Update app.py

app.py

@@ -233,17 +233,61 @@ def ClassifyMIDI(input_midi):
     
         results.append(result)
     
-    all_results_labels = [classifier_labels[0][r-384] for r in results]
     final_result = mode(results)
     
+    print('=' * 70)
     print('Done!')
     print('=' * 70)
     
-    print('Most common classification label:', classifier_labels[0][final_result-384])
-    print('Most common classification label ratio:' , results.count(final_result) / len(results))
-    print('Most common classification label index', final_result)
+    result_toks = [final_result[0]-512, final_result[1]-512]
+    mc_song_artist = song_artist_tokens_to_song_artist(result_toks)
+    gidx = genre_labels_fnames.index(mc_song_artist)
+    mc_genre = genre_labels[gidx][1]
+    
+    print('Most common classification genre label:', mc_genre)
+    print('Most common classification song-artist label:', mc_song_artist)
+    print('Most common song-artist classification label ratio:' , results.count(final_result) / len(results))
+    print('=' * 70)
+    
+    print('All classification labels summary:')
+    print('=' * 70)
+    
+    all_artists_labels = []
+    
+    for i, res in enumerate(results):
+      result_toks = [res[0]-512, res[1]-512]
+      song_artist = song_artist_tokens_to_song_artist(result_toks)
+      gidx = genre_labels_fnames.index(song_artist)
+      genre = genre_labels[gidx][1]
+      print('Notes', i*170, '-', (i*170)+340, '===', genre, '---', song_artist)
+    
+      artist_label = str_strip_artist(song_artist.split(' --- ')[1])
+    
+      all_artists_labels.append(artist_label)
+    
+    print('=' * 70)
+    
+    mode_artist_label = mode(all_artists_labels)
+    mode_artist_label_count = all_artists_labels.count(mode_artist_label)
+    
+    print('Aggregated artist classification label:', mode_artist_label)
+    print('Aggregated artist classification label ratio:', mode_artist_label_count / len(all_artists_labels))
+    
+    print('=' * 70)
+    print('Done!')
     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'
@@ -265,53 +309,13 @@ def ClassifyMIDI(input_midi):
     print('=' * 70)
     
     #===============================================================================
-    print('Rendering results...')
-
-    score_idx = processed_scores_labels.index(classifier_labels[0][final_result-384])
-
-    output_score = processed_scores[score_idx][1][:6000]
-    
-    print('=' * 70)
-    print('Sample INTs', results[:15])
-    print('=' * 70)
-    
-    fn1 = processed_scores[score_idx][0]
-
-    output_score = TMIDIX.recalculate_score_timings(output_score)
-
-    output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(output_score)
-    
-    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,
-                                                              output_signature = 'Advanced MIDI Classifier',
-                                                              output_file_name = fn1,
-                                                              track_name='Project Los Angeles',
-                                                              list_of_MIDI_patches=patches,
-                                                              timings_multiplier=16
-                                                              )
-    
-    new_fn = fn1+'.mid'
-            
-    
-    audio = midi_to_colab_audio(new_fn, 
-                        soundfont_path=soundfont,
-                        sample_rate=16000,
-                        volume_scale=10,
-                        output_for_gradio=True
-                        )
-    
-    print('Done!')
-    print('=' * 70)
 
     #========================================================
 
     output_midi_title = str(fn1)
     output_midi_summary = classification_summary_string
-    output_midi = str(new_fn)
-    output_audio = (16000, audio)
     
-    output_plot = TMIDIX.plot_ms_SONG(output_score, plot_title=output_midi, return_plt=True, timings_multiplier=16)
-
-    print('Output MIDI file name:', output_midi)
+    print('Output MIDI file name:', mc_song_artist)
     print('Output MIDI title:', output_midi_title)
     print('=' * 70) 
     
@@ -323,7 +327,7 @@ def ClassifyMIDI(input_midi):
     print('-' * 70)
     print('Req execution time:', (reqtime.time() - start_time), 'sec')
 
-    return output_midi_title, output_midi_summary, output_midi, output_audio, output_plot
+    return classification_summary_string
 
 # =================================================================================================
 
@@ -403,13 +407,9 @@ if __name__ == "__main__":
 
         gr.Markdown("## Classification results")
 
-        output_midi_title = gr.Textbox(label="Best classification match MIDI title")
-        output_midi_summary = gr.Textbox(label="MIDI classification summary")
-        output_audio = gr.Audio(label="Best classification match MIDI audio", format="wav", elem_id="midi_audio")
-        output_plot = gr.Plot(label="Best classification match MIDI score plot")
-        output_midi = gr.File(label="Best classification match MIDI file", file_types=[".mid"])
+        output_midi_cls_summary = gr.Textbox(label="MIDI classification summary")
 
         run_event = run_btn.click(ClassifyMIDI, [input_midi],
-                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
+                                  [output_midi_cls_summary])
 
         app.queue().launch()