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app.py

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+from transformers import pipeline
+import gradio as gr
+
+
+MODEL_NAME = "catsound"
+HF_USER = "universalml"
+
+
+def prediction_function(input_file):
+    repo_id = HF_USER + "/" + MODEL_NAME
+    model = pipeline("audio-classification", model=repo_id)
+
+    try:
+        result = model(input_file)
+        predictions = {}
+        labels = []
+        for each_label in result:
+            predictions[each_label["label"]] = each_label["score"]
+            labels.append(each_label["label"])
+        result = predictions
+    except:
+        result = "no data provided!!"
+
+    return result
+
+
+def create_interface():
+    interface = gr.Interface(
+        fn=prediction_function,
+        # inputs=gr.Audio(sources="upload", type="filepath"),
+        inputs=gr.Audio(sources="microphone", type="filepath"),
+        outputs=gr.Label(num_top_classes=3),
+        title=MODEL_NAME,
+    )
+
+    interface.launch()
+
+
+create_interface()