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 Intelligence 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. # Classifiers # - Netzero: # """) # st.write("") apps = [processing.app, target_extraction.app, netzero.app, ghg.app, sector.app, adapmit.app] multiplier_val =100/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)