Update app.py

app.py

@@ -1,3 +1,43 @@
 import gradio as gr
+import torch
+from transformers import pipeline
 
-gr.Interface.load("models/ardneebwar/wav2vec2-animal-sounds-finetuned-hubert-finetuned-animals").launch()
+username = "ardneebwar"  ## Complete your username
+model_id = f"{username}/facebook/hubert-base-ls960"
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+pipe = pipeline("audio-classification", model=model_id, device=device)
+
+
+def classify_audio(filepath):
+    import time
+    start_time = time.time()
+    
+    # Assuming `pipe` is your model pipeline for inference
+    preds = pipe(filepath)
+    
+    outputs = {}
+    for p in preds:
+        outputs[p["label"]] = p["score"]
+    
+    end_time = time.time()
+    prediction_time = end_time - start_time
+    
+    return outputs, prediction_time
+
+title = "🎵 Animal Sound Classifier"
+description = """
+Animal Sound Classifier model (Fine-tuned "ntu-spml/distilhubert") Dataset: ESC-50 from Github (only the animal sounds)
+"""
+
+filenames = ['dog.wav', 'cat.wav', 'cow.wav']
+filenames = [f"./{f}" for f in filenames]
+
+demo = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(type="filepath"),
+    outputs=[gr.Label(), gr.Number(label="Prediction time (s)")],
+    title=title,
+    description=description,
+)
+
+demo.launch()