def homepage():
utils.connect()
if request.method == "GET":
return render_template("musicboxhome.html");
else:
global current_user
button = str(request.form["button"])
if button == "Login":
user = request.form.get("login-or-register")
current_user = utils.add_or_view_user(user)
session["user"]=current_user
songs = utils.get_songs(current_user)
return redirect("/"+current_user)
def play_music_in_dir(self, dir_path, n_songs=1):
songs = get_songs(dir_path)
rn.shuffle(songs)
if len(songs) >= n_songs:
print("About to play: {}".format(songs[:n_songs]))
for i in range(n_songs):
os.system('mplayer \"{}\"'.format(songs[i]))
else:
print("About to play: {}".format(songs))
for song in songs:
os.system('mplayer \"{}\"'.format(songs[i]))
def login(username):
global current_user
global current_artist
global current_artistID
utils.connect()
if request.method == "GET":
songs = utils.get_songs(current_user)
current_user=session["user"]
return render_template("login.html",user=current_user,songs=songs)
else:
button = str(request.form["button"])
if button == "Close account":
utils.remove_user(current_user)
return redirect("/")
elif button == "Search":
current_artist = request.form.get("search")
current_artistID = str(musicservices.getID(
musicservices.getArtistInfo(
utils.curate(current_artist))))
session["artist"]=current_artist
session["aID"]=current_artistID
return redirect("/"+current_user+"/"+current_artistID)
username = sys.argv[1]
URL = sys.argv[2]
playlist_name = sys.argv[3]
else:
print("Usage : %s username" % (sys.argv[0], ))
sys.exit()
token = util.prompt_for_user_token(username,
SCOPE,
client_id=CLIENT_ID,
client_secret=CLIENT_ID_SECRET,
redirect_uri='http://localhost/')
#create spotipy agent
sp = spotipy.Spotify(auth=token)
#get artist and song names from youtube link
artists, songs = get_songs(URL)
print('got youtube links...')
#create a list of spotify song urls
song_urls = []
for i in range(len(songs)):
try:
results = sp.search(q='track:' + songs[i] + ",", type='track,artist')
song = None
for item in results['tracks']['items']:
if item['artists'][0]['name'] == artists[i]:
song = item
break
if song is None:
print('couldnt find song: ' + songs[i] + " by: " + arists[i])
song_urls.append(song['external_urls']['spotify'])
except Exception as e:
print("Split_50:", len(slices_50))
print("Split_100:", len(slices_100), "\n")
pp = pprint.PrettyPrinter(indent=2)
sp = utils.get_spotify_client()
songs_dict = defaultdict(list)
for i, sslice in enumerate(slices_50):
print("\033[K\033[1000DGetting Song Metadata: " + str(i + 1) + "/" +
str(len(slices_50)) + " Dropped: " +
str((i * 50) - len(songs_dict)),
flush=True,
end='')
songs_dict = utils.get_songs(sp, sslice, songs_dict)
l1_total = len(songs_dict)
print("\nFinished stage 1 with", l1_total, "out of", len(songs), "songs")
for i, sslice in enumerate(slices_100):
print("\033[K\033[1000DGetting Song Attributes: " + str(i + 1) + "/" +
str(len(slices_100)) + " Dropped: " +
str(l1_total - len(songs_dict)),
flush=True,
end='')
songs_dict = utils.get_song_info(sp, sslice, songs_dict)
print("\nDropped",
len(songs) - len(songs_dict), "out of", len(songs), "songs")
#print("Dropped", 300 - len(songs_dict), "out of", 300, "songs")
print("missing_reason info")
pp.pprint(dict(utils.get_missing()))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 6 20:53:36 2018
@author: Arpit
"""
from utils import save_preds, get_songs, split_data, to_label
from data_processing import get_image_data
from model import Model
# get names of all the songs
songs = get_songs()
# split them in training and valid sets according to given percentage
songs_train, songs_valid = split_data(songs, 0.85)
# get actual spectrogram(2d np.array) data for the songs
X_train, Y_train = get_image_data('train', songs_train)
X_valid, Y_valid = get_image_data('valid', songs_valid)
# get names and spectrogram data for the final testing set(which is to be uploaded)
X_test, keys = get_image_data('test')
model = Model(False)
model.train(X_train, Y_train, X_valid, Y_valid, 5000)
preds = model.predict(X_test)
preds = [to_label(pred) for pred in preds]
save_preds(keys, preds, 'predictions.csv')
label = [0] * 10
label[label_index(song.split('.')[0])] = 1
cnt = int(size / input_size)
for i in range(cnt):
X.append(abs(scipy.fft(raw[input_size * i:input_size * (i + 1)])))
Y.append(label)
X = np.array(X)
Y = np.array(Y)
return X, Y
# gets all songs' names
songs = get_songs()
#splits them into training and validation sets
training_songs, validation_songs = split_data(np.array(songs), 0.85)
# gets fourier transformed data for given songs
X, Y = get_song_data(training_songs)
#fits and transforms a normalizer
norm = Normalizer(norm='l1').fit(X)
X = norm.transform(X)
# gets fourier transformed data of validation set and transforms according to training set
X_valid, Y_valid = get_song_data(validation_songs)
X_valid = norm.transform(X_valid)
