app.py

import streamlit as st
from transformers import pipeline

# Load the audio classification pipeline
audio_classification_pipeline = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")

def classify_audio(audio_file):
    # Perform audio classification
    results = audio_classification_pipeline(audio=audio_file)
    return results

def main():
    st.title('Hugging Face Audio Classification')

    # File uploader for audio file
    st.subheader('Upload Audio File:')
    audio_file = st.file_uploader("Choose a WAV file", type=["wav"])

    # Check if audio file is uploaded
    if audio_file is not None:
        st.audio(audio_file, format='audio/wav')

        # Button to classify audio
        if st.button('Classify'):
            with st.spinner('Classifying...'):
                # Perform classification
                results = classify_audio(audio_file)
            st.success('Classification complete!')
            st.write("Prediction:", results)

if __name__ == '__main__':
    main()