Update 01_🏠_Home.py

01_🏠_Home.py

@@ -20,9 +20,9 @@ st.sidebar.header("Home")
 
 asr_model_options = ['tiny.en','base.en','small.en']
     
-asr_model_name = st.sidebar.selectbox("Whisper Model Options", options=asr_model_options, key="sbox")
+asr_model_name = st.sidebar.selectbox("Transcription model", options=asr_model_options, key="sbox")
 
-st.markdown("## Earnings Call Analysis Whisperer")
+st.markdown("## Interview analyzer")
 
 twitter_link = """
 [![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
@@ -31,14 +31,7 @@ twitter_link = """
 st.markdown(twitter_link)
 
 st.markdown(
-    """
-    This app assists finance analysts with transcribing and analysis Earnings Calls by carrying out the following tasks:
-    - Transcribing earnings calls using Open AI's Whisper API, takes approx 3mins to transcribe a 1hr call less than 25mb in size.
-    - Analysing the sentiment of transcribed text using the quantized version of [FinBert-Tone](https://huggingface.co/nickmuchi/quantized-optimum-finbert-tone).
-    - Summarization of the call with [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) model with entity extraction
-    - Question Answering Search engine powered by Langchain and [Sentence Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2).
-    - Knowledge Graph generation using [Babelscape/rebel-large](https://huggingface.co/Babelscape/rebel-large) model.
-    
+    """  
     **👇 Enter a YouTube Earnings Call URL below and navigate to the sidebar tabs** 
     
 """
@@ -68,5 +61,3 @@ st.markdown(
 )
 
 upload_wav = st.file_uploader("Upload a .wav/.mp3/.mp4 audio file ",key="upload",type=['.wav','.mp3','.mp4'])
-
-st.markdown("![visitors](https://visitor-badge.glitch.me/badge?page_id=nickmuchi.earnings-call-whisperer)")