import streamlit as st import json import os # shifted from below - this must be the first streamlit call; otherwise: problems st.set_page_config(page_title = 'Climate Policy Intelligence', initial_sidebar_state='expanded', layout="wide") import logging logging.getLogger().setLevel(logging.INFO) import appStore.target as tapp_extraction import appStore.sector as sector import appStore.adapmit as adapmit import appStore.conditional as conditional import appStore.subtarget as subtarget import appStore.category as category import appStore.iki_sheets as iki_sheets import appStore.doc_processing as processing import appStore.excel_convert as excel_convert from utils.uploadAndExample import add_upload from PIL import Image import pkg_resources installed_packages = pkg_resources.working_set 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) with(open('docStore/sample/files.json','r')) as json_file: files = json.load(json_file) add_upload(choice, files) with st.container(): st.markdown("

Climate Policy Understanding: IKI Tracs

", 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. """) st.write('**Definitions**') st.caption(""" - **Target**: Targets are an intention to achieve a specific result, \ for example, to reduce GHG emissions to a specific level \ (a GHG target) or increase energy efficiency or renewable \ energy to a specific level (a non-GHG target), typically by \ a certain date. - **Economy-wide Target**: Certain Target are applicable \ not at specific Sector level but are applicable at economic \ wide scale. - **Netzero**: Identifies if its Netzero Target or not. - 'NET-ZERO target_labels' = ['T_Netzero','T_Netzero_C'] - **GHG Target**: GHG targets refer to contributions framed as targeted \ outcomes in GHG terms. - 'GHG': ['T_Transport_Unc','T_Transport_C','T_Economy_C','T_Economy_Unc','T_Energy_C','T_Energy_Unc'] - 'NON GHG TARGET': ['T_Adaptation_Unc','T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C'] - **Conditionality**: An “unconditional contribution” is what countries \ could implement without any conditions and based on their own \ resources and capabilities. A “conditional contribution” is one \ that countries would undertake if international means of support \ are provided, or other conditions are met. - **Action**: Actions are an intention to implement specific means of \ achieving GHG reductions, usually in forms of concrete projects. - **Policies and Plans**: Policies are domestic planning documents \ such as policies, regulations or guidlines, and Plans are broader \ than specific policies or actions, such as a general intention \ to ‘improve efficiency’, ‘develop renewable energy’, etc. \ The terms come from the World Bank's NDC platform and WRI's publication. """) c1, c2, c3 = st.columns([12,1,10]) with c1: image = Image.open('docStore/img/flow.jpg') st.image(image) with c3: st.write(""" 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 **TAPP(Target/Action/Policy/Plan multilabel) Classifier** which detects if the paragraph contains any *TAPP* related information or not. - Step 3: The paragraphs which are detected containing some TAPP \ related information are then fed to multiple classifier to enrich the Information Extraction. """) list_ = "" for package in installed_packages: list_ = list_ + f"{package.key}=={package.version}\n" st.download_button('Download Requirements', list_, file_name='requirements.txt') st.write("") apps = [processing.app, tapp_extraction.app, sector.app, adapmit.app, conditional.app, subtarget.app, category.app] multiplier_val =1/len(apps) if st.button("Analyze Document"): prg = st.progress(0.0) for i,func in enumerate(apps): func() prg.progress((i+1)*multiplier_val) prg.empty() if 'key1' in st.session_state: iki_sheets.netzero() iki_sheets.targets() iki_sheets.mitigation() iki_sheets.adaptation() with st.sidebar: topic = st.radio( "Which category you want to explore?", ('Netzero', 'Targets', 'Mitigation','Adaptation')) if topic == 'Netzero': iki_sheets.netzero_display() excel_convert.filter_dataframe('netzero_hits',['keep','text','Parameter','page']) with st.sidebar: st.write('-------------') df_xlsx = excel_convert.to_excel() st.download_button(label='📥 Download Result', data=df_xlsx , file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') elif topic == 'Targets': iki_sheets.target_display() excel_convert.filter_dataframe('target_hits',['keep','text','Parameter','page']) with st.sidebar: st.write('-------------') df_xlsx = excel_convert.to_excel() st.download_button(label='📥 Download Result', data=df_xlsx , file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') elif topic == 'Mitigation': iki_sheets.mitigation_display() excel_convert.filter_dataframe('mitigation_hits',['keep','text','Parameter','Type','page']) with st.sidebar: st.write('-------------') df_xlsx = excel_convert.to_excel() st.download_button(label='📥 Download Result', data=df_xlsx , file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') else: iki_sheets.adaptation_display() excel_convert.filter_dataframe('adaptation_hits',['keep','text','Type','page']) with st.sidebar: st.write('-------------') df_xlsx = excel_convert.to_excel() st.download_button(label='📥 Download Result', data=df_xlsx , file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') #policyaction.policy_display() # st.write(st.session_state.key1)