Update app.py

app.py

@@ -144,17 +144,21 @@ def ClassifyMIDI(input_midi):
     #==============================================================
 
     classification_summary_string = '=' * 70
+    classification_summary_string += '\n'
     
     print('Composition has', notes_counter, 'notes')
     print('=' * 70)
     print('Composition was split into' , len(input_data), 'chunks of 340 notes each with 170 notes overlap')
     print('Number of notes in all composition chunks:', len(input_data) * 340)
 
-    classification_summary_string += 'Composition has ' + str(notes_counter) + ' notes'
+    classification_summary_string += 'Composition has ' + str(notes_counter) + ' notes\n'
     classification_summary_string += '=' * 70
-    classification_summary_string += 'Composition was split into ' + str(len(input_data)) + ' chunks of 340 notes each with 170 notes overlap'
-    classification_summary_string += 'Number of notes in all composition chunks: ' + str(len(input_data) * 340)
-
+    classification_summary_string += '\n'
+    classification_summary_string += 'Composition was split into ' + str(len(input_data)) + ' chunks of 340 notes each with 170 notes overlap\n'
+    classification_summary_string += 'Number of notes in all composition chunks: ' + str(len(input_data) * 340) + '\n'
+    classification_summary_string += '=' * 70
+    classification_summary_string += '\n'
+    
     number_of_batches = 100 # @param {type:"slider", min:1, max:100, step:1}
     
     # @markdown NOTE: You can increase the number of batches on high-ram GPUs for better classification
@@ -198,12 +202,22 @@ def ClassifyMIDI(input_midi):
     print('Most common classification label ratio:' , results.count(final_result) / len(results))
     print('Most common classification label index', final_result)
     print('=' * 70)
+
+    classification_summary_string += 'Most common classification label: ' + str(classifier_labels[0][final_result-384]) + '\n'
+    classification_summary_string += 'Most common classification label ratio: ' + str(results.count(final_result) / len(results)) + '\n'
+    classification_summary_string += 'Most common classification label index '+ str(final_result) + 'n'
+    classification_summary_string += '=' * 70
+    classification_summary_string += '\n'
     
     print('All classification labels summary:')
     print('=' * 70)
     
     for i, a in enumerate(all_results_labels):
-      print('Notes', i*170, '-', (i*170)+340, '===', a)
+        print('Notes', i*170, '-', (i*170)+340, '===', a)
+        classification_summary_string += 'Notes ' + str(i*170) + ' - ' + str((i*170)+340) + ' === ' + str(a) + '\n'
+    
+    classification_summary_string += '=' * 70
+    classification_summary_string += '\n'
     
     print('=' * 70)
     print('Done!')
@@ -248,7 +262,7 @@ def ClassifyMIDI(input_midi):
     #========================================================
 
     output_midi_title = str(fn1)
-    output_midi_summary = str('Summary')
+    output_midi_summary = classification_summary_string
     output_midi = str(new_fn)
     output_audio = (16000, audio)
     
@@ -256,7 +270,6 @@ def ClassifyMIDI(input_midi):
 
     print('Output MIDI file name:', output_midi)
     print('Output MIDI title:', output_midi_title)
-    print('Output MIDI summary:', output_midi_summary)
     print('=' * 70)