def info():
session['token_info'], authorized = get_token(session)
session.modified = True
if not authorized:
flash("Please Login with your Spotify Account")
return redirect('/')
sp = spotipy.Spotify(auth=session.get('token_info').get('access_token'))
user = get_user(sp)
songs = get_all_top_tracks(sp)
artists = get_all_top_artists(sp)
audio_features = get_audio_features(sp, songs['uri'])
recommended_artists = get_recommended_artists(
sp, artists['artists'][0]['url'][32:])
recommended_songs = get_recommendations(sp, artists, songs['uri'],
artists['genres'][0],
audio_features['values'])
return render_template('info.html',
songs=songs,
artists=artists,
similar=recommended_artists,
tracks=recommended_songs,
audio=audio_features,
user=user)
def home_agent(user):
airline, flight_num, departure_airport, departure_city, departure_time, arrival_airport, arrival_city, \
arrival_time, price, status, airplane_id = utils.retrieve_get_args_for_flight_query(request)
airlines_and_cities = utils.get_all_airlines_and_cities(mysqltool=mt)
if airline == flight_num == departure_airport == departure_city == departure_time == arrival_airport \
== arrival_city == arrival_time == price == status == airplane_id is None:
flight_list = utils.get_recommendations(mt, user=user, how_many=10)
if len(flight_list) == 0:
flight_list = utils.get_popular_flights(mysqltool=mt)
flight_list = utils.flight_list_add_check_ticket_exists(
mysqltool=mt, flight_list=flight_list)
my_json = {
"msg": "ok",
"user": user[:-2],
"role": "Agent",
'searched': 0,
"flights": utils.flight_list_to_json_list(flight_list)
}
my_json.update(airlines_and_cities)
return render_template('home.html', data=my_json)
departure_airport = utils.airport_city_to_airport_name_list(
mt, session['user'], departure_city, departure_airport)
arrival_airport = utils.airport_city_to_airport_name_list(
mt, session['user'], arrival_city, arrival_airport)
if departure_airport == False or arrival_airport == False:
my_json = {
"msg": "ok",
"user": user[:-2],
"role": "Agent",
"flights": [],
'searched': 0,
}
my_json.update(airlines_and_cities)
return render_template('home.html', data=my_json)
attribute = [
'airline_name', 'flight_num', 'departure_airport', 'departure_time',
'arrival_airport', 'arrival_time'
]
value = [
airline, flight_num, departure_airport, departure_time,
arrival_airport, arrival_time
]
result = mt.agent_query(user=session['user'],
table='flight',
attribute=attribute,
value=value)
result = utils.flight_list_add_check_ticket_exists(mysqltool=mt,
flight_list=result)
my_json = {
"msg": "ok",
'user': user[:-2],
'role': 'Agent',
'searched': 1,
'flights': utils.flight_list_to_json_list(result)
}
my_json.update(airlines_and_cities)
return render_template('home.html', data=my_json)
import utils
import pickle
import settings
token = utils.get_token(settings.CLIENT_ID, settings.CLIENT_SECRET)
genres = utils.get_genres(token)
discovered_artists = {}
print("Discovering artists...")
for genre in genres:
res = utils.get_recommendations(seed_tracks=[],
seed_artists=[],
seed_genres=[genre],
token=token)
for track in res["tracks"]:
artists = track["artists"]
for artist in artists:
if artist["id"] not in discovered_artists.keys():
discovered_artists[artist["id"]] = {}
print("genre: %s, total artists: %d" % (genre, len(discovered_artists)))
print("Enough discovered. Now will get info about them.")
artists = {}
buffer = []
it = 0
for key in discovered_artists.keys():
it += 1
if len(buffer) < 49:
buffer.append(key)
else:
buffer.append(key)
depth = int(raw_input("Enter the maximum depth of the tree (0 for no limit): "))
start = timer()
for index in xrange(len(users_db)):
X_train, Y_train, X_test, Y_test = utils.extract_data(users_db[index], items_db, 70)
dataset = {'X': X_train, 'Y': Y_train}
classes = utils.get_classes(dataset)
features = range(len(X_train[0]))
root = DT(dataset, classes, features, 0, depth)
Y_pred = utils.classify(root, X_test)
for k in xrange(1, K+1):
if k <= len(Y_test):
top_K_indices = utils.get_recommendations(Y_pred, k)
precision, recall = utils.compute_metrics(Y_pred, Y_test, top_K_indices)
precision_dict[k].append(precision)
recall_dict[k].append(recall)
MAE = utils.calc_MAE(Y_pred, Y_test)
RMSE = utils.calc_RMSE(Y_pred, Y_test)
accu = utils.accuracy(Y_pred, Y_test)
MAE_arr.append(MAE)
RMSE_arr.append(RMSE)
accuracy_arr.append(accu)
print "User: {} - Test accuracy: {}".format(index+1, accu)
print "\nTime taken: {} sec".format(timer() - start)
print 'Average MAE: {}'.format(sum(MAE_arr)/float(len(MAE_arr)))
keywords_similarity,
overview_similarity,
user_embedding_similarity,
]
sim_weights = [
cast_weight,
director_weight,
keywords_weight,
overview_weight,
user_embedding_weight,
]
sim_mat = generate_weighted_similarity_matrix(arrays=sim_matrices,
weights=sim_weights)
recommendations = get_recommendations(
films=data,
titles=liked_films,
similarity_matrix=sim_mat,
top_n=config.POSTERS_PER_ROW * config.NUM_POSTER_ROWS,
)
filtered_films = data[data["title"].isin(recommendations)]
recommendation_order = CategoricalDtype(recommendations, ordered=True)
filtered_films["title"] = filtered_films["title"].astype(
recommendation_order)
filtered_films = filtered_films.sort_values("title")
display_film_posters(
streamlit=st,
data=filtered_films,
num_rows=config.NUM_POSTER_ROWS,
posters_per_row=config.POSTERS_PER_ROW,
)