More output while building and fix learner typo.

.ipynb_checkpoints/app-checkpoint.py

@@ -19,11 +19,13 @@ import os
 print(os.getcwd())
 
 # Load the stored data. This is needed to generate the vocab.
+print('Loading data to build vocabulary.')
 data_dir = Path('.')
 data = load_data(data_dir, 'data.pkl')
 
 from huggingface_hub import hf_hub_download
 
+print('Downloading model.')
 model_cache_path = hf_hub_download(repo_id="psistolar/musicautobot-fine1", filename="model.pth")
 
 
@@ -33,7 +35,7 @@ config = default_config()
 config['encode_position'] = True
 
 
-
+print("Building model.")
 # Load our fine-tuned model
 learner = music_model_learner(
     data, 
@@ -41,6 +43,8 @@ learner = music_model_learner(
     pretrained_path=model_cache_path
 )
 
+print("Ready to use.")
+
 
 
 def process_midi(midi_file):
@@ -50,7 +54,7 @@ def process_midi(midi_file):
     item = MusicItem.from_file(name, data.vocab);
 
     # full is the prediction appended to the input
-    pred, full = learn.predict(item, n_words=100)
+    pred, full = learner.predict(item, n_words=100)
    
     # convert to stream and then MIDI file
     stream = full.to_stream()

app.py

@@ -19,11 +19,13 @@ import os
 print(os.getcwd())
 
 # Load the stored data. This is needed to generate the vocab.
+print('Loading data to build vocabulary.')
 data_dir = Path('.')
 data = load_data(data_dir, 'data.pkl')
 
 from huggingface_hub import hf_hub_download
 
+print('Downloading model.')
 model_cache_path = hf_hub_download(repo_id="psistolar/musicautobot-fine1", filename="model.pth")
 
 
@@ -33,7 +35,7 @@ config = default_config()
 config['encode_position'] = True
 
 
-
+print("Building model.")
 # Load our fine-tuned model
 learner = music_model_learner(
     data, 
@@ -41,6 +43,8 @@ learner = music_model_learner(
     pretrained_path=model_cache_path
 )
 
+print("Ready to use.")
+
 
 
 def process_midi(midi_file):
@@ -50,7 +54,7 @@ def process_midi(midi_file):
     item = MusicItem.from_file(name, data.vocab);
 
     # full is the prediction appended to the input
-    pred, full = learn.predict(item, n_words=100)
+    pred, full = learner.predict(item, n_words=100)
    
     # convert to stream and then MIDI file
     stream = full.to_stream()