Update app.py

app.py

@@ -1,14 +1,15 @@
 import streamlit as st
+import time as t
 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')
+#pipe = pipeline('sentiment-analysis')
+#text = st.text_area('Enter your notes')
 
-if text:
-    out = pipe(text)
-    st.json(out)
+#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)
@@ -20,19 +21,11 @@ if audio:
 # stride_length_s is a tuple of the left and right stride length.
 # With only 1 number, both sides get the same stride, by default
 # the stride_length on one side is 1/6th of the chunk_length_s
-        audio_segment= AudioSegment.from_file(audio)
-        output = pipe(audio_segment, chunk_length_s=10, stride_length_s=(4, 2))
-
-
-#        st.json(output)
-        
-#        asr = pipeline('automatic-speech-recognition')
-#        asr_out = asr (audio_segment)
-#    st.json(asr_out)
-    
-         
-#    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)
-        
+        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):
+                #output = pipe(audio_segment, chunk_length_s=10, stride_length_s=(4, 2))
+                print (chunk)
+