Change app.py

app.py

@@ -1,12 +1,17 @@
 import streamlit as st
 from transformers import pipeline
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.markdown("# Podcast Q&A")
 
-pipe = pipeline("summarization", model='facebook/bart-large-cnn')
-text = st.text_area('enter some text')
+st.markdown(
+        """
+        This helps understand information-dense podcast episodes by doing the following:
+        - Speech to Text transcription - using OpenSource Whisper Model
+        - Summarizes the episode
+        - Allows you to ask questions and returns direct quotes from the episode.
+
+        """
+        )
+
+audio_file = st.file_uploader("Upload audio copy of file", key="upload", type=['.mp3'])
 
-if text:
-    out = pipe(text, max_length=130, min_length=30, do_sample=False)
-    st.json(out)