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import streamlit as st |
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import json |
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import os |
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st.set_page_config(page_title = 'Climate Policy Intelligence', |
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initial_sidebar_state='expanded', layout="wide") |
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import logging |
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logging.getLogger().setLevel(logging.INFO) |
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import appStore.target as tapp_extraction |
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import appStore.sector as sector |
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import appStore.adapmit as adapmit |
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import appStore.conditional as conditional |
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import appStore.subtarget as subtarget |
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import appStore.category as category |
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import appStore.iki_sheets as iki_sheets |
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import appStore.doc_processing as processing |
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import appStore.excel_convert as excel_convert |
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from utils.uploadAndExample import add_upload |
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from PIL import Image |
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import pkg_resources |
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installed_packages = pkg_resources.working_set |
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with st.sidebar: |
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choice = st.sidebar.radio(label = 'Select the Document', |
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help = 'You can upload the document \ |
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or else you can try a example document', |
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options = ('Upload Document', 'Try Example'), |
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horizontal = True) |
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with(open('docStore/sample/files.json','r')) as json_file: |
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files = json.load(json_file) |
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add_upload(choice, files) |
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with st.container(): |
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st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Understanding: IKI Tracs </h2>", unsafe_allow_html=True) |
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st.write(' ') |
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with st.expander("ℹ️ - About this app", expanded=False): |
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st.write( |
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""" |
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Climate Policy Understanding App is an open-source\ |
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digital tool which aims to assist policy analysts and \ |
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other users in extracting and filtering relevant \ |
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information from public documents. |
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""") |
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st.write('**Definitions**') |
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st.caption(""" |
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- **Target**: Targets are an intention to achieve a specific result, \ |
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for example, to reduce GHG emissions to a specific level \ |
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(a GHG target) or increase energy efficiency or renewable \ |
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energy to a specific level (a non-GHG target), typically by \ |
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a certain date. |
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- **Economy-wide Target**: Certain Target are applicable \ |
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not at specific Sector level but are applicable at economic \ |
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wide scale. |
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- **Netzero**: Identifies if its Netzero Target or not. |
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- 'NET-ZERO target_labels' = ['T_Netzero','T_Netzero_C'] |
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- **GHG Target**: GHG targets refer to contributions framed as targeted \ |
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outcomes in GHG terms. |
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- 'GHG': ['T_Transport_Unc','T_Transport_C','T_Economy_C','T_Economy_Unc','T_Energy_C','T_Energy_Unc'] |
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- 'NON GHG TARGET': ['T_Adaptation_Unc','T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C'] |
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- **Conditionality**: An “unconditional contribution” is what countries \ |
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could implement without any conditions and based on their own \ |
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resources and capabilities. A “conditional contribution” is one \ |
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that countries would undertake if international means of support \ |
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are provided, or other conditions are met. |
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- **Action**: Actions are an intention to implement specific means of \ |
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achieving GHG reductions, usually in forms of concrete projects. |
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- **Policies and Plans**: Policies are domestic planning documents \ |
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such as policies, regulations or guidlines, and Plans are broader \ |
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than specific policies or actions, such as a general intention \ |
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to ‘improve efficiency’, ‘develop renewable energy’, etc. \ |
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The terms come from the World Bank's NDC platform and WRI's publication. |
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""") |
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c1, c2, c3 = st.columns([12,1,10]) |
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with c1: |
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image = Image.open('docStore/img/flow.jpg') |
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st.image(image) |
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with c3: |
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st.write(""" |
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What Happens in background? |
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- Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\ |
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In this step the document is broken into smaller paragraphs \ |
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(based on word/sentence count). |
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- Step 2: The paragraphs are fed to **TAPP(Target/Action/Policy/Plan multilabel) Classifier** which detects if |
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the paragraph contains any *TAPP* related information or not. |
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- Step 3: The paragraphs which are detected containing some TAPP \ |
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related information are then fed to multiple classifier to enrich the |
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Information Extraction. |
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""") |
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list_ = "" |
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for package in installed_packages: |
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list_ = list_ + f"{package.key}=={package.version}\n" |
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st.download_button('Download Requirements', list_, file_name='requirements.txt') |
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st.write("") |
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apps = [processing.app, tapp_extraction.app, sector.app, adapmit.app, |
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conditional.app, subtarget.app, category.app] |
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multiplier_val =1/len(apps) |
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if st.button("Analyze Document"): |
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prg = st.progress(0.0) |
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for i,func in enumerate(apps): |
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func() |
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prg.progress((i+1)*multiplier_val) |
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prg.empty() |
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if 'key1' in st.session_state: |
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iki_sheets.netzero() |
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iki_sheets.targets() |
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iki_sheets.mitigation() |
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iki_sheets.adaptation() |
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with st.sidebar: |
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topic = st.radio( |
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"Which category you want to explore?", |
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('Netzero', 'Targets', 'Mitigation','Adaptation')) |
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if topic == 'Netzero': |
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iki_sheets.netzero_display() |
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excel_convert.filter_dataframe('netzero_hits',['keep','text','Parameter','page']) |
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with st.sidebar: |
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st.write('-------------') |
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df_xlsx = excel_convert.to_excel() |
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st.download_button(label='📥 Download Result', |
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data=df_xlsx , |
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file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') |
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elif topic == 'Targets': |
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iki_sheets.target_display() |
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excel_convert.filter_dataframe('target_hits',['keep','text','Parameter','page']) |
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with st.sidebar: |
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st.write('-------------') |
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df_xlsx = excel_convert.to_excel() |
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st.download_button(label='📥 Download Result', |
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data=df_xlsx , |
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file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') |
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elif topic == 'Mitigation': |
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iki_sheets.mitigation_display() |
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excel_convert.filter_dataframe('mitigation_hits',['keep','text','Parameter','Type','page']) |
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with st.sidebar: |
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st.write('-------------') |
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df_xlsx = excel_convert.to_excel() |
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st.download_button(label='📥 Download Result', |
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data=df_xlsx , |
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file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') |
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else: |
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iki_sheets.adaptation_display() |
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excel_convert.filter_dataframe('adaptation_hits',['keep','text','Type','page']) |
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with st.sidebar: |
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st.write('-------------') |
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df_xlsx = excel_convert.to_excel() |
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st.download_button(label='📥 Download Result', |
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data=df_xlsx , |
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file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx') |
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