app.py

import streamlit as st
from transformers import pipeline
from pydub import AudioSegment, silence
#import speech_recognition as sr

pipe = pipeline('sentiment-analysis')
text = st.text_area('Enter your notes')

if text:
    out = pipe(text)
    st.json(out)

st.markdown("<h1 style = text align:center;'> Group Therapy Notes </h1>",unsafe_allow_html = True)
st.markdown("---",unsafe_allow_html=True)
audio=st.file_uploader("Upload Your Audio File", type=['mp3','wav','m4a'])

if audio:
    audio_segment= AudioSegment.from_file(audio)
    chunks=silence.split_on_silence(audio_segment, min_silence_len=500, silence_thresh= audio_segment.dBFS-20,keep_silence=100)
    for index, chunk in enumerate (chunks):
        chunk.export(str(index)+".wav", format="wav")
        print(chunk)