Update app.py

app.py

@@ -1,9 +1,17 @@
 import streamlit as st
 from transformers import pipeline
+from pydub import AudioSegment
+import io
 
 # Load the audio classification pipeline
 audio_classification_pipeline = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")
 
+def convert_mp3_to_wav(audio_bytes):
+    # Convert MP3 bytes to WAV format
+    audio = AudioSegment.from_file(io.BytesIO(audio_bytes), format="mp3")
+    wav_bytes = audio.export(format="wav").read()
+    return wav_bytes
+
 def classify_audio(audio_file):
     # Perform audio classification
     results = audio_classification_pipeline(audio=audio_file)
@@ -14,19 +22,21 @@ def main():
 
     # File uploader for audio file
     st.subheader('Upload Audio File:')
-    audio_file = st.file_uploader("Choose a WAV file", type=["wav"])
+    audio_file = st.file_uploader("Choose an MP3 file", type=["mp3"])
 
     # Check if audio file is uploaded
     if audio_file is not None:
-        st.audio(audio_file, format='audio/wav')
+        st.audio(audio_file, format='audio/mp3')
 
         # Button to classify audio
         if st.button('Classify'):
             with st.spinner('Classifying...'):
-                # Perform classification
-                results = classify_audio(audio_file)
+                # Convert MP3 to WAV and classify
+                wav_bytes = convert_mp3_to_wav(audio_file.read())
+                results = classify_audio(wav_bytes)
             st.success('Classification complete!')
             st.write("Prediction:", results)
 
 if __name__ == '__main__':
     main()
+