Nick Walsh nmwalsh

## comprehend-detect-key-entities.py
import boto3

# instantiate local comprehend client
comprehend_client = boto3.client('comprehend')


text = "A classic love heart emoji, used for expressions of love. Displayed in various shades of red on most platforms. A similar emoji exists for the heart suit in a deck of playing cards. On Snapchat, this emoji displays next to a friend when you have been #1 BFs with each other for two consecutive weeks."

# Invoke detect_key_phrases endopint and get response
comprehend_response = comprehend_client.detect_key_phrases(Text=text, LanguageCode='en')

## template.html
<!doctype html>
<html lang="en" prefix="op: http://media.facebook.com/op#">
  <head>
    <meta charset="utf-8">
    <!-- URL of the web version of this article -->
    <!-- TODO: Change the domain to match the domain of your website -->
    <link rel="canonical" href="http://example.com/article.html">
    <meta property="op:markup_version" content="v1.0">
  </head>
  <body>

## falcon_gateway.py
# falcon_gateway.py

import falcon
import json
from data_handler import invoke_predict

# Falcon follows the REST architectural style, meaning (among
# other things) that you think in terms of resources and state
# transitions, which map to HTTP verbs.

## charge_credit_card.py
from authorizenet import apicontractsv1
from authorizenet.apicontrollers import*
from decimal import*

import credentials # importing our credentials from credentials.py

# Authentication steps using Authorize.Net API credentials
merchantAuth = apicontractsv1.merchantAuthenticationType()
merchantAuth.name = credentials.api_login_name
merchantAuth.transactionKey = credentials.transaction_key

## select a language version
(1) py27
(2) py35
Please select one of the above environment language (e.g. py27):

## select an environment
(1) data-analytics : has libraries such as xgboost, lightgbm, sklearn etc.
(2) mxnet : has libraries for mxnet(v1.1.0) along with sklearn, opencv etc.
(3) caffe2 : has libraries for caffe2(v0.8.0) along with sklearn, opencv etc.
(4) keras-tensorflow : has libraries for keras(v2.1.6) and tensorflow(v1.9.0) along with sklearn, opencv etc.
(5) kaggle : has the environment provided by kaggle
(6) pytorch : has libraries for pytorch(v0.4.0) along with sklearn, opencv etc.
(7) python-base : has base python image with no libraries installed
(8) r-base : has base R image with no libraries installed. Use this environment for rstudio workspace
Please select one of the above environments (e.g. 1 or data-analytics):

## select system drivers
(1) gpu
(2) cpu
Please select one of the above environment type (e.g. 1 or gpu):

## torch_wipe.sh
if [[ `uname` == 'Linux' ]]; then
    echo 'Removing old Torch files from your Linux...'
    # Removing folders
    sudo rm -rf /usr/local/lib/{luarocks/,lua/,torch/,torchrocks/}
    sudo rm -rf /usr/local/share/{torch,cmake/torch/,lua}
    sudo rm -rf /usr/local/etc/{luarocks/,torchrocks/}
    sudo rm -rf /usr/local/include/{torch,TH,THC,lauxlib.h,lua.h,lua.hpp,luaT.h,luaconf.h,luajit.h,lualib.h,qtlua}
    sudo rm -rf ~/.luarocks
    sudo rm -rf ~/.cache/luarocks*
    # Removing files

## FalconMainCode
# falcon.API instances are callable WSGI apps. Never change this.
app = falcon.API()

# Resources are represented by long-lived class instances. Each Python class becomes a different "URL directory"
info = InfoResource()
predicts = PredictsResource()
# things will handle all requests to the '/info' or '/predicts' URL path
app.add_route('/info', info)
app.add_route('/predicts', predicts)

## PredictsResource
class PredictsResource(object):
    def on_get(self, req, resp):
        """Handles GET requests"""
        resp.status = falcon.HTTP_200  # This is the default status
        resp.body = ('\nThis is the PREDICT endpoint. \n'
        			 'Both requests and responses are served in JSON. \n'
        		     '\n'
        		     'INPUT:  Flower Lengths (in cm) \n'
                     '   "sepal_length":[num]        \n'
                     '   "sepal_width": [num]        \n'
	import boto3

	# instantiate local comprehend client
	comprehend_client = boto3.client('comprehend')


	text = "A classic love heart emoji, used for expressions of love. Displayed in various shades of red on most platforms. A similar emoji exists for the heart suit in a deck of playing cards. On Snapchat, this emoji displays next to a friend when you have been #1 BFs with each other for two consecutive weeks."

	# Invoke detect_key_phrases endopint and get response
	comprehend_response = comprehend_client.detect_key_phrases(Text=text, LanguageCode='en')
	<!doctype html>
	<html lang="en" prefix="op: http://media.facebook.com/op#">
	<head>
	<meta charset="utf-8">
	<!-- URL of the web version of this article -->
	<!-- TODO: Change the domain to match the domain of your website -->
	<link rel="canonical" href="http://example.com/article.html">
	<meta property="op:markup_version" content="v1.0">
	</head>
	<body>
	# falcon_gateway.py

	import falcon
	import json
	from data_handler import invoke_predict

	# Falcon follows the REST architectural style, meaning (among
	# other things) that you think in terms of resources and state
	# transitions, which map to HTTP verbs.
	from authorizenet import apicontractsv1
	from authorizenet.apicontrollers import*
	from decimal import*

	import credentials # importing our credentials from credentials.py

	# Authentication steps using Authorize.Net API credentials
	merchantAuth = apicontractsv1.merchantAuthenticationType()
	merchantAuth.name = credentials.api_login_name
	merchantAuth.transactionKey = credentials.transaction_key
	(1) py27
	(2) py35
	Please select one of the above environment language (e.g. py27):
	(1) data-analytics : has libraries such as xgboost, lightgbm, sklearn etc.
	(2) mxnet : has libraries for mxnet(v1.1.0) along with sklearn, opencv etc.
	(3) caffe2 : has libraries for caffe2(v0.8.0) along with sklearn, opencv etc.
	(4) keras-tensorflow : has libraries for keras(v2.1.6) and tensorflow(v1.9.0) along with sklearn, opencv etc.
	(5) kaggle : has the environment provided by kaggle
	(6) pytorch : has libraries for pytorch(v0.4.0) along with sklearn, opencv etc.
	(7) python-base : has base python image with no libraries installed
	(8) r-base : has base R image with no libraries installed. Use this environment for rstudio workspace
	Please select one of the above environments (e.g. 1 or data-analytics):
	(1) gpu
	(2) cpu
	Please select one of the above environment type (e.g. 1 or gpu):
	if [[ `uname` == 'Linux' ]]; then
	echo 'Removing old Torch files from your Linux...'
	# Removing folders
	sudo rm -rf /usr/local/lib/{luarocks/,lua/,torch/,torchrocks/}
	sudo rm -rf /usr/local/share/{torch,cmake/torch/,lua}
	sudo rm -rf /usr/local/etc/{luarocks/,torchrocks/}
	sudo rm -rf /usr/local/include/{torch,TH,THC,lauxlib.h,lua.h,lua.hpp,luaT.h,luaconf.h,luajit.h,lualib.h,qtlua}
	sudo rm -rf ~/.luarocks
	sudo rm -rf ~/.cache/luarocks*
	# Removing files
	# falcon.API instances are callable WSGI apps. Never change this.
	app = falcon.API()

	# Resources are represented by long-lived class instances. Each Python class becomes a different "URL directory"
	info = InfoResource()
	predicts = PredictsResource()
	# things will handle all requests to the '/info' or '/predicts' URL path
	app.add_route('/info', info)
	app.add_route('/predicts', predicts)
	class PredictsResource(object):
	def on_get(self, req, resp):
	"""Handles GET requests"""
	resp.status = falcon.HTTP_200 # This is the default status
	resp.body = ('\nThis is the PREDICT endpoint. \n'
	'Both requests and responses are served in JSON. \n'
	'\n'
	'INPUT: Flower Lengths (in cm) \n'
	' "sepal_length":[num] \n'
	' "sepal_width": [num] \n'