Update app.py

app.py

@@ -1,43 +1,23 @@
-import os
+mport os
 import gradio as gr
 from scipy.io.wavfile import write
 
 
 def inference(audio):
-    os.makedirs("out", exist_ok=True)
-    vocals_files = []
-    instrumental_files = []
-    for audio_file in audio:
-        write('test.wav', audio_file[0], audio_file[1])
-        os.system("python3 -m demucs.separate -n htdemucs --two-stems=vocals -d cpu test.wav -o out")
-        vocals_files.append("./out/htdemucs/test/vocals.wav")
-        instrumental_files.append("./out/htdemucs/test/no_vocals.wav")
-    return vocals_files, instrumental_files
-
-
-def download_file(filepath):
-    with open(filepath, "rb") as f:
-        file_bytes = f.read()
-    return file_bytes
-
-
+  os.makedirs("out", exist_ok=True)
+  write('test.wav', audio[0], audio[1])
+  os.system("python3 -m demucs.separate -n htdemucs --two-stems=vocals -d cpu test.wav -o out")
+  return "./out/htdemucs/test/vocals.wav","./out/htdemucs/test/no_vocals.wav"
+    
 title = "Source Separation (v4)"
-description = "This is the latest 'bleeding edge version' which enables the new v4 Hybrid Transformer model. <br> for this space, 2 stem separation (Karaoke Mode) is enabled and CPU mode which has been optimized for best quality & processing time. <p>| Gradio demo for Demucs(v4): Music Source Separation in the Waveform Domain. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below.</p>"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.13254' target='_blank'>Music Source Separation in the Waveform Domain</a> | <a href='https://github.com/facebookresearch/demucs' target='_blank'>Github Repo</a> | <a href='https://www.thafx.com' target='_blank'>//THAFX</a></p>"
-
-audio_input = gr.inputs.Audio(label="Input", type="file", multiple=True)
-vocals_output = gr.outputs.Audio(label="Vocals", type="file", process=download_file)
-instrumental_output = gr.outputs.Audio(label="No Vocals / Instrumental", type="file", process=download_file)
-
-examples = [['test.mp3']]
 
-# Create the Gradio interface
+examples=[['test.mp3']]
 gr.Interface(
-    fn=inference,
-    inputs=audio_input,
-    outputs=[vocals_output, instrumental_output],
+    inference, 
+    gr.Audio(type="numpy", label="Input"),
+    [gr.Audio(type="filepath", label="Vocals"),gr.Audio(type="filepath", label="No Vocals / Instrumental")],
     title=title,
     description=description,
     article=article,
     examples=examples
-).launch(enable_queue=True, share=True)
+    ).launch(enable_queue=True)