| import streamlit as st | |
| from functions import * | |
| st.set_page_config(page_title="Interview Summarization", page_icon="📖") | |
| st.sidebar.header("Summarization") | |
| max_len= st.slider("Maximum length of the summarized text",min_value=70,max_value=200,step=10,value=100) | |
| min_len= st.slider("Minimum length of the summarized text",min_value=20,max_value=200,step=10) | |
| st.markdown("####") | |
| st.subheader("Summarized Interview with matched Entities") | |
| if "earnings_passages" not in st.session_state: | |
| st.session_state["earnings_passages"] = '' | |
| if st.session_state['earnings_passages']: | |
| with st.spinner("Summarizing and matching entities, this takes a few seconds..."): | |
| try: | |
| text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages']) | |
| print(text_to_summarize) | |
| summarized_text = summarize_text(text_to_summarize,max_len=max_len,min_len=min_len) | |
| except IndexError: | |
| try: | |
| text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages']) | |
| summarized_text = summarize_text(text_to_summarize,max_len=max_len,min_len=min_len) | |
| except IndexError: | |
| text_to_summarize = chunk_and_preprocess_text(st.session_state['earnings_passages']) | |
| summarized_text = summarize_text(text_to_summarize,max_len=max_len,min_len=min_len) | |
| entity_match_html = highlight_entities(text_to_summarize,summarized_text) | |
| st.markdown("####") | |
| with st.expander(label='Summarized Interview',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") |