Update app.py

app.py

@@ -18,19 +18,26 @@ pipe = pipeline("audio-classification", model=model_id, device=device)
 
 def classify_audio(filepath):
     """
-      Goes from
-      [{'score': 0.8339303731918335, 'label': 'country'},
+    Goes from
+    [{'score': 0.8339303731918335, 'label': 'country'},
     {'score': 0.11914275586605072, 'label': 'rock'},]
-     to
-     {"country":  0.8339303731918335, "rock":0.11914275586605072}
+    to
+    {"country":  0.8339303731918335, "rock":0.11914275586605072}
     """
+    import time
+    start_time = time.time()
+    
+    # Assuming `pipe` is your model pipeline for inference
     preds = pipe(filepath)
-    # preds = predict_trunc(filepath)
+    
     outputs = {}
     for p in preds:
         outputs[p["label"]] = p["score"]
-    return outputs
-
+    
+    end_time = time.time()
+    prediction_time = end_time - start_time
+    
+    return outputs, prediction_time
 
 title = "🎵 Music Genre Classifier"
 description = """
@@ -38,14 +45,15 @@ Music Genre Classifier model (Fine-tuned "ntu-spml/distilhubert") Dataset: [GTZA
 """
 
 filenames = ['rock-it-21275.mp3']
-filenames = [[f"./{f}"] for f in filenames]
+filenames = [f"./{f}" for f in filenames]
 demo = gr.Interface(
     fn=classify_audio,
     inputs=gr.Audio(type="filepath"),
     outputs=[gr.outputs.Label(), gr.Number(label="Prediction time (s)")],
     title=title,
     description=description,
-    examples=filenames,
+    examples=[(f,) for f in filenames],
 )
 
+
 demo.launch()