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import streamlit as st
from functions import *
st.set_page_config(page_title="Earnings Summarization", page_icon="📖")
st.sidebar.header("Earnings Summarization")
st.markdown("## Earnings Summarization with FaceBook-Bart")
max_len= st.slider("Maximum length of the summarized text",min_value=50,max_value=200,step=10)
min_len= st.slider("Minimum length of the summarized text",min_value=20,max_value=200,step=10)
st.markdown("####")
st.subheader("Summarized Earnings Call with matched Entities")
if st.session_state['earnings_passages']:
text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages'])
summarized_text = sum_pipe(text_to_summarize,max_length=max_len,min_length=min_len,clean_up_tokenization_spaces=True,no_repeat_ngram_size=4)
summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])
with st.spinner("Summarizing and matching entities, this takes a few seconds..."):
entity_match_html = highlight_entities(text_to_summarize,summarized_text)
st.markdown("####")
with st.expander(label='Summarized Earnings Call',expanded=True):
st.write(entity_match_html, unsafe_allow_html=True)
st.markdown("####")
summary_downloader(summarized_text)
else:
st.write("No text to summarize detected, please ensure you have entered the YouTube URL on the Sentiment Analysis page") |