app.py

import gradio as gr
from transformers import pipeline

# This links your Space to your specific AST model repository
pipe = pipeline("audio-classification", model="aarya030402/ast_messy_mashup")

def classify_audio(filepath):
    # The pipeline automatically handles the AST feature extraction and 16kHz resampling
    preds = pipe(filepath)
    outputs = {p["label"]: p["score"] for p in preds}
    return outputs

# Build the Gradio Interface
demo = gr.Interface(
    fn=classify_audio,
    inputs=gr.Audio(type="filepath", label="Upload Music Mashup (.wav)"),
    outputs=gr.Label(num_top_classes=5),
    title="AST Music Genre Classifier",
    description="Upload a 20-second audio clip to identify the genre using your fine-tuned Audio Spectrogram Transformer."
)

if __name__ == "__main__":
    demo.launch()