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diff --git a/MC/markov_chain.pickle b/MC/markov_chain.pickle
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diff --git a/MC/markov_chain.py b/MC/markov_chain.py
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index 0000000000000000000000000000000000000000..5a4c87d0b7f05e283af2798f58ccd3e4cc33966e
--- /dev/null
+++ b/MC/markov_chain.py
@@ -0,0 +1,246 @@
+
+import os,glob
+from matplotlib.pyplot import pie
+from mido import MidiFile
+import datetime
+import numpy as np
+import pandas as pd
+import subprocess
+from music21 import *
+from music21 import converter
+from mido import Message, MidiFile, MidiTrack, MetaMessage
+
+#number of notes to be used for prediction
+window = 3
+
+#num of notes to generate
+#TODO: change this to accept values according to user
+num_notes = 100
+
+#midi ticks per quarter note, indicates tempo of track
+quarter_note_ticks = 480
+
+#accepted note durations: ranges from 16th note to whole dotted notes
+accepeted_lengths = [0.25,0.375,0.5,0.75,1,1.5,2.0,3.0,4.0]
+#Finds all absolute paths in directory
+#https://stackoverflow.com/questions/9816816/get-absolute-paths-of-all-files-in-a-directory
+def abs_paths(dir):
+    for dir_path,_,filenames in os.walk(dir):
+        for f in filenames:
+            yield os.path.abspath(os.path.join(dir_path, f))
+def pitch_to_int(nameWithOctave):
+    # letter names with corresponding values
+    letter_dict = {'C':0,'D':2,'E':4,'F':5,'G':7,'A':9,'B':11}
+    # parse characters from strings
+    chars = list(nameWithOctave)
+    # convert octave number to corresponding midi value
+    octave = 12*(int(chars[-1])+1)
+    # select value from letter_dict using first character
+    note = letter_dict[chars[0]]
+    # set accidental value
+    accidental = 0
+    # does accidental exist?
+    if not len(chars)==2:
+        # increase (sharp) or decrease (flat) value by one
+        accidental = 1 if chars[1]=='#' else -1
+    # return sum of these numbers, middle C(4) == 60
+    return octave + note + accidental
+def get_pngs(path):
+	filelist=os.listdir(path)
+	for fichier in filelist[:]: # filelist[:] makes a copy of filelist.
+		if not(fichier.endswith(".png")):
+			filelist.remove(fichier)
+	newlist = [path+'/'+x for x in filelist] #making it cwd
+	return newlist
+def generate_notes(csv_file):
+    df_notes = pd.read_csv(csv_file)
+    print(df_notes.shape)
+    # define arrays for generated notes and durations
+    gen_notes = []
+    gen_durations = []
+    # define note and duration feature columns based on names
+    features = df_notes.columns[:-2]
+    note_features = [s for s in features if "note" in s]
+    duration_features = [s for s in features if "duration" in s]
+    # define target columns
+    note_target = df_notes.columns[-2]
+    duration_target = df_notes.columns[-1]
+
+    # sample random row from dataframe and define start notes and durations
+    initial_sample = df_notes.sample()
+    start_notes = list(initial_sample[note_features].values[0])
+    start_durations = list(initial_sample[duration_features].values[0])
+    # append starting notes and durations to gen arrays
+    for note in start_notes:
+        gen_notes.append(int(note))
+    for duration in start_durations:
+        gen_durations.append(duration)
+
+    for i in range(num_notes) :
+        rows = df_notes
+        for i in range(window-1):
+            rows = rows.loc[df_notes[note_features[i]] == start_notes[i]]
+            rows = rows.loc[df_notes[duration_features[i]]== start_durations[i]]
+    
+        #This gives the same effect as probability.
+        # We effectively sample from a list which might have more than 1 C note, Hence increasing its probability
+        #Sometime, The start_notes and durations could be selected in such a way that we cannot generate any further notes uptill num_notes,
+        #This means there maybe some combinations of notes such as 76,68 which are not there in the dataset and hence cannot be sampled.
+        #In such cases, the only way about it would be to reset the start notes, because we cannot sample from an empty row 
+        #So here we check if any rows which we ta
+        if len(rows):
+            next_sample = rows.sample()
+            next_note = next_sample[note_target].values[0]
+            next_duration = next_sample[duration_target].values[0]
+            gen_notes.append(int(next_note))
+            gen_durations.append(next_duration)
+
+            start_notes.pop()
+            start_durations.pop()
+
+            start_notes.append(next_note)
+            start_durations.append(next_duration)
+        else:
+            #Received empty row
+            # print("Exiting!!!!!!")
+            #restarting again to get new start notes
+            return [],[]
+           
+        # print(rows[note_target].value_counts(normalize=True))
+        # print(rows[duration_target].value_counts(normalize=True))
+
+    return gen_notes, gen_durations
+
+#MAIN FUNCTION
+def main_markov(time_sign):
+    command = "rm -r MC/gen_songs_midi/*"
+    subprocess.Popen(command,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE).communicate()
+    # https://stackoverflow.com/questions/49462107/how-can-i-get-all-piano-parts-from-a-music21-score
+    if not os.path.exists('tracks'):
+        os.mkdir('tracks')
+        os.mkdir('tracks/3_4')
+        os.mkdir('tracks/4_4')
+        os.mkdir('tracks/6_8')
+        os.mkdir('tracks/2_2')
+        os.mkdir('tracks/2_4')
+        i = 0
+        #Parse midi files into tracks folder
+
+        
+        for path in abs_paths('data'):
+            print(path)
+            piece = converter.parse(path)
+            #print(list(piece.parts))
+            # for part in piece.parts:
+            part_notes = []
+            l = piece.getTimeSignatures()
+            
+            # s = l.show('text') #prints piece time signature
+            
+            time_sig_num = piece.recurse().getElementsByClass(meter.TimeSignature)[0].numerator
+            time_sig_denum = piece.recurse().getElementsByClass(meter.TimeSignature)[0].denominator #gets time signature for piece
+            #print(piece[meter.TimeSignature][0])
+            #print(piece['Measure'][0].timeSignature)
+            
+            for el in piece.recurse().notes:
+                # print(el.offset, el, el.activeSite)
+                # print(el.beatDuration)
+                # print(el.quarterLength)
+                # print(el._getTimeSignatureForBeat)
+                # print(el.beat)
+                
+                # if getattr(el, 'isNote', None):
+                #     print("this method works")
+                #     print(el.nameWithOctave)
+                #get all note messages from all tracks
+                # for event in el:
+                    # for y in event.contextSites():
+                    #     if y[0] is part:
+                    #         offset = y[1]
+                
+                if getattr(el, 'isNote', None) and el.isNote:
+                    # print('note in {}'.format(el))
+                    #check if note is in accepted length
+                    #convert string to numerical value 
+                    if el.quarterLength in accepeted_lengths:
+                        part_notes.append([pitch_to_int(el.nameWithOctave), el.quarterLength])
+            if not len(part_notes) == 0:
+                np.save('tracks/'+str(time_sig_num)+'_'+str(time_sig_denum)+'/'+'{}.npy'.format(i), np.array(part_notes))
+                i+=1
+        print('Number of tracks parsed: {}'.format(i))
+    if not glob.glob('MC/prepared*.csv'):
+        sigs = ['3_4','4_4','6_8','2_2','2_4']
+        columns = []
+        for i in range(window):
+            columns.append('note' + str(i))
+            columns.append('duration' + str(i))
+        for sig in sigs:
+            df_notes = pd.DataFrame(columns=columns)
+            # append segments from each track as rows to dataframe
+            for path in abs_paths('tracks/'+sig):
+                notes = np.load(path)
+                for i in range(len(notes)-window):
+                    # take every x notes and durations
+                    segment = notes[i:i+window].flatten()
+                    # make into pd.Series row
+                    row = pd.Series(segment, index=df_notes.columns)
+                    # append row to dataframe
+                    df_notes = df_notes.append(row, ignore_index=True)
+            # export
+            df_notes.to_csv('prepared'+sig+'.csv', index=False)
+    time_signature = str(time_sign).split('/')
+
+    success = False
+    gen_notes =[]
+    gen_durations =[]
+
+    #Retry mechanism
+    csv_path = 'MC/prepared'+time_signature[0]+'_'+time_signature[1]+'.csv'
+    while len(gen_notes)<num_notes:
+        gen_notes,gen_durations = generate_notes(csv_path)
+
+
+    print('Generated  notes/durations'.format(num_notes))
+    print(gen_notes)
+    print(gen_durations)
+
+    #for unique file name
+    dob = datetime.datetime.now().strftime('%H%M%S')
+    modifier = format(dob)
+
+    path = "MC/gen_songs_midi/song_"+modifier
+    song_path = path + "/gen_song_"+modifier
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+    mid = MidiFile()
+    track = MidiTrack()
+       
+    mid.tracks.append(track)
+    print(mid)
+    track.append((MetaMessage('time_signature', numerator=6, denominator=8)))
+    # https://mido.readthedocs.io/en/latest/midi_files.html
+    for i in range(num_notes):
+        track.append(Message('note_on', channel=0, note=gen_notes[i], velocity=60, time=0))
+        track.append(Message('note_on', channel=0, note=gen_notes[i], velocity=0,time=int(gen_durations[i]*quarter_note_ticks)))
+    mid.save(song_path+".mid")
+
+    subprocess.Popen(['midi2ly','-o',song_path+'.ly',song_path+'.mid']).communicate()
+    subprocess.Popen(['lilypond','-fpng','-o',path,song_path+".ly"]).communicate()
+    subprocess.Popen(['timidity',song_path+'.midi','-Ow','-o',song_path+'.wav']).communicate()
+    png_list = get_pngs(path)
+
+    return png_list,song_path+".wav"
+
+
+
+
+# LILYPOND COMMANDS : To be used for generating music scores
+# Installation : sudo apt-get install -y lilypond
+# !midi2ly "new_song.ly"
+# !lilypond -fpng "new_song-midi.ly"
+
+
+
+
+
diff --git a/MC/prepared2_2.csv b/MC/prepared2_2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7e1474c822087c626c0642f32aa06c21e8516c0d
--- /dev/null
+++ b/MC/prepared2_2.csv
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diff --git a/MC/prepared2_4.csv b/MC/prepared2_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..47a43f0edbfad74bdc6c7a06aa0c191bdd462edc
--- /dev/null
+++ b/MC/prepared2_4.csv
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diff --git a/MC/prepared3_4.csv b/MC/prepared3_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f39f014dcaa20fa38dd3a293dfb71513f838fa06
--- /dev/null
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diff --git a/MC/prepared4_4.csv b/MC/prepared4_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8eb85964a39ed8bb6796020f9f68fee27a5cd327
--- /dev/null
+++ b/MC/prepared4_4.csv
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diff --git a/MC/prepared6_8.csv b/MC/prepared6_8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6bca552eaa182b610f85d02bb862cefc92f4f926
--- /dev/null
+++ b/MC/prepared6_8.csv
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diff --git a/__pycache__/music.cpython-39.pyc b/__pycache__/music.cpython-39.pyc
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diff --git a/app2.py b/app2.py
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--- /dev/null
+++ b/app2.py
@@ -0,0 +1,69 @@
+from matplotlib.pyplot import title
+from yaml import Mark
+from music import music_gen #Funtion to generate music based on ABC files
+import gradio as gr
+import os
+from MC.markov_chain import main_markov #Function to generate music based on midi files
+
+keysignature = ["C","G","D","No selection"]
+difficulty = ["beginner","intermediate","expert"]
+timesignature = ['3/4','4/4','1/8','6/8']
+
+# output = gr.Gallery() if GlobalUIGallery else "image"
+
+# interface = gr.Interface(fn = music_gen,
+# inputs=[gr.Radio(difficulty,label="Difficulty"),
+# 		gr.Radio(timesignature,label="Time Signature"),
+# 		gr.Dropdown(keysignature,label="Key Signature")],
+# outputs = [gr.Gallery(label="Sheet Music"),gr.Audio(label="Audio")],
+# title="Sheet Music Generation for Sight-Reading",
+# description="TO be added")
+# interface.launch(inline=False)
+
+import numpy as np
+
+def flip_text(x):
+    return x[::-1]
+
+def flip_image(x):
+    return np.fliplr(x)
+
+with gr.Blocks() as demo:
+    
+    gr.Markdown("""
+    ## Sheet Music Generation for Sight-Reading
+    """)
+    with gr.Tabs():
+        with gr.TabItem("ABC Model"):
+            gr.Markdown("ABC Model discription")
+            with gr.Row():
+                with gr.Column():
+                    difficulty_input_abc = gr.Radio(difficulty,label="Difficulty") #input
+                    time_sig_input_abc = gr.Radio(timesignature,label="Time Signature") #input
+                    key_sig_input_abc = gr.Dropdown(keysignature,label="Key Signature") #input
+                    with gr.Row():
+                        abc_button = gr.Button("Create Music!!") #Submit
+
+                with gr.Column():
+                    output_gallery_abc = gr.Gallery(label="Sheet Music")
+                    output_audio_abc = gr.Audio(label="Audio")
+        with gr.TabItem("Midi Model"):
+            gr.Markdown("ABC Model discription")
+            with gr.Row():
+                with gr.Column():
+                    # difficulty_input_midi = gr.Radio(difficulty,label="Difficulty") #input
+                    time_sig_input_midi = gr.Radio(timesignature,label="Time Signature") #input
+                    # key_sig_input_midi = gr.Dropdown(keysignature,label="Key Signature") #input
+                    with gr.Row():
+                        midi_button = gr.Button("Create Music!!") #Submit
+
+                with gr.Column():
+                    output_gallery_midi = gr.Gallery(label="Sheet Music")
+                    output_audio_midi = gr.Audio(label="Audio")
+    
+    abc_button.click(music_gen, inputs=[difficulty_input_abc,time_sig_input_abc,key_sig_input_abc], outputs=[output_gallery_abc,output_audio_abc])
+    midi_button.click(main_markov, inputs= time_sig_input_midi, outputs=[output_gallery_midi,output_audio_midi])
+    
+if __name__ == "__main__":
+    demo.launch()
+
diff --git a/music.py b/music.py
index 6ce06d07117a3a66f91a4f732d0df0916f7202c3..53de6c6e4422413077d625b8255f9f22aa983b1a 100644
--- a/music.py
+++ b/music.py
@@ -10,6 +10,7 @@ import pickle
 import subprocess
 import gradio as gr
 import time
+from MC.markov_chain import get_pngs
 
 
 
@@ -54,13 +55,13 @@ def time_sigFinder(time_Signature):
 		return 'M:2/4',2
 	elif time_Signature == "1/8":
 		pass
-def get_pngs(path):
-	filelist=os.listdir(path)
-	for fichier in filelist[:]: # filelist[:] makes a copy of filelist.
-		if not(fichier.endswith(".png")):
-			filelist.remove(fichier)
-	newlist = [path+'/'+x for x in filelist] #making it cwd
-	return newlist
+# def get_pngs(path):
+# 	filelist=os.listdir(path)
+# 	for fichier in filelist[:]: # filelist[:] makes a copy of filelist.
+# 		if not(fichier.endswith(".png")):
+# 			filelist.remove(fichier)
+# 	newlist = [path+'/'+x for x in filelist] #making it cwd
+# 	return newlist
 def music_gen(difficulty,time_Signature, Key_Signature):
 	if deployed:
 		#delete all files stored in gen_songs_abc