def init(db):
upcomingReleases.getUpcomingReleases()
iter = utils.read_movies('newReleases.json')
collection = []
print "Pre-processing data"
start = time.time()
# Pre-process movies to classify
file = open('classify.json','w')
for movie in iter:
if 'imdb_id' in movie:
movie['rating'] = 5.0
movie['rating_count'] = 100
collection.append(movie)
# Write movie data
data = StringIO()
json.dump(movie, data)
file.write(data.getvalue())
file.write(",\n")
file.close()
end = time.time()
print "Finished in %.3f seconds\n" % (end-start)
iter = utils.read_movies('mv.json')
for movie in iter:
collection.append(movie)
for movie in collection:
if "poster" not in movie:
movie['poster'] = '/static/images/caverlee.png'
if "plot_simple" not in movie:
movie['plot_simple'] = ' '
if "director" not in movie:
movie['directors'] = ['Unknown']
print "Indexing movie titles"
start = time.time()
indexer = moviesearch.MovieSearch(db)
indexer.index_movies(collection)
end = time.time()
print "Finished in %.3f seconds\n" % (end-start)
for movie in iter:
collection.append(movie)
for movie in collection:
if "poster" not in movie:
movie['poster'] = '/static/images/caverlee.png'
if "plot_simple" not in movie:
movie['plot_simple'] = ' '
if "director" not in movie:
movie['directors'] = ['Unknown']
print "Indexing movie titles"
start = time.time()
indexer = moviesearch.MovieSearch(db)
indexer.index_movies(collection)
end = time.time()
print "Finished in %.3f seconds\n" % (end-start)
if __name__=="__main__":
print "In Main"
_db = utils.connect_db('termather',remove_existing=True)
init(_db)
_predictor = predictor.Predictor(_db)
_predictor.train_classifier(utils.read_movies('mv.json'))
_predictor.main(utils.read_movies('classify.json'))
_searcher = moviesearch.MovieSearch(_db)
bottle.run(host=settings['http_host'],
port=settings['http_port'])
