starting to make front end changes

server/predict.py

@@ -22,7 +22,7 @@ from magenta.models.melody_rnn import melody_rnn_model
 from magenta.models.melody_rnn import melody_rnn_sequence_generator
 from magenta.protobuf import generator_pb2
 from magenta.protobuf import music_pb2
-
+from usingMusicNN import predictmood
 
 import os
 import time
@@ -73,3 +73,11 @@ def generate_midi(midi_data, total_seconds=10):
     magenta.music.midi_io.sequence_proto_to_midi_file(generated_sequence, output.name)
     output.seek(0)
     return output
+
+
+def predictmood(midi_data):
+    '''
+    Attempt to predict the mood of the music in the midi file
+    '''
+
+    return predictmood(midi_data)

server/savedModels/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "musicModel"
+all_model_checkpoint_paths: "musicModel"

server/server.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 # 
 
-from predict import generate_midi
+from predict import generate_midi, predictmood
 import os
 from flask import send_file, request
 import pretty_midi
@@ -35,11 +35,19 @@ def predict():
     now = time.time()
     values = json.loads(request.data)
     midi_data = pretty_midi.PrettyMIDI(StringIO(''.join(chr(v) for v in values)))
+    print values
     duration = float(request.args.get('duration'))
     ret_midi = generate_midi(midi_data, duration)
     return send_file(ret_midi, attachment_filename='return.mid', 
         mimetype='audio/midi', as_attachment=True)
 
+@app.route('/getmood', methods=['POST'])
+def getmood():
+    values = json.loads(request.data)
+    midi_data = pretty_midi.PrettyMIDI(StringIO(''.join(chr(v) for v in values)))
+    ret_file = predictmood(midi_data)
+    print ret_file
+    return send_file(ret_file, attachment_filename='return.json', as_attachment=True)
 
 @app.route('/', methods=['GET', 'POST'])
 def index():

server/usingMusicNN.py

@@ -0,0 +1,130 @@
+'''
+Thomas Matlak Avi Vajpeyi, Avery Rapson
+CS 310 Final Project
+
+Takes example midi file and prints if its happy or sad
+
+Usage:
+python [/path/to/midi/file.mid]
+'''
+
+import tensorflow as tf
+import json
+from mido import MidiFile
+import numpy as np
+import tempfile
+
+
+midiFile =  "testMidi.mid"
+saveFile = "savedModels/musicModel"
+
+pianoSize = 128
+
+
+n_nodes_hl1 = 1500
+n_nodes_hl2 = 1500
+n_nodes_hl3 = 1500
+
+n_classes = 2
+hm_data = 2000000
+
+batch_size = 32
+hm_epochs = 10
+
+x = tf.placeholder('float')
+y = tf.placeholder('float')
+
+
+current_epoch = tf.Variable(1)
+
+hidden_1_layer = {'f_fum':n_nodes_hl1,
+                  'weight':tf.Variable(tf.random_normal([pianoSize, n_nodes_hl1])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
+
+hidden_2_layer = {'f_fum':n_nodes_hl2,
+                  'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}
+
+hidden_3_layer = {'f_fum':n_nodes_hl3,
+                  'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}
+
+output_layer = {'f_fum':None,
+                'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
+                'bias':tf.Variable(tf.random_normal([n_classes])),}
+
+
+
+def neural_network_model(data):
+    ####INPUT LAYER (HIDDEN LAYER 1)
+    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
+    l1 = tf.nn.relu(l1)
+    ####HIDDEN LAYER 2
+    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
+    l2 = tf.nn.relu(l2)
+    ####HIDDEN LAYER 3
+    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
+    l3 = tf.nn.relu(l3)
+    ####OUTPUT LAYER
+    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']
+    return output
+
+
+
+
+
+
+#
+def predictmood(input_midi_file):
+    output = tempfile.NamedTemporaryFile()
+    prediction = neural_network_model(x)
+    # with open('musicModel.pickle','rb') as f:
+    #     lexicon = pickle.load(f)
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        saver = tf.train.import_meta_graph('savedModels/musicModel.meta')
+        saver.restore(sess, 'savedModels/musicModel')
+        #### CONVERT THE MIDI TO NOTES AND FEATURES (without [0,1])
+        #### need it in the [0 112 1 1 0 0 0 ....] format
+        mid = MidiFile(input_midi_file)
+        notes = []
+        time = float(0)
+        prev = float(0)
+        for msg in mid:
+            if time >= 10:
+                break
+            ### this time is in seconds, not ticks
+            time += msg.time
+            if not msg.is_meta:
+                ### only interested in piano channel
+                if msg.channel == 0:
+                    if msg.type == 'note_on':
+                        # note in vector form to train on
+                        note = msg.bytes()
+                        # only interested in the note #and velocity. note message is in the form of [type, note, velocity]
+                        note = note[1] #:3]
+                        # note.append(time - prev)
+                        prev = time
+                        notes.append(note)
+        noteCount = np.zeros(pianoSize)
+        for note in notes:
+            noteCount[note] += 1
+        noteCount = list(noteCount)
+        #features = np.array(list(features))
+        # pos: [1,0] , argmax: 0
+        # neg: [0,1] , argmax: 1
+        result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:[noteCount]}),1)))
+        if result[0] == 0:
+            output.write("Happy")
+        elif result[0] == 1:
+            output.write("Sad")
+        # with open('mood.txt', 'w') as outfile:
+        #     mood_dict = dict()
+        #     if result[0] == 0:
+        #         mood_dict = {'Mood': "Happy"}
+        #     elif result[0] == 1:
+        #         mood_dict = {'Mood': "Sad"}
+        #     json.dump(mood_dict, outfile)
+    output.seek(0) #resets the pointer to the data of the file to the start
+    return output
+