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import streamlit as st |
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from functions import * |
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st.sidebar.header("Summarization") |
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max_len= st.slider("Maximum length of the summarized text",min_value=70,max_value=200,step=10,value=100) |
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min_len= st.slider("Minimum length of the summarized text",min_value=20,max_value=200,step=10) |
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st.markdown("####") |
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st.subheader("Summarized Earnings Call with matched Entities") |
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if "earnings_passages" not in st.session_state: |
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st.session_state["earnings_passages"] = '' |
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if st.session_state['earnings_passages']: |
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with st.spinner("Summarizing and matching entities, this takes a few seconds..."): |
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try: |
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text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages']) |
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print(text_to_summarize) |
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summarized_text = summarize_text(text_to_summarize,max_len=max_len,min_len=min_len) |
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except IndexError: |
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try: |
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text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages']) |
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summarized_text = summarize_text(text_to_summarize,max_len=max_len,min_len=min_len) |
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except IndexError: |
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text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages']) |
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summarized_text = summarize_text(text_to_summarize,max_len=max_len,min_len=min_len) |
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entity_match_html = highlight_entities(text_to_summarize,summarized_text) |
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st.markdown("####") |
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with st.expander(label='Summarized Earnings Call',expanded=True): |
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st.write(entity_match_html, unsafe_allow_html=True) |
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st.markdown("####") |
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summary_downloader(summarized_text) |
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else: |
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st.write("No text to summarize detected, please ensure you have entered the YouTube URL on the Sentiment Analysis page") |
