Update app.py

app.py

@@ -1,27 +1,24 @@
 import gradio as gr
 from transformers import pipeline
-import torch
 import soundfile as sf
+import io
+import numpy as np
 
-# Load your pre-trained text-to-music model (example: using a music generation model)
 model = pipeline("text-to-music", model="facebook/musicgen-small")
 
-# Function to process text and generate music
 def generate_music(text_input):
-    # This part depends on the model you use
-    music_output = model(text_input)  # Generate music based on input text
+    music_output = model(text_input)
+
+    audio_buffer = io.BytesIO() 
+    sf.write(audio_buffer, music_output, 22050, format='WAV')  
+    audio_buffer.seek(0)
     
-    # Save the music to a file or return it directly
-    output_file = "/path/to/output.wav"
-    sf.write(output_file, music_output, 22050)  # Example: Saving at 22050 Hz sample rate
-    return output_file
+    return audio_buffer 
 
-# Set up the Gradio interface
 iface = gr.Interface(
     fn=generate_music,
     inputs=gr.Textbox(label="Enter Text for Music"),
     outputs=gr.Audio(label="Generated Music")
 )
 
-# Launch the app
 iface.launch()