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import io

import boto3
import PIL.Image
import torch
from torch.utils import model_zoo
import torchvision


s3_client = boto3.client('s3')

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#
# written for Amazon Linux AMI
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
# assumes lambda function defined in ~/main.py
# deployment package created at ~/waya-ai-lambda.zip
#

#
# install python 3.6.1
#

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import io

from PIL import Image  # https://pillow.readthedocs.io/en/4.3.x/
import requests  # http://docs.python-requests.org/en/master/


# example image url: https://m.media-amazon.com/images/S/aplus-media/vc/6a9569ab-cb8e-46d9-8aea-a7022e58c74a.jpg
def download_image(url, image_file_path):
    r = requests.get(url, timeout=4.0)
    if r.status_code != requests.codes.ok:

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import torch
from torch import autograd
from torch import nn


class CrossEntropyLoss(nn.Module):
    """
    This criterion (`CrossEntropyLoss`) combines `LogSoftMax` and `NLLLoss` in one single class.
    
    NOTE: Computes per-element losses for a mini-batch (instead of the average loss over the entire mini-batch).

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"""
wGAN implemented on top of tensorflow as described in: [Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf)
with improvements as described in: [Improved Training of Wasserstein GANs](https://arxiv.org/pdf/1704.00028.pdf).

"""

import tensorflow as tf


#

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import torch
from torch.autograd import Variable
import torch.nn as nn


class Bottleneck(nn.Module):
    cardinality = 32  # the size of the set of transformations

    def __init__(self, nb_channels_in, nb_channels, nb_channels_out, stride=1):
        super().__init__()