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main.py
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main.py
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import base64
import random
import nn
import numpy as np
import pickle
from io import BytesIO
from flask import Flask, request, render_template, flash, redirect, send_from_directory
from flask_login import LoginManager, login_user, UserMixin, login_required, logout_user
from flask_wtf import FlaskForm
from wtforms import StringField, PasswordField, SubmitField
from wtforms.validators import DataRequired
from matplotlib.figure import Figure
app = Flask(__name__)
login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = "/login"
app.secret_key = b'_6#e4F"S2Z8z\n\xec]/'
song = None
# Index - Authenticated users only
@app.route('/')
@login_required
def index():
return render_template("index.html", generate_song=generate_song, song=song)
# Visualization - Authenticated users only
@app.route('/visualization')
@login_required
def visualization():
return render_template("visualization.html", get_figure=get_figure, get_model_summary=get_model_summary)
# Login
@app.route('/login')
def login():
form = LoginForm()
return render_template("login.html", form=form)
# Login POST method
@app.route('/login', methods=['POST'])
def login_post():
global user
# recreate default user - bugfix
user = User()
username = request.form.get('username')
password = request.form.get('password')
if username == user.username and password == user.password:
login_user(user)
return redirect("/")
else:
flash("Unable to log in")
return redirect("/login")
# Check if user is authenticated
@login_manager.user_loader
def load_user(user_id):
global user
user = User() # bugfix user
if int(user_id) == user.id:
return user
# Return None of user is not authenticated
return None
# Log out
@app.route("/logout")
@login_required
def logout():
logout_user()
return redirect("/login")
# Generate song
@app.route('/generate_song')
@app.route('/generate_song/<path:transpose>/<path:time_sig>/<path:length>')
@login_required
def generate_song(transpose=0, time_sig=44, length=32):
if request.args.get('length', ''):
length = request.args.get('length', '')
if request.args.get('time_sig', ''):
time_sig = request.args.get('time_sig', '')
if request.args.get('transpose', ''):
transpose = request.args.get('transpose', '')
nn.generate_song(int(transpose), int(time_sig), int(length))
global song
song = True
return redirect("/")
# Get file without using cache
@app.route('/media/<path:filename>')
@login_required
def get_media(filename):
return send_from_directory('static/', filename, cache_timeout=0)
# Get a matplotlib figure for visualization. Embed in html.
def get_figure(name):
# Generate figure
if name is "note_frequency":
fig = Figure(figsize=(14, 5))
fig.suptitle("Note Frequency in Key of C", fontsize=18)
ax = fig.subplots()
with open(nn.MODEL_DIR + '/notes', 'rb') as filepath:
notes = np.array(pickle.load(filepath))
unique, counts = np.unique(notes, return_counts=True)
ax.bar(unique, counts)
elif name is "key_frequency":
fig = Figure()
fig.suptitle("Key Signature Frequency")
ax = fig.subplots()
ax.plot([1, 2])
elif name is "midi_data":
# Generate Multiple Correspondence Analysis (MCA) graph
X, mca = nn.generate_mca_graph()
ax = mca.plot_coordinates(
X=X,
ax=None,
figsize=(20, 8),
show_row_points=True,
row_points_size=10,
show_row_labels=False,
show_column_points=True,
column_points_size=30,
show_column_labels=True,
legend_n_cols=1
)
fig = ax.get_figure()
else:
fig = Figure()
fig.suptitle("Generic Title")
ax = fig.subplots()
ax.plot([1, 2])
# Save to temporary buffer
buf = BytesIO()
fig.savefig(buf, format="png")
# Embed result in html output
data = base64.b64encode(buf.getbuffer()).decode("ascii")
return f"<img src='data:image/png;base64,{data}' style='max-width:100%'/>"
# Get summary of Neural Network model to display to the user
def get_model_summary():
# summary = []
# # create model
# model = nn.create_model(nn.nn_input, nn.n_vocab)
# model.summary(print_fn=lambda x: summary.append(x))
# res = ''
# for line in summary:
# res += line + '<br>'
# # Return hard-coded model details to save memory
res = 'Model: "sequential"<br>_________________________________________________________________<br>Layer (type) Output Shape Param # <br>=================================================================<br>lstm (LSTM) (None, 8, 512) 1052672 <br>_________________________________________________________________<br>lstm_1 (LSTM) (None, 8, 512) 2099200 <br>_________________________________________________________________<br>lstm_2 (LSTM) (None, 512) 2099200 <br>_________________________________________________________________<br>batch_normalization (BatchNo (None, 512) 2048 <br>_________________________________________________________________<br>dropout (Dropout) (None, 512) 0 <br>_________________________________________________________________<br>dense (Dense) (None, 256) 131328 <br>_________________________________________________________________<br>activation (Activation) (None, 256) 0 <br>_________________________________________________________________<br>batch_normalization_1 (Batch (None, 256) 1024 <br>_________________________________________________________________<br>dropout_1 (Dropout) (None, 256) 0 <br>_________________________________________________________________<br>dense_1 (Dense) (None, 48) 12336 <br>_________________________________________________________________<br>activation_1 (Activation) (None, 48) 0 <br>=================================================================<br>Total params: 5,397,808<br>Trainable params: 5,396,272<br>Non-trainable params: 1,536<br>_________________________________________________________________<br>'
return res
# Class to create login form
class LoginForm(FlaskForm):
username = StringField('', [DataRequired()])
password = PasswordField('', [DataRequired()])
submit = SubmitField('Sign in')
# Class to create default user
class User(UserMixin):
username = "user"
password = "user"
random.seed(27)
id = random.randint(1000000, 9999999) # Generate unique ID number at app startup
# init app
if __name__ == "__main__":
# create default user
user = User()
# Run app
app.run(debug=True)