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app.py

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+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)

requirements.txt

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+gradio
+torch
+transformers