def get_weather(city):
rapid = RapidConnect("default-application_5a68be4ce4b09c6b06da6c08",
"551285ee-2f7d-45e2-8471-bc4aaae024ed")
result = rapid.call('YahooWeatherAPI', 'getWeatherForecast', {
'location': city,
'filter': ['item.condition,item.forecast']
})
return result
def post(self):
url = self.get_body_argument('url')
rapid = RapidConnect("default-application_59dda5c4e4b04627fc65932d",
"50647dfc-4872-43d2-a233-87ea46cc35e7")
result = rapid.call(
'MicrosoftComputerVision', 'describeImage', {
'subscriptionKey': 'e67939b51a3f4cd3b11da510fbed6d0b',
'image': url,
'region': 'westcentralus'
})
data = json.loads(result) #it is a dictionary
tags = data.get('description').get('tags') #iterate this
caption = data.get('description').get('captions')[0].get('text')
confidence = data.get('description').get('captions')[0].get(
'confidence') * 100
self.render_template("microsoft-recognition.html", {
'tags': tags,
'caption': caption,
'confidence': confidence
})
def maps():
form = AirportsForm(request.form)
departure = None
rate = 1
mapcost = ''
mapduration = ''
mapeasiness = ''
msg = ''
did = ''
with orm.db_session:
logs = Log.select(lambda p: p.country == 'AIR').order_by(
orm.desc(Log.id))[:]
previous = orm.select(c.departure
for c in Direction).prefetch(Airport)[:]
if logs[0].dt + timedelta(days=1) > datetime.now():
delta = (logs[0].dt + timedelta(days=1) - datetime.now()).seconds
hours = (delta // 3600)
minutes = (delta - hours * 3600) // 60
msg = 'New search will be available in %s hour(s) %s minutes.' % (
hours, minutes)
else:
airports = Airport.select().order_by(Airport.country)[:]
form.airports.choices = [
(x.id, '%s - %s, %s' % (x.country, x.code, x.city))
for x in airports
]
if request.method == 'GET':
did = request.values.get('did', '')
currency = 'USD'
if did != '':
cr = CurrencyRates()
with orm.db_session:
departure = Airport.get(id=int(did))
#directions=Direction.select(lambda p: p.departure==departure).prefetch(Airport)[:]
directions = Direction.select_by_sql(
'select d0.* from Directions d0 join Airports a0 on a0.id=d0.destination where d0.cost=(select min(d.cost) from Directions d join Airports a on a.id=d.destination where a.country=a0.country and d.departure=d0.departure) and departure=%s'
% departure.id)
try:
rate = cr.get_rate(directions[0].currency, 'USD')
except RatesNotAvailableError:
if directions[0].currency == 'KZT':
rate = 0.0032
else:
rate = 1
currency = directions[0].currency
locations = [d.destination.iso3 for d in directions]
texts = [
'%s - %s, %s' % (d.destination.country, d.destination.code,
d.destination.city) for d in directions
]
vals = [int(d.cost * rate) for d in directions]
costperm = [
int(d.cost * rate * d.duration / float(d.distance))
for d in directions
]
times = [d.duration // 60 for d in directions]
mapcost = makemap(
'Flight cost from %s to some countries.' % departure.code,
'Cost, %s' % currency, locations, vals, texts)
mapduration = makemap(
'Total flight duration, best from 3 most cheap flight.',
'Duration, hours', locations, times, texts)
mapeasiness = makemap('Easiness (cost * duration / distance).',
'Easiness, $*h/mi', locations, costperm,
texts)
elif form.validate_on_submit() and msg == '':
with orm.db_session:
dtstr = (datetime.now() + timedelta(days=30)).strftime('%Y-%m-%d')
departure = Airport.get(id=int(request.form.get('airports')))
iteration = orm.max(p.iteration for p in Direction
if p.departure == departure)
if iteration == None:
iteration = 0
if iteration < 3:
iteration += 1
else:
iteration = 1
dirs = Direction.select(lambda p: p.departure == departure and p.
iteration == iteration).delete(bulk=True)
portstodo = Airport.select(lambda p: p.iteration == iteration and p
.code != departure.code)[:]
rapid = RapidConnect("flight-167611",
"12489be9-a1b2-442f-9f98-77609a7d6a9d")
for x in portstodo:
processdestination(rapid, departure, x, dtstr, iteration)
log = Log(keyword='none', dt=datetime.now(), country='AIR')
return redirect('%s?did=%s' % (url_for('maps'), departure.id))
return render_template('parsers/maps.jade',
form=form,
msg=msg,
departure=departure,
previous=previous,
mapcost=mapcost,
mapduration=mapduration,
mapeasiness=mapeasiness)
from rapidconnect import RapidConnect
import sys
city = sys.argv[1]
rapid = RapidConnect("default-application_59bbda2be4b0b0cacf7c7995",
"db4efe57-20a5-4f2d-966c-b292a14a8695")
result = rapid.call('YahooWeatherAPI', 'getWeatherForecast', {'location': city})
desc = result["query"]["results"]["channel"]["item"]["condition"]["text"]
temp = result["query"]["results"]["channel"]["item"]["condition"]["temp"]
print("It's " + desc + " in " + city.capitalize() + ", with a temperature of " + temp + " Farenheit.")
sys.stdout.flush()
from rapidconnect import RapidConnect
rapid = RapidConnect('Emotions', '24aa499b-e5a4-491e-90ce-9bb0f8d75c86');
import base64
import json
import emotion
from collections import Counter
# result = rapid.call('MicrosoftEmotionAPI', 'getEmotionRecognition', {
# 'subscriptionKey': '90febbecca1c462f871ea1d8e349d76a',
# 'image': 'http://i.imgur.com/shDtPNc.jpg'
# })
def faceArea(dct):
area = dct['faceRectangle']['width']*dct['faceRectangle']['height']
return area
# maxFace = max(result, key=faceArea)
def getEmotions(face):
return rankedEmotions
def getMaxEmotion(face):
emotionsList = dictToList(face)
maxEmo = max(emotionsList, key=lambda x: x[1])
return maxEmo
def dictToList(dct):
keys = dct['scores'].keys()
scores = dct['scores']
tupList = []
for key in keys:
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 07:10:48 2018
@author: FluxMonitor
Description: Uses Dark Sky API to request historical weather data
"""
import pandas as pd
import numpy as np
from rapidconnect import RapidConnect
import datetime as dt
import os
username = os.getlogin()
rapid = RapidConnect("default-application_5b72b75fe4b02799e7f62892", "4df23935-0f28-4bd5-9eb1-0e8d7b021f78")
today = pd.to_datetime(dt.datetime.now().strftime('%y-%m-%d 00:00:00'),yearfirst = True)
today = today
date_range = pd.date_range('2018-04-17 00:00:00',today)
date_range_hourly = pd.date_range('2018-04-17 00:00:00','2018-04-20 00:00:00',freq = '1h')
precip = pd.DataFrame(index = date_range,columns = ['date','precip_rate_mm_hour','precip_type'])
daily_precip = pd.DataFrame(index = date_range,columns = ['precip_avg_rate_mm_hour','precip_type'])
hourly_precip = pd.DataFrame(index = date_range_hourly, columns = ['precip_rate_mm_hour','precip_type'])
for i in date_range:
print(i)
for d in date_range:
result = rapid.call('Darksky', 'getTimeMachineRequest', {
'apiKey': 'c230f720cf53566b8f33657451e694d3',
'time': d,
'coordinates': '42.5315, -72.1899',
# -*- coding: utf-8 -*-
from rapidconnect import RapidConnect
rapid = RapidConnect("testa", "a3787239-bb1e-4fa4-85c3-a423fa6af51f")
result = rapid.call(
"GoogleTranslate", "translate", {
'string': 'שלום',
'sourceLanguage': 'he',
'targetLanguage': 'en',
'apiKey': 'XXXX'
})
print result
def on_join():
print("joined!")
def on_message(message):
print(message)
def on_close():
print("Closed!")
def on_error(message):
print("error:")
def dawgstuff():
r2 = str(request.get_data())
print(r2)
pagina = "https://www.allmenus.com/custom-results/-/ca/san-jose/" + r2[1:]
print(pagina)
pg = urllib2.urlopen(pagina)
sopa = BeautifulSoup(pg, "html.parser")
thelinks = sopa.findAll("a", {"data-masterlist-id": re.compile(r".*")})
print(thelinks)
da_str = str(thelinks[0])
#print(re.search(r'\"\.+\"',da_str).group(0))
start = da_str.index("/ca/")
end = da_str.index("/menu/") + 6
da_str = da_str[start:end]
print(da_str)
quote_page = "https://www.allmenus.com" + da_str
#https://www.allmenus.com/ca/san-jose/216536-mcdonalds/menu/
print(quote_page)
page = urllib2.urlopen(quote_page)
soup = BeautifulSoup(page, 'html.parser')
titles = soup.find_all("span", attrs={'class': 'item-title'})
print(titles)
ingredients = soup.find_all("p", attrs={'class': 'description'})
print(ingredients)
counter = 0
class total_values:
fat = 65
carbs = 300
protein = 50
sodium = 2.4
def function(self):
pass
class ideal_values:
fat = total_values.fat / 3
carbs = total_values.carbs / 3
protein = total_values.protein / 3
for i in range(0, len(titles)):
print(i)
print(titles[i].get_text())
print(ingredients[i].get_text())
stripped_menu.append(titles[i].get_text() + ": " +
ingredients[i].get_text())
#print(stripped_menu)
parsed_titles = []
for title in titles:
parsed_titles.append(title)
print(parsed_titles)
parsed_titles = remove_duplicates(parsed_titles)
print(parsed_titles)
rapid = RapidConnect("d_562854dae4b049ee93f17049",
"1d71d14a-e7c0-478c-9635-f76b62490adb")
for i in range(0, len(parsed_titles)):
food = parsed_titles[i]
print(food)
counter += 1
if counter > 5:
break
result = rapid.call(
'Nutritionix', 'getFoodsNutrients', {
'applicationSecret': '8594e772f2b653ab072e04d6a5feb6e0',
'foodDescription': food,
'applicationId': '444f4a33'
})
print(result)
if "We couldn't match any of your foods" in str(result):
pass
else:
info = result[0]["foods"][0]
print(info["food_name"])
#print("Sodium: " + str(info["nf_sodium"]))
print("fat: " + str(info["nf_total_fat"]))
print("carbohydrates: " + str(info["nf_total_carbohydrate"]))
print("protein: " + str(info["nf_protein"]))
#sodium = info["nf_sodium"] aayush says something abuot iterations in presentaion
fat = info["nf_total_fat"]
carbs = info["nf_total_carbohydrate"]
protein = info["nf_protein"]
rating = (abs(0.33 - fat / ideal_values.fat) +
abs(0.33 - carbs / ideal_values.carbs) +
abs(0.33 - protein / ideal_values.protein))
ratings.append([stripped_menu[i], rating])
print(ratings)
sorted_ratings = sorted(ratings, key=lambda x: x[1])
final_stuff = sorted_ratings
return str(final_stuff)
clarifai_model = clarifai.models.get('general-v1.3')
tagger = PerceptronTagger()
total_word_counts = {}
for s in brown.sents():
s = map(lambda k: k.lower(), s)
for w in set(s):
if w not in total_word_counts:
total_word_counts[w] = 0
total_word_counts[w] += 1
brown_sent_count = len(brown.sents())
rapid = RapidConnect('cmpre', 'a0c4b418-1531-4c31-abbe-24df50f8a74b')
@app.route('/convert_html', methods=['POST'])
def convert_html():
"""
Given an HTML page as a string, apply all of our comprehension-improving
transformations, and return the new HTML.
"""
data = request.get_json(force=True)
html = data['page']
host = data['host']
hostrel = data['hostrel']
count, html, summary = convert(html, host, hostrel)
print(summary)
return json.dumps({'count': count, 'html': html, 'summary': summary})
from rapidconnect import RapidConnect
from os import path
from pprint import pprint
import json
app_dir = path.dirname(__file__)
image_dir = path.join(app_dir, 'images')
API_KEY = "########"
# rapidAPI Initialize
rapid = RapidConnect('WasteZero', API_KEY)
# Opening text files and spliting them into words
with open('compost.txt', 'r+') as f:
compost = f.readlines()
compost = [x.lower() for x in compost]
compost = [x.strip() for x in compost]
with open('recyclables.txt', 'r+') as f:
recycable = f.readlines()
recycable = [x.lower() for x in recycable]
recycable = [x.strip() for x in recycable]
with open('landfill.txt', 'r+') as f:
landfill = f.readlines()
landfill = [x.lower() for x in landfill]
landfill = [x.strip() for x in landfill]
def sortImage(url):
"""
import json, os
from rapidconnect import RapidConnect
rapid = RapidConnect("default-application_5a191a72e4b0d45349f766fe",
"bc65de32-d2c1-4679-94d9-2683183f4521")
if __name__ == '__main__':
downloaded = open('../latest_songs_2/downloaded_songs.json', 'w+')
d_json = {}
artists_json = rapid.call('LastFM', 'getTopArtistsChart',
{'apiKey': '9c57fb1f3de3132bda349ef57801988d'})
#artists_json = json.loads(artists);
full_list = artists_json['artists']['artist']
for item in full_list:
temp = []
artist_name = item['name'].encode('ascii', 'ignore')
top_50_songs = rapid.call(
'LastFM', 'getTopArtistTracks', {
'apiKey': '9c57fb1f3de3132bda349ef57801988d',
'artist': artist_name,
'limit': '5',
'page': '5'
})
all_tracks = top_50_songs['toptracks']['track']
from urllib.request import urlopen
from pydub import AudioSegment
from pydub.playback import play
# mp3file = urlopen("https://cs9-19v4.userapi.com/p1/d722df69efb593.mp3")
# with open('./test.mp3', 'wb') as output:
# output.write(mp3file.read())
#
# song = AudioSegment.from_mp3("./test.mp3")
# play(song)
from rapidconnect import RapidConnect
rapid = RapidConnect('robotshanti_5a4f3a79e4b09c6b28427e54',
'f9379269-6068-433b-a882-e22a4655ebd8')
result = rapid.call(
'SpotifyPublicAPI', 'searchTracks', {
'accessToken': 'robotshanti_5a4f3a79e4b09c6b28427e54',
'query': 'I am not Afraid'
})
print(result)
from rapidconnect import RapidConnect
from flask import request
rapid = RapidConnect('foodpool', '521832dd-f936-404c-a121-9ee6781cabea')
from flask import Flask
from flask import g
import cf_deployment_tracker
import sqlite3
import os
import uuid, os
from flask import Flask, jsonify, request
#Square Modules
import squareconnect
from squareconnect.rest import ApiException
from squareconnect.apis.transaction_api import TransactionApi
access_token = 'sandbox-sq0atb-vwQUCVDYaJrgubwxf5VnkQ'
location_id = 'CBASEMe2x6MwoifEPrKa8Toz5gk'
# Emit Bluemix deployment event
cf_deployment_tracker.track()
app = Flask(__name__)
# On Bluemix, get the port number from the environment variable VCAP_APP_PORT
# When running this app on the local machine, default the port to 8080
port = int(os.getenv('VCAP_APP_PORT', 8080))
DATABASE = './database.db'
from rapidconnect import RapidConnect
rapid = RapidConnect('openwerx-greypill',
'b6b91fb4-9c29-43ee-8ffe-342f8bca0dcf')
def gtDetectLang(text):
return rapid.call('GoogleTranslate', 'detectLanguage', {
'apiKey': 'AIzaSyBv2QEVIH-ZNORgAJkTTaDmx_f_vMM5RYE',
'string': text
})
def gtAutoTranslate(text):
return rapid.call(
'GoogleTranslate', 'translateAutomatic', {
'apiKey': 'AIzaSyBv2QEVIH-ZNORgAJkTTaDmx_f_vMM5RYE',
'string': text,
'targetLanguage': 'en'
})
def gtTranslate(text, lang):
return rapid.call(
'GoogleTranslate', 'translate', {
'apiKey': 'AIzaSyBv2QEVIH-ZNORgAJkTTaDmx_f_vMM5RYE',
'string': text,
'targetLanguage': 'en',
'sourceLanguage': lang
})
from rapidconnect import RapidConnect
rapid = RapidConnect('whatscookin_5bdcbabde4b09efa5fbcded9',
'04f4dd28-d4ff-4c6d-9f6b-6d8bd607659b')
from rapidconnect import RapidConnect
rapid = RapidConnect("cs421", "c10b4173-cbf3-47c1-a35c-385dc88905c9")
result = rapid.call(
'YelpAPI', 'getBusinesses', {
'accessToken':
'_4Zt6rM00ZWHNhuIjmN7vGittFp5PoII9pZjidLmuCc2EAy2jTqPYCV2gnBN1c_SuxFMLkg4hnxL0FVz5Rz8G7jmfopiae2hrw-4VqiA6LX_lK3jOU5LkkFBWUL6WHYx',
'term': '',
'location': '842 w taylor street',
'latitude': '',
'longitude': '',
'radius': '200',
'categories': 'food,vietnamese',
'locale': '',
'limit': '',
'offset': '',
'sortBy': '',
'price': '',
'openNow': '',
'openAt': '',
'attributes': ''
})
print result
from rapidconnect import RapidConnect
rapid = RapidConnect('calhacks3', 'f58ef2cf-6cfa-4cd0-a653-beb778f317e1')
result = rapid.call(
'MicrosoftEmotionAPI', 'getEmotionRecognition', {
'subscriptionKey': 'eb8317cc011540c9b52cef81219dee60',
'image': 'http://i.imgur.com/gUHZXOh.jpg'
})
print(type(result))
print(result)