app.py

from transformers import pipeline

model_id = "arham061/distilhubert-finetuned-PASCAL_Dataset_Augmented"
pipe = pipeline("audio-classification", model=model_id)

def classify_audio(filepath):
    preds = pipe(filepath)
    outputs = {}
    for p in preds:
        outputs[p["label"]] = p["score"]
    return outputs

import gradio as gr

demo = gr.Interface(
    fn=classify_audio, 
    inputs=gr.Audio(type="filepath"), 
    outputs="label", 
    examples = ['normal.wav', 'murmur.wav', 'extra_systole.wav', 'extra_hystole.wav', 'artifact.wav'],
)

demo.launch(debug=True)