app.py

import gradio as gr
import os
import torch
import io
from pyannote.audio import Pipeline
from pyannote.audio import Audio
from pyannote.audio.pipelines.utils.hook import TimingHook
from pyannote.core import Segment

pipeline = Pipeline.from_pretrained(
  "pyannote/speaker-diarization-3.1",
  use_auth_token=os.environ['api'])



#def process_audio(audio):
    # Your audio processing logic goes here
    # For demonstration purposes, we'll just return the input audio
    return audio

#with gr.Blocks() as demo:
    audio_input = gr.Audio(label="Upload Audio", source="upload")
    process_button = gr.Button("Process")
    audio_output = gr.Audio(label="Processed Audio")

    process_button.click(fn=process_audio, inputs=audio_input, outputs=audio_output)

demo.launch()