Update app.py

app.py

@@ -1,29 +1,17 @@
+from diffusers import DiffusionPipeline
 import gradio as gr
-import torchaudio
-import torch
-from transformers import AutoProcessor, MusicgenForConditionalGeneration
 
-# Load MusicGen model
-model_name = "facebook/musicgen-small"
-model = MusicgenForConditionalGeneration.from_pretrained(model_name)
-processor = AutoProcessor.from_pretrained(model_name)
-model.to("cuda" if torch.cuda.is_available() else "cpu")
+pipe = DiffusionPipeline.from_pretrained("riffusion/riffusion-model-v1")
+pipe.to("cpu")  # Safe for any system
 
-def generate_music(description):
-    inputs = processor(text=[description], return_tensors="pt")
-    inputs = {k: v.to(model.device) for k, v in inputs.items()}
-    audio_values = model.generate(**inputs, max_new_tokens=256)
-    audio = processor.decode(audio_values[0], sampling_rate=16000)
-    torchaudio.save("output.wav", audio.unsqueeze(0), 16000)
-    return "output.wav"
+def generate_music(prompt):
+    output = pipe(prompt)
+    audio = output["audio"]
+    return audio
 
-# Gradio UI
-demo = gr.Interface(
+gr.Interface(
     fn=generate_music,
-    inputs=gr.Textbox(label="Describe your song"),
-    outputs=gr.Audio(label="Generated Track"),
-    title="LarynxLab MVP",
-    description="Type a music idea and get a short AI instrumental."
-)
-
-demo.launch()
+    inputs=gr.Textbox(label="Describe your music"),
+    outputs=gr.Audio(label="AI Song"),
+    title="LarynxLab - Riffusion Edition"
+).launch()