Update app.py

app.py

@@ -3,8 +3,19 @@ import torchaudio
 from torchaudio.transforms import Resample
 import torch
 import gradio as gr
-def separate(inst,audio_path,progress=gr.Progress(True)):
-    model = Splitter(inst)
+def separate(audio_path:str,inst_no:int,):
+    """
+    Separate audio into instrument tracks.
+
+    Args:
+        audio_path (str): Path to input audio.
+        inst_no (int): Number of instruments to separate.
+
+    Returns:
+        tuple: Up to 5 MP3 file paths for separated tracks.
+    """
+    progress=gr.Progress(True)
+    model = Splitter(inst_no)
     wav, sr = torchaudio.load(audio_path)
     target_sr = 44100
     if sr != target_sr:
@@ -14,7 +25,7 @@ def separate(inst,audio_path,progress=gr.Progress(True)):
     with torch.no_grad():
         results = model.forward(wav)
     for i in results:
-        torchaudio.save(f"{i}.mp3", results[i], sr,format="mp3")
+        torchaudio.save(f"{i}.mp3", results[i], sr)
     return tuple([i+".mp3" for i in results] + [None for _ in range(5-len(results))])
 
-gr.Interface(separate, [gr.Dropdown([2,4,5]),gr.Audio(type="filepath")], [gr.Audio(type="filepath"), gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath")]).launch()
+gr.Interface(separate, [gr.Audio(type="filepath"),gr.Dropdown([2,4,5],2)], [gr.Audio(type="filepath"), gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath"),gr.Audio(type="filepath")]).launch(mcp_server=True)