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# Creating the class Mobile
class Mobile:
    def __init__(self, serviceProvider, mobileNumber, dataUsed, paymentMethod):
        self.serviceProvider = serviceProvider
        self.mobileNumber = mobileNumber
        self.dataUsed = dataUsed
        self.paymentMethod = paymentMethod

# Creating the class Bill
class Bill:    

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# Creating the class Apartment
class Apartment:
    def __init__(self, flatNumber, ownerName, billAmount):
        self.flatNumber = flatNumber
        self.ownerName = ownerName
        self.billAmount = billAmount

# Creating the class Apartment_Demo

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offset = 0 # Time
output_notes = []

for pattern in prediction_output:
    
    # if the pattern is a chord
    if ('+' in pattern) or pattern.isdigit():
        notes_in_chord = pattern.split('+')
        temp_notes = []
        for current_note in notes_in_chord:

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sequence_length = 100
network_input = []

for i in range(len(notes) - sequence_length):
    seq_in = notes[i : i+sequence_length] # contains 100 values
    network_input.append([ele_to_int[ch] for ch in seq_in])
    
# Any random start index
start = np.random.randint(len(network_input) - 1)

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from keras.models import Sequential, load_model
from keras.layers import *
from keras.callbacks import ModelCheckpoint, EarlyStopping

model = Sequential()
model.add( LSTM(units=512,
               input_shape = (normalised_network_input.shape[1], normalised_network_input.shape[2]),
               return_sequences = True) )
model.add( Dropout(0.3) )
model.add( LSTM(512, return_sequences=True) )

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# No. of examples
n_patterns = len(network_input)
print(n_patterns)

# Desired shape for LSTM
network_input = np.reshape(network_input, (n_patterns, sequence_length, 1))
print(network_input.shape)

normalised_network_input = network_input/float(n_vocab)

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# Hoe many elements LSTM input should consider
sequence_length = 100

# All unique classes
pitchnames = sorted(set(notes))

# Mapping between ele to int value
ele_to_int = dict( (ele, num) for num, ele in enumerate(pitchnames) )
network_input = []
network_output = []

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n_vocab = len(set(notes))

print("Total notes- ", len(notes))
print("Unique notes- ",  n_vocab)

#output: Total notes-  60498
#         Unique notes-  359

print(notes[100:200])

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notes = []

for file in glob.glob("midi_songs/*.mid"):
    midi = converter.parse(file) # Convert file into stream.Score Object
    
    print("parsing %s"%file)
    
    elements_to_parse = midi.flat.notes
    
    

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notes_demo = []

for ele in elements_to_parse:
    # If the element is a Note,  then store it's pitch
    if isinstance(ele, note.Note):
        notes_demo.append(str(ele.pitch))
    
    # If the element is a Chord, split each note of chord and join them with +
    elif isinstance(ele, chord.Chord):
        notes_demo.append("+".join(str(n) for n in ele.normalOrder))