Update app.py

app.py

@@ -23,12 +23,9 @@ import matplotlib.pyplot as plt
 in_space = os.getenv("SYSTEM") == "spaces"
          
 # =================================================================================================
-                       
+
 @spaces.GPU
-def ClassifyMIDI(input_midi):
-    print('=' * 70)
-    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
-    start_time = reqtime.time()
+def classify_GPU(input_data):
 
     print('Loading model...')
 
@@ -70,6 +67,48 @@ def ClassifyMIDI(input_midi):
 
     #==================================================================
 
+    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
+    
+    print('=' * 70)
+    print('Annotated MIDI Dataset Classifier')
+    print('=' * 70)
+    print('Classifying...')
+    
+    torch.cuda.empty_cache()
+    
+    model.eval()
+    
+    results = []
+    
+    for input in input_data:
+    
+        x = torch.tensor([input[:1022]] * number_of_batches, dtype=torch.long, device='cuda')
+    
+        with ctx:
+          out = model.generate(x,
+                              1,
+                              temperature=0.3,
+                              return_prime=False,
+                              verbose=False)
+    
+        y = out.tolist()
+    
+        output = [l[0] for l in y]
+        result = mode(output)
+    
+        results.append(result)
+
+    return results
+                       
+# =================================================================================================
+
+def ClassifyMIDI(input_midi):
+    print('=' * 70)
+    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+    start_time = reqtime.time()
+
     print('=' * 70)
 
     fn = os.path.basename(input_midi.name)
@@ -158,39 +197,8 @@ def ClassifyMIDI(input_midi):
     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
-    
-    print('=' * 70)
-    print('Annotated MIDI Dataset Classifier')
-    print('=' * 70)
-    print('Classifying...')
-    
-    torch.cuda.empty_cache()
-    
-    model.eval()
-    
-    results = []
-    
-    for input in input_data:
-    
-        x = torch.tensor([input[:1022]] * number_of_batches, dtype=torch.long, device='cuda')
-    
-        with ctx:
-          out = model.generate(x,
-                              1,
-                              temperature=0.3,
-                              return_prime=False,
-                              verbose=False)
-    
-        y = out.tolist()
-    
-        output = [l[0] for l in y]
-        result = mode(output)
-    
-        results.append(result)
+
+    results = classify_GPU(input_data)
     
     all_results_labels = [classifier_labels[0][r-384] for r in results]
     final_result = mode(results)