Fixed download button.

app.py

@@ -4,9 +4,15 @@ import whisper
 import subprocess
 from simple_diarizer.diarizer import Diarizer
 import streamlit as st
+import base64
 
+model_size = 'tiny'
 
-model_size = 'base'
+
+
+def create_download_link(val, filename, label):
+    b64 = base64.b64encode(val)
+    return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="{filename}">{label}</a>'
 
 
 def speech_to_text(uploaded):
@@ -84,22 +90,7 @@ def transcribe(uploaded, nu_speakers):
 
     tdf_cols = ['speaker','start','end','text']
     #st.dataframe(tdf[tdf_cols])
-
-    st.download_button(
-         label="Download transcript as text file",
-         data='\n'.join(lines),
-         file_name='transcript.txt',
-         mime='text/plain',
-         )
-
-    st.download_button(
-         label="Download transcript as CSV (with time codes)",
-         data=tdf[tdf_cols].to_csv( float_format='%.2f', index=False).encode('utf-8'),
-         file_name='transcript.csv',
-         mime='text/csv',
-         )
-
-    return tdf[tdf_cols]
+    return {'text':lines, 'df': tdf[tdf_cols]}
 
 
 descript = ("This web app creates transcripts using OpenAI's [Whisper](https://github.com/openai/whisper) to transcribe "
@@ -123,4 +114,12 @@ if submit:
     bytes_data = uploaded.getvalue()
     with open('temp_audio', 'wb') as outfile:
         outfile.write(bytes_data)
-    text_df = transcribe('temp_audio', nu_speakers)
+    transcript = transcribe('temp_audio', nu_speakers)
+
+    csv = transcript['df'].to_csv( float_format='%.2f', index=False).encode('utf-8')
+    text = '\n'.join(transcript['text']).encode('utf-8')
+    download_url = create_download_link(text, 'transcript.txt', 'Download transcript as plain text.')
+    st.markdown(download_url, unsafe_allow_html=True)
+
+    download_url = create_download_link(csv, 'transcript.csv', 'Download transcript as CSV (with time codes)')
+    st.markdown(download_url, unsafe_allow_html=True)