app.py

import torch
from transformers import pipeline
import gradio as gr

MODEL_NAME = "Music Genre classification_NIST"

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="audio-classification",
    model=MODEL_NAME,
    device=device,
)

def classify_audio(filepath):
    preds = pipe(filepath, top_k = 10)
    outputs = {}
    for p in preds:
        outputs[p["label"]] = p["score"]
    return outputs

demo = gr.Interface(
    fn=classify_audio,
    inputs= gr.Audio(label="Audio file", type="filepath"),
    outputs=gr.Label(),
    title="Music Genre Classification",
    description=(
        "Project by-Asutosh Panda and Bismaytosh Malik."
    ),
    examples="./examples",
    cache_examples=True,
    allow_flagging="never",
)
share=True in launch()
demo.launch()