def __init__(self):
self.al_object = alchemy()
self.tw_object = twitter()
self.meta_data = np.empty([25258,6],dtype='|S1000')
with open('metadata.csv', 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
i = 0
for row in spamreader:
self.meta_data[i, :] = row
i += 1
self.meta_data_concepts = []
iter = 10 #self.meta_data[:,0].size
for i in range(0, iter):
s = " ";
seq = (self.meta_data[i][0], self.meta_data[i][1], self.meta_data[i][2], self.meta_data[i][3], self.meta_data[i][4], self.meta_data[i][5])
self.meta_data_concepts.append(self.al_object.find_keywords(s.join(seq)))
},
{
'title':u'World in White',
'synopsis':u'Surrounded by dark forces who suppress and ridicule him, the Hero slowly blossoms into a mature figure who ultimately gets riches, a kingdom, and the perfect mate.',
'imgurl':u'http://uploads.neatorama.com/images/posts/95/58/58095/1360112719-0.jpg',
'trailerurl':u'https://www.youtube.com/watch?v=a2MnKebNlRo',
'genre':u'classic,thriller'
}
]
},
]
meta = {
'location': u'London, UK',
'weather': weather.getWeather(2882, 'London'),
'trends': twitter().get_trends()
}
def get_lists(recommendation):
ts = int(time.time())
datapoint = np.array([weekday(ts), hour_of_day(ts), 1])
#print datapoint
#datapoint = np.array([6,10,1])
list_of_rec = recommendation.get_kmeans_recommendations(datapoint)
return jsonify({'lists': mapping.programs_to_data(list_of_rec, recommendation.get_meta_data())})
def get_trending_concepts(recommendation):
list_of_rec = recommendation.get_trend_recommendations()
return jsonify({'lists': mapping.programs_to_data(list_of_rec, recommendation.get_meta_data())})
def metadata():
