import appStore.target as target_extraction import appStore.netzero as netzero import appStore.sector as sector import appStore.adapmit as adapmit import appStore.ghg as ghg import appStore.doc_processing as processing from utils.uploadAndExample import add_upload import streamlit as st st.set_page_config(page_title = 'Climate Policy Intelligence', initial_sidebar_state='expanded', layout="wide") with st.sidebar: # upload and example doc choice = st.sidebar.radio(label = 'Select the Document', help = 'You can upload the document \ or else you can try a example document', options = ('Upload Document', 'Try Example'), horizontal = True) add_upload(choice) with st.container(): st.markdown("

Climate Policy Understanding App

", unsafe_allow_html=True) st.write(' ') with st.expander("ℹ️ - About this app", expanded=False): st.write( """ Climate Policy Understanding App is an open-source\ digital tool which aims to assist policy analysts and \ other users in extracting and filtering relevant \ information from public documents. What Happens in background? - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\ In this step the document is broken into smaller paragraphs \ (based on word/sentence count). - Step 2: The paragraphs are fed to **Target Classifier** which detects if the paragraph contains any *Target* related information or not. - Step 3: The paragraphs which are detected containing some target \ related information are then fed to multiple classifier to enrich the Information Extraction. """) st.write("") apps = [processing.app, target_extraction.app, netzero.app, ghg.app, sector.app, adapmit.app] multiplier_val =1/len(apps) if st.button("Get the work done"): prg = st.progress(0.0) for i,func in enumerate(apps): func() prg.progress((i+1)*multiplier_val) if 'key1' in st.session_state: target_extraction.target_display() st.write(st.session_state.key1)