Update app.py

app.py

@@ -20,6 +20,8 @@ from models import MusicAudioDiffusion
 
 from gradio import Markdown
 
+import spaces
+
 class MusicFeaturePredictor:
     def __init__(self, path, device="cuda:0", cache_dir=None, local_files_only=False):
         self.beats_tokenizer = AutoTokenizer.from_pretrained(
@@ -221,7 +223,7 @@ else:
     mustango = Mustango(device="cpu")
     
 # output_wave = mustango.generate("This techno song features a synth lead playing the main melody.", 5, 3, disable_progress=False)
-
+@spaces.GPU(duration=120)
 def gradio_generate(prompt, steps, guidance):
     output_wave = mustango.generate(prompt, steps, guidance)
     # output_filename = f"{prompt.replace(' ', '_')}_{steps}_{guidance}"[:250] + ".wav"
@@ -270,7 +272,7 @@ gr_interface = gr.Interface(
         ["This is a live performance of a classical music piece. There is an orchestra performing the piece with a violin lead playing the main melody. The atmosphere is sentimental and heart-touching. This piece could be playing in the background at a classy restaurant. The chord progression in this song is Am7, Gm, Dm, A7, Dm. The beat is 3. This song is in Largo. The key of this song is D minor.", 200, 3],
         ["This is a techno piece with drums and beats and a leading melody. A synth plays chords. The music kicks off with a powerful and relentless drumbeat. Over the pounding beats, a leading melody emerges. In the middle of the song, a flock of seagulls flies over the venue and make loud bird sounds. It has strong danceability and can be played in a club. The tempo is 120 bpm. The chords played by the synth are Am, Cm, Dm, Gm.", 200, 3],
     ],
-    cache_examples=True,
+    cache_examples="lazy",
 )
 
 with gr.Blocks(css=css) as demo: