Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the audio classification pipeline with the MIT model
+pipe = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")
+
+# Define a function to classify an audio file
+def classify_audio(audio):
+    # The input audio is passed to the model for classification
+    result = pipe(audio)
+    return {label['label']: label['score'] for label in result}
+
+# Set up the Gradio interface
+app = gr.Interface(
+    fn=classify_audio,                  # Function to classify audio
+    inputs=gr.Audio(source="upload", type="filepath"),  # Input field for uploading an audio file
+    outputs=gr.Label(num_top_classes=3),              # Output with top 3 classification results
+    title="Audio Classification",                     # App title
+    description="Upload an audio file to classify it using MIT's fine-tuned AudioSet model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()