Create app.py

app.py

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+import gradio as gr
+from magenta.models.music_vae import configs
+from magenta.models.music_vae.trained_model import TrainedModel
+import note_seq
+
+# Load the MusicVAE model
+CONFIG = configs.CONFIG_MAP['cat-mel_2bar_big']
+checkpoint = 'https://storage.googleapis.com/magentadata/models/music_vae/checkpoints/cat-mel_2bar_big.ckpt'
+music_vae = TrainedModel(CONFIG, batch_size=4, checkpoint_dir_or_path=checkpoint)
+
+def generate_music(tempo):
+    """Generate music based on the tempo."""
+    # Generate a latent vector
+    z = music_vae.sample(n=1)
+    
+    # Decode the latent vector into a music sequence
+    generated_sequence = music_vae.decode(z)
+    
+    # Adjust the sequence tempo
+    for note in generated_sequence[0].notes:
+        note.velocity = max(40, min(127, int(tempo)))  # Adjust note velocity
+    
+    # Save as MIDI file
+    output_midi = "generated_music.mid"
+    note_seq.sequence_proto_to_midi_file(generated_sequence[0], output_midi)
+    return output_midi
+
+# Define the Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Interactive Music Composer 🎵")
+    tempo = gr.Slider(40, 200, step=10, label="Select Tempo (BPM)")
+    generate_button = gr.Button("Generate Music")
+    output_file = gr.Audio(label="Generated Music")
+    
+    generate_button.click(fn=generate_music, inputs=tempo, outputs=output_file)
+
+# Launch the app
+demo.launch()