def home():
"""Renders the home page which is the CNS of the web app currently, nothing pretty."""
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST' and form.validate():
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
data = {
"Inputs": {
"input1": {
"ColumnNames": ["gender", "age", "size", "weight"],
"Values": [[0, 1, form.title.data.lower(), 0]]
}
},
"GlobalParameters": {}
}
# Serialize the input data into json string
body = str.encode(json.dumps(data))
# Formulate the request
#req = urllib.request.Request(URL, body, HEADERS)
req = urllib.request.Request(BRAIN_URL, body, HEADERS)
# Send this request to the AML service and render the results on page
try:
# response = requests.post(URL, headers=HEADERS, data=body)
response = urllib.request.urlopen(req)
#print(response)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = do_something_pretty(result)
# result = json.dumps(result, indent=4, sort_keys=True)
return render_template(
'result.html',
title=
"This is the result from AzureML running our example Student Brain Weight Prediction:",
result=result)
# An HTTP error
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('result.html',
title='There was an error',
result=result)
#print(err)
# Just serve up the input form
return render_template(
'form.html',
form=form,
title='Run App',
year=datetime.now().year,
message='Demonstrating a website using Azure ML Api')
def do_something_pretty(jsondata):
form = SubmissionForm(request.form)
"""We want to process the AML json result to be more human readable and understandable"""
import itertools # for flattening a list of tuples below
# We only want the first array from the array of arrays under "Value"
# - it's cluster assignment and distances from all centroid centers from k-means model
value = jsondata["Results"]["output1"]["value"]["Values"][0]
# valuelen = len(value)
print(value)
scored_label = value[8]
# Convert values (a list) to a list of tuples [(cluster#,distance),...]
# valuetuple = list(zip(range(valuelen-1), value[1:(valuelen)]))
# Convert the list of tuples to one long list (flatten it)
# valuelist = list(itertools.chain(*valuetuple))
# Convert to a tuple for the list
# data = tuple(list(value[0]) + valuelist)
# Build a placeholder for the cluster#,distance values
#repstr = '<tr><td>%d</td><td>%s</td></tr>' * (valuelen-1)
# print(repstr)
output_bayesian = f'Our machine learning model predicted a closing value of {str(round(float(scored_label),2))} USD for the 30th trading day following {date.today()}.'
# Build the entire html table for the results data representation
#tablestr = 'Cluster assignment: %s<br><br><table border="1"><tr><th>Cluster</th><th>Distance From Center</th></tr>'+ repstr + "</table>"
#return tablestr % data
return output_bayesian
def secondresult():
form = SubmissionForm(request.form)
etf_weight = form.etf_weighting.data
bond_weight = form.bond_weighting.data
energy_weight = form.energy_weighting.data
tech_weight = form.tech_weighting.data
util_weight = form.util_weighting.data
fin_weight = form.fin_weighting.data
health_weight = form.health_weighting.data
constap_weight = form.constap_weighting.data
condisc_weight = form.condisc_weighting.data
energy_tick = form.energy_ticker.data
tech_tick = form.tech_ticker.data
util_tick = form.util_ticker.data
fin_tick = form.fin_ticker.data
health_tick = form.health_ticker.data
constap_tick = form.constap_ticker.data
condisc_tick = form.condisc_ticker.data
datetime_1 = datetime.now()
date_1 = date.today()
weights_chosen = [
etf_weight, bond_weight, energy_weight, tech_weight, util_weight,
fin_weight, health_weight, constap_weight, condisc_weight
]
tickers_chosen = [
energy_tick, tech_tick, util_tick, fin_tick, health_tick, constap_tick,
condisc_tick
]
#timestamp conversion from user-submitted date
timestamp_t = str(
int(datetime.strptime(str(date_1), '%Y-%m-%d').timestamp()))
timestamp_priordays = str(int(timestamp_t) - (86400 * 7))
#variables for call
url = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/v2/get-historical-data"
# Financial (VFH ETF)
etf_tickers = [
'VFH', 'VGT', 'YLCO', 'IHI', 'IEO', 'VDC', 'DBC', 'FREL', 'AGG', 'IYF',
'KRE', 'FTEC', 'FDN', 'RYU', 'FUTY', 'FHLC', 'XHE', 'XLE', 'PXI',
'FSTA', 'IYK', 'PDBC', 'GSG', 'MORT', 'VNQ'
]
etf_attr_list = []
for i in etf_tickers:
single_etf = []
querystring = {
"frequency": "1d",
"filter": "history",
"period1": timestamp_priordays,
"period2": timestamp_t,
"symbol": i
}
headers = {
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com",
'x-rapidapi-key':
"ca07005c56mshafe5b7a7c516a9dp1b90e2jsn1e7c85e6edd1"
}
response = requests.request("GET",
url,
headers=headers,
params=querystring)
#convert response variable to json format for slicing / variable assignment
response_json = response.json()
# feature set variables
open_t = response_json['prices'][0]['open']
high_t = response_json['prices'][0]['high']
low_t = response_json['prices'][0]['low']
close_t = response_json['prices'][0]['close']
adjclose_t = response_json['prices'][0]['adjclose']
volume_t = response_json['prices'][0]['volume']
t_1_closediff = response_json['prices'][1]['close'] - response_json[
'prices'][0]['close']
single_etf.extend((open_t, high_t, low_t, close_t, adjclose_t,
volume_t, t_1_closediff))
etf_attr_list.append(single_etf)
global closing_price
closing_price = close_t
if request.method == 'POST':
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
body_list = []
for i in etf_attr_list:
data = {
"Inputs": {
"input1": {
"ColumnNames": [
# "Date",
"Open",
"High",
"Low",
"Close",
"Adj Close",
"Volume",
"T-1_Close_Diff",
"T+30_Close"
],
"Values": [[
# str(date_1),
i[0],
i[1],
i[2],
i[3],
i[4],
i[5],
i[6],
"0"
]]
}
},
"GlobalParameters": {}
}
# Serialize the input data into json string
body = str.encode(json.dumps(data))
body_list.append(body)
VFH_ML_KEY = os.environ.get(
'API_KEY',
"8FErDr2F8XnmhbR0XscVKYRpgAC4R7E7yR9cIFyCAzpZavP0a1FaWQ9AV9qBvw7SYc8pNc7CfU9rS2oSI5AIgg=="
)
VFH_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/171d5eadacbb47b5ab0811ef139f692b/execute?api-version=2.0&details=true"
)
# Deployment environment variables defined on Azure (pull in with os.environ)
VGT_ML_KEY = os.environ.get(
'API_KEY',
"3BgDbPsWBJQ8+a3i9kXiSYRCjJuYZ97IIqbfT/gjWbNYixGdlKWiJvN6OvEYY2brXFhYSDshlNUJCtf8kc7REg=="
)
VGT_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/467d867f6dbd4f70b1015b2eb21eab4b/execute?api-version=2.0&details=true"
)
YLCO_ML_KEY = os.environ.get(
'API_KEY',
"czcrAnbVlR+GrpAFatQNuFD0HOQuWnU+9mXSZC1XJR+UF+zYJiDce9fysiiGe90bO4wTrtWa45BV6bEJgbeTmg=="
)
YLCO_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/bf2d38a37d9a4c919f963e9df662c35e/execute?api-version=2.0&details=true"
)
IHI_ML_KEY = os.environ.get(
'API_KEY',
"brT8wq3oF7SVXiHd0gFqiRTxnFygqVBNujs21JJ6HFHhqjKfVhoUbCzIfopC/g5JLc5jeDrEcVbr5GdvnATVTQ=="
)
IHI_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/ce7d321e9e7240c0bc2e11dfae547a18/execute?api-version=2.0&details=true"
)
IEO_ML_KEY = os.environ.get(
'API_KEY',
"IvZHJcesvOSHNzGmB++orqv4B2ivf2fqchqOx4A6ATL61AfJquyVzb9LYpySQFD2vYIvoCqM5jIYgzYPV47l0w=="
)
IEO_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/7a32eb19edfa49768b38be1b00778a5c/execute?api-version=2.0&details=true"
)
VDC_ML_KEY = os.environ.get(
'API_KEY',
"oSd5LHaVAmDJOoUIE04hXaZLJ2lbHoaNIkCXUeAdAT4CZzhfl/rRh0geSzAU3FrXRLVuDAMTyjgHOhfsv1dYAA=="
)
VDC_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/dc4ab8189fd84a7085062076e4ba12a2/execute?api-version=2.0&details=true"
)
DBC_ML_KEY = os.environ.get(
'API_KEY',
"TJH8ueT5rjb3JQN962ytyrRvuPECX2IIUBpN+xUMZwcqfaKxvwlRXXnpcwF/z64Ret1edLO79NjYIFWbsG2lbQ=="
)
DBC_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/99b436f2f83c4db693fdd3351d109fbe/execute?api-version=2.0&details=true"
)
FREL_ML_KEY = os.environ.get(
'API_KEY',
"2GMc3n+xQoK+2Nie+jiTXANIMxRzXT8U5T/IYFQIeY7mJVH+jYwmc3FPETEMMBqHQmjTzF8gbHgM8E0gbdAoTg=="
)
FREL_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/0cf918761a0245df91c9df109ac8ead8/execute?api-version=2.0&details=true"
)
AGG_ML_KEY = os.environ.get(
"API_KEY",
"99n5TXjg/US+B8iKuUikp7AMoYO81Ak3Umq6xzraiwMeKPRrByEl3bQhNFGMxWY/eeg4f7hUSUGjEHv4nvEM6Q=="
)
AGG_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/2402bd9da1eb40c2a90551198b4d209d/execute?api-version=2.0&details=true"
)
IYF_ML_KEY = os.environ.get(
"API_KEY",
"+0PZ4Do44hDEPRIiMrZMBIz1A5OoaetZqMLWrHYwXoq78AgyG2mk+ZNjqynH8G25aR2TwP1bvgPWcnPYEVEWGw=="
)
IYF_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/b37ee96356a54c44a0cb7bc96390cce1/execute?api-version=2.0&details=true"
)
KRE_ML_KEY = os.environ.get(
"API_KEY",
"JP9KQOeG6nzgikEm3jZFB+SKVJAcwbDzBWLUcO5WsbDom2d2gr0//LlQoKOvmmwg5ZjAs+4wj/TKDupRVG8p3w=="
)
KRE_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/7778b0ee5a714a6292711988618e45ce/execute?api-version=2.0&details=true"
)
FTEC_ML_KEY = os.environ.get(
"API_KEY",
"2kupaA1PW5SkfRl49IeapOnLFgVFyA7QswqxJ/be9HF4hBJwx8wNkflplB8H4x5xW/Mit4knheqNSA1SQhtjnQ=="
)
FTEC_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/df47509c0f7b4c809a7e3098cabea7e8/execute?api-version=2.0&details=true"
)
FDN_ML_KEY = os.environ.get(
"API_KEY",
"kalqSeYcT03ac7+Y0yorrdSEvQ82s5q4pzONeLabw150VEHqzYkvPN55FbYbBpuytgJCnhflleJx1Wjqx9Y+0g=="
)
FDN_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/f9056118871c4016b64df3ff306ea6d0/execute?api-version=2.0&details=true"
)
RYU_ML_KEY = os.environ.get(
"API_KEY",
"x6sXZgP4Z60ihwBpuum5pk4B2n5nKkRMAA4i6G6BZ4Ju7z7LLNPdbkWfdStOburjAvRrLPN3F0jsyFQfwpaiOA=="
)
RYU_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/673eab634e9a43bab8bd916293f1b0fe/execute?api-version=2.0&details=true"
)
FUTY_ML_KEY = os.environ.get(
"API_KEY",
"rFkaSEiOg5LU1ODHMy9+ObnBmg/tdngaFgNosGCEKVQRMBIcr71ZKM5hd1iEBc62qO6qLoWrIzyy1ghXEgTheA=="
)
FUTY_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/e8d9b311444e43bab767e1b37d814e73/execute?api-version=2.0&details=true"
)
FHLC_ML_KEY = os.environ.get(
"API_KEY",
"FgtA+3/f3MwNUBbrdkOMvbKEoKk5uTxXk2bzVhiAMEbffGnL2TFj3loNFkeY7up7n6m5U9+WUmCVQS2RgWurGQ=="
)
FHLC_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/7bc24f3c6a724311aa2b9fe9aac3b072/execute?api-version=2.0&details=true"
)
XHE_ML_KEY = os.environ.get(
"API_KEY",
"pgZCaps937cjlFeCAORzoOKPrYvNkjnJBRrB78nnwly8kHJs5xa9oEaZ9eS9x2MIVAPf/pi/xZcdg1QUVkkJnQ=="
)
XHE_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/f2e083369a9b444e8ad87bdcac6de097/execute?api-version=2.0&details=true"
)
XLE_ML_KEY = os.environ.get(
"API_KEY",
"qUqVTIcPCI1GPOWJb0vomlJUAXsHIfmxRgzeUYV0XRkYMm4AoIOGMzzYiPrwgehYyJn9nkb8bFtnQO05cub/TQ=="
)
XLE_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/93c8c6280f4843738eb0f3ae63b1fbfc/execute?api-version=2.0&details=true"
)
PXI_ML_KEY = os.environ.get(
"API_KEY",
"jtEp8rzvtY3jyCoGs2swh/FGJ7iU5PhDhb54+9EIqfuckZEN7cjtC4hlOEQhNmIzAMi4VH7g7kZ3FyG8y75xXw=="
)
PXI_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/2b0d8f6af1024e90a9e62b297a1f9b75/execute?api-version=2.0&details=true"
)
FSTA_ML_KEY = os.environ.get(
"API_KEY",
"Vl6kPEZ4fUbZYIUl5uHUYPsjieZeg7AujcweePGQhGdOSZV0cpjtrVbcfqg/DeBjDwgF401sDUKPljQuAfWIcw=="
)
FSTA_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/36f73752487041209c2f85210bbdcb1b/execute?api-version=2.0&details=true"
)
IYK_ML_KEY = os.environ.get(
"API_KEY",
"4UNxbiK9mauO+/VRpMPSjxa7FR30M98Mkcl0qCE6waS+azVCsGCALIk9Dp8TF2bdeQYvByXpK25a5N7D7NKDpQ=="
)
IYK_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/555cc9684b404539b26e51bf9714c049/execute?api-version=2.0&details=true"
)
PDBC_ML_KEY = os.environ.get(
"API_KEY",
"iLpxmDXQr5AuCHA5fCcWcLNk0cDz+QzklttzS97v5C6cvjGK5if1zqFNEdskmUz6ggs7FQfPsK8DtY9HVWplAw=="
)
PDBC_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/3dc8e15a453f4109928b82948e1cf1ce/execute?api-version=2.0&details=true"
)
GSG_ML_KEY = os.environ.get(
"API_KEY",
"wmqwEYBSE3IqPIlFAXqXHAx/Pa1nTDQnsL6/ZB4mlpv6X7YIIOE2PpUzrmePhqVIIitdYVbaT/oHhmegOjN//Q=="
)
GSG_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/1e321f9a89834fdb84138589a28eda6e/execute?api-version=2.0&details=true"
)
MORT_ML_KEY = os.environ.get(
"API_KEY",
"XJg1jk5vbd59+u3uX+q0Nn+7u7kzA4FVN7QoTLaoiPyFz6EuGENXZjyejVRhjj5S5XPOyU1ARRKk0Gror0RNzQ=="
)
MORT_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/1ab49df3ef7d4b34ac35e1125bd69566/execute?api-version=2.0&details=true"
)
VNQ_ML_KEY = os.environ.get(
"API_KEY",
"szJppS8Xj/SyzRgmg44tqeGxSOVzRhV/1gMG5TU3l0s9xEmfH+1ZZ6SelSDUgd4i8cITqjRwYuKNK9gG569GUA=="
)
VNQ_URL = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1ebda07f5b83468fa934325b157c5759/services/9b0ca03dd5b94782854e6b2fc35db3b7/execute?api-version=2.0&details=true"
)
# Construct the HTTP request header
# HEADERS = {'Content-Type':'application/json', 'Authorization':('Bearer '+ API_KEY)}
VFH_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + VFH_ML_KEY)
}
VGT_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + VGT_ML_KEY)
}
YLCO_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + YLCO_ML_KEY)
}
IHI_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + IHI_ML_KEY)
}
IEO_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + IEO_ML_KEY)
}
VDC_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + VDC_ML_KEY)
}
DBC_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + DBC_ML_KEY)
}
FREL_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + FREL_ML_KEY)
}
AGG_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + AGG_ML_KEY)
}
IYF_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + IYF_ML_KEY)
}
KRE_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + KRE_ML_KEY)
}
FTEC_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + FTEC_ML_KEY)
}
FDN_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + FDN_ML_KEY)
}
RYU_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + RYU_ML_KEY)
}
FUTY_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + FUTY_ML_KEY)
}
FHLC_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + FHLC_ML_KEY)
}
XHE_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + XHE_ML_KEY)
}
XLE_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + XLE_ML_KEY)
}
PXI_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + PXI_ML_KEY)
}
FSTA_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + FSTA_ML_KEY)
}
IYK_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + IYK_ML_KEY)
}
PDBC_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + PDBC_ML_KEY)
}
GSG_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + GSG_ML_KEY)
}
MORT_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + MORT_ML_KEY)
}
VNQ_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + VNQ_ML_KEY)
}
# str.encode
# Formulate the request
#req = urllib.request.Request(URL, body, HEADERS)
vfh_req = urllib.request.Request(VFH_URL, body_list[0], VFH_HEADERS)
vgt_req = urllib.request.Request(VGT_URL, body_list[1], VGT_HEADERS)
ylco_req = urllib.request.Request(YLCO_URL, body_list[2], YLCO_HEADERS)
ihi_req = urllib.request.Request(IHI_URL, body_list[3], IHI_HEADERS)
ieo_req = urllib.request.Request(IEO_URL, body_list[4], IEO_HEADERS)
vdc_req = urllib.request.Request(VDC_URL, body_list[5], VDC_HEADERS)
dbc_req = urllib.request.Request(DBC_URL, body_list[6], DBC_HEADERS)
frel_req = urllib.request.Request(FREL_URL, body_list[7], FREL_HEADERS)
agg_req = urllib.request.Request(AGG_URL, body_list[8], AGG_HEADERS)
iyf_req = urllib.request.Request(IYF_URL, body_list[9], IYF_HEADERS)
kre_req = urllib.request.Request(KRE_URL, body_list[10], KRE_HEADERS)
ftec_req = urllib.request.Request(FTEC_URL, body_list[11],
FTEC_HEADERS)
fdn_req = urllib.request.Request(FDN_URL, body_list[12], FDN_HEADERS)
ryu_req = urllib.request.Request(RYU_URL, body_list[13], RYU_HEADERS)
futy_req = urllib.request.Request(FUTY_URL, body_list[14],
FUTY_HEADERS)
fhlc_req = urllib.request.Request(FHLC_URL, body_list[15],
FHLC_HEADERS)
xhe_req = urllib.request.Request(XHE_URL, body_list[16], XHE_HEADERS)
xle_req = urllib.request.Request(XLE_URL, body_list[17], XLE_HEADERS)
pxi_req = urllib.request.Request(PXI_URL, body_list[18], PXI_HEADERS)
fsta_req = urllib.request.Request(FSTA_URL, body_list[19],
FSTA_HEADERS)
iyk_req = urllib.request.Request(IYK_URL, body_list[20], IYK_HEADERS)
pdbc_req = urllib.request.Request(PDBC_URL, body_list[21],
PDBC_HEADERS)
gsg_req = urllib.request.Request(GSG_URL, body_list[22], GSG_HEADERS)
mort_req = urllib.request.Request(MORT_URL, body_list[23],
MORT_HEADERS)
vnq_req = urllib.request.Request(VNQ_URL, body_list[24], VNQ_HEADERS)
# Send this request to the AML service and render the results on page
try:
request_list = [
vfh_req, vgt_req, ylco_req, ihi_req, ieo_req, vdc_req, dbc_req,
frel_req, agg_req, iyf_req, kre_req, ftec_req, fdn_req,
ryu_req, futy_req, fhlc_req, xhe_req, xle_req, pxi_req,
fsta_req, iyk_req, pdbc_req, gsg_req, mort_req, vnq_req
]
# response = requests.post(VFH_URL, headers=HEADERS, data=body)
result_list = []
a = 0
for i in request_list:
response = urllib.request.urlopen(i)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = do_something_pretty(result)
result = json.dumps(result, indent=4, sort_keys=True)
result_list.append([etf_tickers[a], result])
a += 1
for i in result_list:
if i[0] == current_etf_global:
result = i[1]
a = 0
for i in weights_chosen:
a += i
if a > 100:
answer = "Your portfolio is overweighted (greater than 100). Please re-adjust on the previous pane."
elif a < 100:
answer = "Your portfolio is does not add up to 100%. Please return to the previous pane and re-weight if you so wish."
else:
answer = "Congratulations! Your portfolio equates 100%, and your weights are the following:"
if sum(weights_chosen[:1]) > 50:
risk_portfolio = "Low Risk and Safer Growth"
elif sum(weights_chosen[:1]) > 25:
risk_portfolio = "Medium Risk and Higher Growth Potential"
else:
risk_portfolio = "High Risk and Aggressive Growth/Loss Potential"
return render_template(
'final_result.html',
title=
'The following prediction was made for the return of your portfolio:',
result=result,
etfg=current_etf_global,
agg_result=result_list[8][1],
energyt=tickers_chosen[0],
techt=tickers_chosen[1],
utilt=tickers_chosen[2],
fint=tickers_chosen[3],
healtht=tickers_chosen[4],
constapt=tickers_chosen[5],
condisct=tickers_chosen[6],
answer=answer,
risk_answer=risk_portfolio,
etf_lbs=str(weights_chosen[0]) + str('%'),
bonds_lbs=str(weights_chosen[1]) + str('%'),
stock_lbs=str(sum(weights_chosen[2:])) + str('%'),
close=closing_price)
# An HTTP error
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('final_result.html',
title='There was an error',
result=result)
return render_template(
'result.html',
# form=form,
# title="Your portfolio:",
# etf_content = longbusinesssum_G,
# etfg = current_etf_global,
# tyg = three_yr_G,
# fyg = five_yr_G,
# yrg = ytd_return_G,
# tyagg_g = three_yr_agg_G,
# fyagg_g = five_yr_agg_G,
# yragg_g = ytd_return_agg_G,
# lbsagg_g = longbusinesssum_agg_G,
# thg = topholdings_G,
# th_realestate = topholdings_G[0]['realestate']['fmt'],
# th_consumer = topholdings_G[1]['consumer_cyclical']['fmt'],
# th_basic = topholdings_G[2]['basic_materials']['fmt'],
# th_consumerdef = topholdings_G[3]['consumer_defensive']['fmt'],
# th_tech = topholdings_G[4]['technology']['fmt'],
# th_communication = topholdings_G[5]['communication_services']['fmt'],
# th_financial = topholdings_G[6]['financial_services']['fmt'],
# th_utilities = topholdings_G[7]['utilities']['fmt'],
# th_industrials = topholdings_G[8]['industrials']['fmt'],
# th_energy = topholdings_G[9]['energy']['fmt'],
# th_health = topholdings_G[10]['healthcare']['fmt'],
# aaa_ratings = bondratings_bonds_G[2]['aaa']['fmt'],
# aa_ratings = bondratings_bonds_G[1]['aa']['fmt'],
# a_ratings = bondratings_bonds_G[3]['a']['fmt'],
# bbb_ratings = bondratings_bonds_G[6]['bbb']['fmt'],
#iframe = 'https://www.nasdaq.com/market-activity/stocks'
)
def home():
"""Renders the home page which is the CNS of the web app currently, nothing pretty."""
# Configuration for RDS instance
mode = "append"
jdbc_url = "jdbc:postgresql://momentum-db.cgk0xpvhfuev.us-east-2.rds.amazonaws.com:5432/postgres"
config = {
"user": "******",
"password": "******",
"driver": "org.postgresql.Driver"
}
form = SubmissionForm(request.form)
#variable formation based on questionnaire submissions
age = form.age.data
email = form.email.data
income_level = form.income_level.data
sector_preference = form.sector_preference.data
citizenship = form.citizenship.data
education = form.education.data
experience_years = form.experience_years.data
periodicals = form.periodicals.data
aspirations = form.aspirations.data
diversification = form.diversification.data
brokerage_acct = form.brokerage_acct.data
interested_in_learning = form.interested_in_learning.data
scenario_1 = form.scenario_1.data
scenario_2 = form.scenario_2.data
port_diversified = form.port_diversified.data
safest_asset = form.safest_asset.data
income_drawing = form.income_drawing.data
fin_info = form.fin_info.data
return_expectations = form.return_expectations.data
normal_expectations = form.normal_expectations.data
poor_expectations = form.poor_expectations.data
three_yr_attitude = form.three_yr_attitude.data
three_month_attitude = form.three_yr_attitude.data
datetime_1 = datetime.now()
# iterate and combine to string MultipleEntryField
combined_periodicals = ""
combined_aspirations = ""
while len(periodicals) > 0:
combined_periodicals = periodicals[0] + ", " + combined_periodicals
periodicals = np.delete(periodicals, 0)
while len(aspirations) > 0:
combined_aspirations = aspirations[0] + ", " + combined_aspirations
aspirations = np.delete(aspirations, 0)
#form dataframe from submission data
d = {
'age': age,
'email': email,
'income_level': income_level,
'sector_preference': sector_preference,
'citizenship': citizenship,
'education': education,
'experience_years': experience_years,
'periodicals': combined_periodicals,
'aspirations': combined_aspirations,
'diversification': diversification,
'brokerage_acct': brokerage_acct,
'interested_in_learning': interested_in_learning,
'scenario_1': scenario_1,
'scenario_2': scenario_2,
'port_diversified': port_diversified,
'safest_asset': safest_asset,
'income_drawing': income_drawing,
'fin_info': fin_info,
'return_expectations': return_expectations,
'normal_expectations': normal_expectations,
'poor_expectations': poor_expectations,
'three_yr_attitude': three_yr_attitude,
'three_month_attitude': three_month_attitude,
'datetime': datetime_1
}
submission_df = pd.DataFrame(data=d, index=[0])
# -------------------------------------------------------------------------------------------
# from sqlalchemy import create_engine
engine = create_engine(
'postgresql://*****:*****@momentum-db.cgk0xpvhfuev.us-east-2.rds.amazonaws.com:5432/postgres'
)
#use pandas to load questionnaire df into database
submission_df.to_sql(name='questionnaire', con=engine, if_exists='append')
# Form has been submitted
if request.method == 'POST':
# -------------------------------------------------------------------------------------------
# #summed column -jsean
summed_df = submission_df.drop(
columns=['email', 'sector_preference', 'periodicals'])
summed_df['sum'] = summed_df.sum(axis=1)
# #Return summed value as string - JOANA
# # there will always be only one item right?
current_user_sum = summed_df['sum'].item()
print(current_user_sum)
# #current_user_agg = submission_df.loc[submission_df['email'] == email,'sum'].item()
# #lower end = riskier | higher = safer
# # The ETF recommendations made is not financial advice. ETF listing is pulled from yahoo Finanace equity screener based upon filters and parameters.
# # The following parameters were used to determine whether the ETF was high, medium, and low risk. Parameter: morningstar performance rating overall is 4-5 stars
# #High Risk: Morningstar Risk Rating Overrrall (5 stars)
etf_dictionary = {
'Financial Sector': [('IYF', 'High'), ('VFH', 'Medium'),
('KRE', 'Low')],
'Technology Sector': [('FTEC', 'High'), ('VGT', 'Medium'),
('FDN', 'Low')],
'Utilities': [('RYU', 'High'), ('YLCO', 'Medium'),
('FUTY', 'Low')],
'Healthcare': [('FHLC', 'High'), ('IHI', 'Medium'),
('XHE', 'Low')],
'Energy': [('XLE', 'High'), ('IEO', 'Medium'), ('PXI', 'Low')],
'Consumer Staples': [('FSTA', 'High'), ('VDC', 'Medium'),
('IYK', 'Low')],
'Commodities': [('PDBC', 'High'), ('DBC', 'Medium'),
('GSG', 'Low')],
'Real Estate': [('MORT', 'High'), ('FREL', 'Medium'),
('VNQ', 'Low')],
'Government Bonds': [('AGG', 'High'), ('AGG', 'Medium'),
('AGG', 'Low')]
}
def chosen_etfs(user_agg, sector_chosen):
for key, value in etf_dictionary.items():
if sector_chosen == key:
if user_agg <= 35:
return value[0][0]
if user_agg <= 55:
return value[1][0]
else:
return value[2][0]
current_etf = chosen_etfs(current_user_sum, sector_preference)
global current_etf_global
current_etf_global = current_etf
# current_etf = chosen_etfs(65, 'Financial Sector')
#yahoo api call
import requests
import json
#ETF: fiveYrAvgReturnPct, threeyearaverage,keystatics-ytdReturn,
#ETF: topholdings-sectorweightings, assetprofile-longBusinessSummary
#BONDS: ALSO APPLICABLE
url = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/get-detail"
querystring = {"region": "US", "lang": "en", "symbol": current_etf}
querystring2 = {"region": "US", "lang": "en", "symbol": "AGG"}
headers = {
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com",
'x-rapidapi-key':
"ca07005c56mshafe5b7a7c516a9dp1b90e2jsn1e7c85e6edd1"
}
response = requests.request("GET",
url,
headers=headers,
params=querystring)
response_agg = requests.request("GET",
url,
headers=headers,
params=querystring2)
response_json_etf2 = response.json()
response_agg = response_agg.json()
three_yr = response_json_etf2['defaultKeyStatistics'][
'threeYearAverageReturn']['fmt']
five_yr = response_json_etf2['defaultKeyStatistics'][
'fiveYearAverageReturn']['fmt']
ytd_return = response_json_etf2['defaultKeyStatistics']['ytdReturn'][
'fmt']
# topholdings = response_json_etf2['topHoldings']['sectorWeightings']
longbusinesssum = response_json_etf2['assetProfile'][
'longBusinessSummary']
#AGG information
three_yr_agg = response_agg['defaultKeyStatistics'][
'threeYearAverageReturn']['fmt']
five_yr_agg = response_agg['defaultKeyStatistics'][
'fiveYearAverageReturn']['fmt']
ytd_return_agg = response_agg['defaultKeyStatistics']['ytdReturn'][
'fmt']
# longbusinesssum_agg = response_agg['assetProfile']['longBusinessSummary']
# bondratings_bonds_agg = response_agg['topHoldings']['bondRatings']
# global three_yr_G, five_yr_G, ytd_return_G, topholdings_G,longbusinesssum_G, bondratings_bonds_G, three_yr_agg_G, five_yr_agg_G, ytd_return_agg_G, topholdings_agg_G, longbusinesssum_agg_G
# three_yr_G = three_yr
# five_yr_G = five_yr
# ytd_return_G = ytd_return
# topholdings_G = topholdings
# longbusinesssum_G = longbusinesssum
# bondratings_bonds_G = bondratings_bonds_agg
# three_yr_agg_G = three_yr_agg
# five_yr_agg_G = five_yr_agg
# ytd_return_agg_G = ytd_return_agg
# longbusinesssum_agg_G = longbusinesssum_agg
# Send this request to the AML service and render the results on page
try:
return redirect(url_for('secondresult'))
# An HTTP error
except urllib.error.HTTPError as err:
# result="The request failed with status code: " + str(err.code)
return render_template(
'result.html',
title='There was an error',
)
#print(err)
# Just serve up the input form
return render_template(
'form.html',
form=form,
title='Run App',
year="2020",
message='Demonstrating a website using Azure ML Api')
def football():
fball_key = os.environ.get(
'API_KEY',
"mjYhDRm7ZyrlHWvAE726VK30whTi+2zu4uxLfLRVCVcryfCXgtwFiFOlfXVmNfW0Gch3QmF1YSIAd9xXod5YtA=="
)
fball_url = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/1bd82c355c1447d591afec4715a4b045/services/7a19aada667c40a1851c13ab20d7df38/execute?api-version=2.0&format=swagger"
)
F_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + fball_key)
}
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST':
# Plug in the data into a dictionary object
# - data from the input form
data = {
"Inputs": {
"input1": [{
'Position': form.position.data.upper(),
'School': form.school.data,
'Height': form.height.data,
'Weight': form.weight.data,
'40yd': "0",
'Vertical': form.vert.data,
'Bench': form.bench.data,
'Broad Jump': "0",
'3Cone': "0",
'Shuttle': "0",
'Drafted': "",
}],
},
"GlobalParameters": {}
}
body = str.encode(json.dumps(data))
req = urllib.request.Request(fball_url, body, F_HEADERS)
# Send this request to the AML service and render the results on page
try:
# response = requests.post(URL, headers=HEADERS, data=body)
response = urllib.request.urlopen(req)
print(response)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = fball_pretty(result)
# result = json.dumps(result, indent=4, sort_keys=True)
return render_template(
'fballResult.html',
title=
"This is the result from AzureML running our Football Draft Prediction",
year=datetime.now().year,
result=result)
# An HTTP error
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('fballResult.html',
title='There was an error',
year=datetime.now().year,
result=result)
print(err)
return render_template('football.html',
form=form,
title='Football Draft Predictions',
year=datetime.now().year,
message='Will you be drafted?')
def basketball():
bball_key = os.environ.get(
'API_KEY',
"J/YazlXkBmLjs+GGrAst0pfD6hxd3l5FQCQ6I7g4pMs/3Dv898Xf/Ra6qjm3sQ72utOXCyC3enYO/1rT1cQytg=="
)
bball_url = os.environ.get(
'URL',
"https://ussouthcentral.services.azureml.net/workspaces/0f1aebe5b0944e9c831c80acc2aa6ee7/services/4c90f9399d64471bb0dc293c2c00b5e0/execute?api-version=2.0&format=swagger"
)
B_HEADERS = {
'Content-Type': 'application/json',
'Authorization': ('Bearer ' + bball_key)
}
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST':
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
data = {
"Inputs": {
"input1": [{
'player': "",
'position': form.position.data.upper(),
'team': "",
'gp': form.gp.data,
'mpg': "1",
'fgm': form.fgm.data,
'fga': "1",
'fg_pct': "1",
'threepm': form.three.data,
'threepa': "1",
'three_pct': "1",
'ftm': "1",
'fta': "1",
'ft_pct': "1",
'tov': "1",
'pf': "1",
'orb': "1",
'drb': "1",
'rpg': "1",
'apg': "1",
'spg': "1",
'bpg': "1",
'ppg': "1",
'pick': "1",
'conference': "",
'college': "",
'School': "",
'Country': "",
'State': "",
'Latitude': "1",
'Longitude': "1",
'Revenue_Men': "",
'Expenses_Men': "",
}],
},
"GlobalParameters": {}
}
body = str.encode(json.dumps(data))
req = urllib.request.Request(bball_url, body, B_HEADERS)
# Send this request to the AML service and render the results on page
try:
# response = requests.post(URL, headers=HEADERS, data=body)
response = urllib.request.urlopen(req)
#print(response)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = bball_pretty(result)
# result = json.dumps(result, indent=4, sort_keys=True)
return render_template(
'bballResult.html',
title=
"This is the result from AzureML running our Basketball Conference Prediction:",
year=datetime.now().year,
result=result)
# An HTTP error
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('bballResult.html',
title='There was an error',
year=datetime.now().year,
result=result)
#print(err)
return render_template('basketball.html',
form=form,
title='Run App',
year=datetime.now().year,
message='Will you be drafted?')
def home():
"""Renders the home page which is the CNS of the web app currently, nothing pretty."""
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST' and form.validate():
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
data = {
"Inputs": {
"input1": {
"ColumnNames": [
"Title",
"Category",
"Text"
],
"Values": [ [
form.title.data,
form.category.data,
form.text.data.lower()
]
]
}
},
"GlobalParameters": {}
}
# Serialize the input data into json string
body = str.encode(json.dumps(data))
# Formulate the request
req = urllib.request.Request(URL, body, HEADERS)
# Send this request to the AML service and render the results on page
try:
# response = requests.post(URL, headers=HEADERS, data=body)
response = urllib.request.urlopen(req)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = do_something_pretty(result)
# result = json.dumps(result, indent=4, sort_keys=True)
return render_template(
'result.html',
title="From your friendly AML experiment's Web Service:",
result=result)
# An HTTP error
except Exception as err:
result = json.loads(str(err.code))
return render_template(
'result.html',
title='There was an error',
result=result)
# Just serve up the input form
return render_template(
'form.html',
form=form,
title='Run App',
year=datetime.now().year,
message='Input form to gain insights into a company using Azure Machine Learning')
def home():
"""Renders the home page which is the CNS of the web app currently, nothing pretty."""
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST' and form.validate():
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
data = {
"Inputs": {
"input1": {
"ColumnNames": [
"Open",
"High",
"Low",
"Close",
"Volume",
"T3_Vol_Diff",
"T3_Close_Diff",
"T3_Open_Diff",
"T2_Vol_Diff",
"T2_Close_Diff",
"T2_Open_Diff",
"T1_Vol_Diff",
"T1_Close_Diff",
"T1_Open_Diff",
"Prior_Day_Vert_Delta_Ratio",
"Retracement_Signal",
"Prior_Day_Derivative",
"T+1_Close",
],
"Values": [[
form.Open.data, form.High.data, form.Low.data,
form.Close.data, form.Volume.data,
form.T3_Vol_Diff.data, form.T3_Close_Diff.data,
form.T3_Open_Diff.data, form.T2_Vol_Diff.data,
form.T2_Close_Diff.data, form.T2_Open_Diff.data,
form.T1_Vol_Diff.data, form.T1_Close_Diff.data,
form.T1_Open_Diff.data,
form.Prior_Day_Vert_Delta_Ratio.data,
form.Retracement_Signal.data,
form.Prior_Day_Derivative.data, ""
]]
}
},
"GlobalParameters": {}
}
# Serialize the input data into json string
body = str.encode(json.dumps(data))
# str.encode
# Formulate the request
#req = urllib.request.Request(URL, body, HEADERS)
req = urllib.request.Request(Bayesian_URL, body, HEADERS)
# Send this request to the AML service and render the results on page
try:
# response = requests.post(URL, headers=HEADERS, data=body)
response = urllib.request.urlopen(req)
#print(response)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = do_something_pretty(result)
# result = json.dumps(result, indent=4, sort_keys=True)
return render_template(
'result.html',
title=
"This is the result from AzureML running our example T+1 Prediction:",
result=result)
# An HTTP error
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('result.html',
title='There was an error',
result=result)
#print(err)
# Just serve up the input form
return render_template(
'form.html',
form=form,
title='Run App',
year=datetime.now().year,
message='Demonstrating a website using Azure ML Api')
def predictresponse():
"""Renders the home page which is the CNS of the web app currently, nothing pretty."""
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST' and form.validate():
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
data = {
"Inputs": {
"input1": {
"ColumnNames": [
"Customer Lifetime Value", "Response",
"EmploymentStatus", "Income", "Monthly Premium Auto",
"Months Since Last Claim",
"Months Since Policy Inception", "Total Claim Amount"
],
"Values": [[
form.lifetime.data.lower(),
"",
# form.employment.data.lower(),
dict(Employment_choices).get(form.employment.data),
form.income.data.lower(),
form.premium.data.lower(),
form.lastclaim.data.lower(),
form.inception.data.lower(),
form.claimamount.data.lower()
]]
}
},
"GlobalParameters": {}
}
# Serialize the input data into json string
body = str.encode(json.dumps(data))
# Formulate the request
req = urllib.request.Request(ML_URL, body, HEADERS)
# Send this request to the AML service and render the results on page
try:
response = urllib.request.urlopen(req)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result = do_something_pretty(result)
return render_template(
'result.html',
title="Insurance Customer Marketing Response Prediction:",
result=result)
# An HTTP error
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('result.html',
title='There was an error',
result=result)
# Just serve up the input form
return render_template(
'form.html',
form=form,
title='Run App',
year=datetime.now().year,
message='Demonstrating a website using Azure ML Api')
def ml():
form = SubmissionForm(request.form)
print(request.form)
reqform = request.form.to_dict()
if request.method == 'POST' and form.validate():
selectedcountry = reqform["country"]
print(selectedcountry)
if selectedcountry == 'US':
MLKEY = MLKEY_US
MLURL = MLURL_US
MLHEADERS = MLHEADERS_US
elif selectedcountry == 'Canada':
MLKEY = MLKEY_CAN
MLURL = MLURL_CAN
MLHEADERS = MLHEADERS_CAN
elif selectedcountry == 'Australia':
MLKEY = MLKEY_AUS
MLURL = MLURL_AUS
MLHEADERS = MLHEADERS_AUS
elif selectedcountry == 'Argentina':
MLKEY = MLKEY_ARG
MLURL = MLURL_ARG
MLHEADERS = MLHEADERS_ARG
elif selectedcountry == 'France':
MLKEY = MLKEY_FRA
MLURL = MLURL_FRA
MLHEADERS = MLHEADERS_FRA
elif selectedcountry == 'Italy':
MLKEY = MLKEY_ITA
MLURL = MLURL_ITA
MLHEADERS = MLHEADERS_ITA
elif selectedcountry == 'Spain':
MLKEY = MLKEY_SPA
MLURL = MLURL_SPA
MLHEADERS = MLHEADERS_SPA
else:
MLKEY = MLKEY_US
MLURL = MLURL_US
MLHEADERS = MLHEADERS_US
data = {
"Inputs": {
"input1": {
"ColumnNames": [
"Column 0", "points_range", "deslen", "price_range",
"variety", "province"
],
"Values": [[
"2", "85-89", reqform["deslen"],
reqform["price_range"], reqform["variety"],
reqform["province"]
]]
}
},
"GlobalParameters": {}
}
body = str.encode(json.dumps(data))
req = urllib.request.Request(MLURL, body, MLHEADERS)
try:
response = urllib.request.urlopen(req)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
result1 = format(result)
return render_template('resultML.html',
title=" ",
icountry=reqform["country"],
ideslen=reqform["deslen"],
ipricerg=reqform["price_range"],
ivariety=reqform["variety"],
iprovince=reqform["province"],
result=result,
result1=result1)
except urllib.error.HTTPError as err:
result = "The request failed with status code: " + str(err.code)
return render_template('resultML.html',
title='There was an error',
result=result)
return render_template(
'formML.html',
form=form,
title='Run App',
year=datetime.now().year,
message='The Wine Rating Model generated via the Azure ML Api')
def home():
"""Renders the home price predictor page."""
form = SubmissionForm(request.form)
# Form has been submitted
if request.method == 'POST' :
# Plug in the data into a dictionary object
# - data from the input form
# - text data must be converted to lowercase
# data = {
# "Inputs": {
# "input1": {
# "ColumnNames": ["gender", "age", "size", "weight"],
# "Values": [ [
# 0,
# 1,
# form.title.data.lower(),
# 0
# ]
# ]
# }
# },
# "GlobalParameters": {}
# }
data = {
"Inputs": {
"input1":
{
"ColumnNames": ["RegionID", "Stock_Market", "Mortgage_Rate", "Monthly_Supply_Of_Homes", "Median_Price_Range"],
"Values": [ [ form.RegionID.data.lower(), form.Stock_Market.data.lower(), form.Mortgage_Rate.data.lower(), form.Monthly_Supply_Of_Homes.data.lower(),0
]
]
}
},
"GlobalParameters": {}
}
# Serialize the input data into json string
body = str.encode(json.dumps(data))
# Formulate the request
#req = urllib.request.Request(URL, body, HEADERS)
req = urllib.request.Request(HOUSING_URL, body, HEADERS)
# Send this request to the AML service and render the results on page
try:
# response = requests.post(URL, headers=HEADERS, data=body)
response = urllib.request.urlopen(req)
respdata = response.read()
result = json.loads(str(respdata, 'utf-8'))
print(result)
result = do_something_pretty(result)
# result = json.dumps(result, indent=4, sort_keys=True)
return render_template(
'result.html',
title="This is the result from AzureML running our Real Estate Value Prediction:",
result=result)
# An HTTP error
except urllib.error.HTTPError as err:
result="The request failed with status code: " + str(err.code)
return render_template(
'result.html',
title='There was an error',
result=result)
#print(err)
# Just serve up the input form
return render_template(
'form.html',
form=form,
title='Run App',
year=datetime.now().year,
message='Demonstrating a website using Azure ML Api')