Update app.py

app.py

@@ -1,24 +1,30 @@
-import os
-os.system('pip install gradio==2.3.0a0')
-os.system('pip install voicefixer --upgrade')
-from voicefixer import VoiceFixer
 import gradio as gr
+from voicefixer import VoiceFixer
+
 voicefixer = VoiceFixer()
-def inference(audio,mode):
-    voicefixer.restore(input=audio.name, # input wav file path
-                    output="output.wav", # output wav file path
-                    cuda=False, # whether to use gpu acceleration
-                    mode = int(mode)) # You can try out mode 0, 1 to find out the best result
-    return 'output.wav'
 
-inputs = [gr.inputs.Audio(type="file", label="Input Audio"),gr.inputs.Radio(choices=['0','1','2'], type="value", default='0', label='mode')]
-outputs =  gr.outputs.Audio(type="file",label="Output Audio")
+def inference(audio, mode):
+    voicefixer.restore(input=audio.name,
+                       output="output.wav",
+                       cuda=False,
+                       mode=int(mode))
+    return "output.wav"
 
+input_audio = gr.inputs.Audio(label="Input Audio")
+mode = gr.inputs.Radio(choices=['0', '1', '2'], label="Mode", default='0')
+output_audio = gr.outputs.Audio(label="Output Audio")
 
 title = "Voice Fixer"
 description = "Gradio demo for VoiceFixer: Toward General Speech Restoration With Neural Vocoder. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below."
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.13731' target='_blank'>VoiceFixer: Toward General Speech Restoration With Neural Vocoder</a> | <a href='https://github.com/haoheliu/voicefixer_main' target='_blank'>Github Repo</a></p>"
 
-examples=[['bruce.wav','2']]
+examples = [["bruce.wav", "2"]]
 
-gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
+gr.Interface(inference, 
+             inputs=[input_audio, mode], 
+             outputs=output_audio,
+             title=title, 
+             description=description, 
+             article=article, 
+             examples=examples, 
+             enable_queue=True).launch()