Update app.py

app.py

@@ -2,15 +2,315 @@ import streamlit as st
 from PIL import Image, ImageDraw
 import numpy as np
 
-    st.title("Space Hugging Face")
-    
-    # Sélection de l'image
-    uploaded_image = st.file_uploader("Sélectionnez une image", type=["jpg", "png", "jpeg"])
+#@title Standard/Simple Continuation
 
-    if uploaded_image is not None:
-        # Charger l'image
-        image = Image.open(uploaded_image)
+#@markdown Text-To-Music Settings
 
-        # Afficher l'image d'origine
-        st.image(image, caption="Image d'origine", use_column_width=True)
+#@markdown NOTE: You can enter any desired title or artist, or both
+
+enter_desired_song_title = "Family Guy" #@param {type:"string"}
+enter_desired_artist = "TV Themes" #@param {type:"string"}
+
+#@markdown Generation Settings
+
+number_of_tokens_to_generate = 426 #@param {type:"slider", min:30, max:2046, step:33}
+number_of_batches_to_generate = 4 #@param {type:"slider", min:1, max:16, step:1}
+temperature = 0.9 #@param {type:"slider", min:0.1, max:1, step:0.1}
+allow_model_to_stop_generation_if_needed = False #@param {type:"boolean"}
+
+print('=' * 70)
+print('Euterpe X TTM Model Generator')
+print('=' * 70)
+
+print('Searching titles...Please wait...')
+random.shuffle(AUX_DATA)
+
+titles_index = []
+
+for A in AUX_DATA:
+  titles_index.append(A[0])
+
+search_string = ''
+
+if enter_desired_song_title != '' and enter_desired_artist != '':
+  search_string = enter_desired_song_title + ' --- ' + enter_desired_artist
+
+else:
+  search_string = enter_desired_song_title + enter_desired_artist
+
+search_match = process.extract(query=search_string, choices=titles_index, limit=1)
+search_index = titles_index.index(search_match[0][0])
+
+print('Done!')
+print('=' * 70)
+print('Selected title:', AUX_DATA[search_index][0])
+print('=' * 70)
+
+if allow_model_to_stop_generation_if_needed:
+  min_stop_token = 3343
+else:
+  min_stop_token = None
+
+# Velocities
+velocities_map = [80, 80, 70, 100, 90, 80, 100, 100, 100, 90, 110, 100]
+vel_map = AUX_DATA[search_index][1]
+
+for i in range(12):
+  if vel_map[i] != 0:
+    velocities_map[i] = vel_map[i]
+
+# Loading data...
+outy = AUX_DATA[search_index][2][3:]
+
+block_marker = sum([(y * 8) for y in outy if y < 256]) / 1000
+
+inp = [outy] * number_of_batches_to_generate
+
+inp = torch.LongTensor(inp).cuda()
+
+out = model.module.generate(inp,
+                      number_of_tokens_to_generate,
+                      temperature=temperature,
+                      return_prime=True,
+                      eos_token=min_stop_token,
+                      verbose=True)
+
+out0 = out.tolist()
+print('=' * 70)
+print('Done!')
+print('=' * 70)
+#======================================================================
+print('Rendering results...')
+
+for i in range(number_of_batches_to_generate):
+
+  print('=' * 70)
+  print('Batch #', i)
+  print('=' * 70)
+
+  out1 = out0[i]
+
+  print('Sample INTs', out1[:12])
+  print('=' * 70)
+
+  if len(out) != 0:
+
+      song = out1
+      song_f = []
+
+      time = 0
+      dur = 0
+      channel = 0
+      pitch = 0
+      vel = 90
+
+      for ss in song:
+
+        if ss > 0 and ss < 256:
+
+            time += ss * 8
+
+        if ss >= 256 and ss < 256+(12*128):
+
+            dur = ((ss-256) % 128) * 30
+
+        if ss >= 256+(12*128) and ss < 256+(12*128)+(12*128):
+            channel = (ss-(256+(12*128))) // 128
+            pitch = (ss-(256+(12*128))) % 128
+            vel = velocities_map[channel]
+
+            song_f.append(['note', time, dur, channel, pitch, vel ])
+
+      detailed_stats = TMIDIX.Tegridy_SONG_to_MIDI_Converter(song_f,
+                                                          output_signature = 'Euterpe X',
+                                                          output_file_name = '/content/Euterpe-X-Music-Composition_'+str(i),
+                                                          track_name='Project Los Angeles',
+                                                          list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0],
+                                                          number_of_ticks_per_quarter=500)
+      print('=' * 70)
+      print('Displaying resulting composition...')
+      print('=' * 70)
+
+      fname = '/content/Euterpe-X-Music-Composition_'+str(i)
+
+      x = []
+      y =[]
+      c = []
+
+      colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']
+
+      for s in song_f:
+        x.append(s[1] / 1000)
+        y.append(s[4])
+        c.append(colors[s[3]])
+
+      FluidSynth("/usr/share/sounds/sf2/FluidR3_GM.sf2", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))
+      display(Audio(str(fname + '.wav'), rate=16000))
+
+      plt.figure(figsize=(14,5))
+      ax=plt.axes(title=fname)
+      ax.set_facecolor('black')
+
+      plt.scatter(x,y, c=c)
+
+      ax.axvline(x=block_marker, c='w')
+
+      plt.xlabel("Time")
+      plt.ylabel("Pitch")
+      plt.show()#@title Standard/Simple Continuation
+
+#@markdown Text-To-Music Settings
+
+#@markdown NOTE: You can enter any desired title or artist, or both
+
+enter_desired_song_title = "Family Guy" #@param {type:"string"}
+enter_desired_artist = "TV Themes" #@param {type:"string"}
+
+#@markdown Generation Settings
+
+number_of_tokens_to_generate = 426 #@param {type:"slider", min:30, max:2046, step:33}
+number_of_batches_to_generate = 4 #@param {type:"slider", min:1, max:16, step:1}
+temperature = 0.9 #@param {type:"slider", min:0.1, max:1, step:0.1}
+allow_model_to_stop_generation_if_needed = False #@param {type:"boolean"}
+
+print('=' * 70)
+print('Euterpe X TTM Model Generator')
+print('=' * 70)
+
+print('Searching titles...Please wait...')
+random.shuffle(AUX_DATA)
+
+titles_index = []
+
+for A in AUX_DATA:
+  titles_index.append(A[0])
+
+search_string = ''
+
+if enter_desired_song_title != '' and enter_desired_artist != '':
+  search_string = enter_desired_song_title + ' --- ' + enter_desired_artist
+
+else:
+  search_string = enter_desired_song_title + enter_desired_artist
+
+search_match = process.extract(query=search_string, choices=titles_index, limit=1)
+search_index = titles_index.index(search_match[0][0])
+
+print('Done!')
+print('=' * 70)
+print('Selected title:', AUX_DATA[search_index][0])
+print('=' * 70)
+
+if allow_model_to_stop_generation_if_needed:
+  min_stop_token = 3343
+else:
+  min_stop_token = None
+
+# Velocities
+velocities_map = [80, 80, 70, 100, 90, 80, 100, 100, 100, 90, 110, 100]
+vel_map = AUX_DATA[search_index][1]
+
+for i in range(12):
+  if vel_map[i] != 0:
+    velocities_map[i] = vel_map[i]
+
+# Loading data...
+outy = AUX_DATA[search_index][2][3:]
+
+block_marker = sum([(y * 8) for y in outy if y < 256]) / 1000
+
+inp = [outy] * number_of_batches_to_generate
+
+inp = torch.LongTensor(inp).cuda()
+
+out = model.module.generate(inp,
+                      number_of_tokens_to_generate,
+                      temperature=temperature,
+                      return_prime=True,
+                      eos_token=min_stop_token,
+                      verbose=True)
+
+out0 = out.tolist()
+print('=' * 70)
+print('Done!')
+print('=' * 70)
+#======================================================================
+print('Rendering results...')
+
+for i in range(number_of_batches_to_generate):
+
+  print('=' * 70)
+  print('Batch #', i)
+  print('=' * 70)
+
+  out1 = out0[i]
+
+  print('Sample INTs', out1[:12])
+  print('=' * 70)
+
+  if len(out) != 0:
+
+      song = out1
+      song_f = []
+
+      time = 0
+      dur = 0
+      channel = 0
+      pitch = 0
+      vel = 90
+
+      for ss in song:
+
+        if ss > 0 and ss < 256:
+
+            time += ss * 8
+
+        if ss >= 256 and ss < 256+(12*128):
+
+            dur = ((ss-256) % 128) * 30
+
+        if ss >= 256+(12*128) and ss < 256+(12*128)+(12*128):
+            channel = (ss-(256+(12*128))) // 128
+            pitch = (ss-(256+(12*128))) % 128
+            vel = velocities_map[channel]
+
+            song_f.append(['note', time, dur, channel, pitch, vel ])
+
+      detailed_stats = TMIDIX.Tegridy_SONG_to_MIDI_Converter(song_f,
+                                                          output_signature = 'Euterpe X',
+                                                          output_file_name = '/content/Euterpe-X-Music-Composition_'+str(i),
+                                                          track_name='Project Los Angeles',
+                                                          list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0],
+                                                          number_of_ticks_per_quarter=500)
+      print('=' * 70)
+      print('Displaying resulting composition...')
+      print('=' * 70)
+
+      fname = '/content/Euterpe-X-Music-Composition_'+str(i)
+
+      x = []
+      y =[]
+      c = []
+
+      colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']
+
+      for s in song_f:
+        x.append(s[1] / 1000)
+        y.append(s[4])
+        c.append(colors[s[3]])
+
+      FluidSynth("/usr/share/sounds/sf2/FluidR3_GM.sf2", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))
+      display(Audio(str(fname + '.wav'), rate=16000))
+
+      plt.figure(figsize=(14,5))
+      ax=plt.axes(title=fname)
+      ax.set_facecolor('black')
+
+      plt.scatter(x,y, c=c)
+
+      ax.axvline(x=block_marker, c='w')
+
+      plt.xlabel("Time")
+      plt.ylabel("Pitch")
+      plt.show()