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import appStore.target as target_extraction |
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import appStore.netzero as netzero |
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import appStore.sector as sector |
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import appStore.adapmit as adapmit |
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import appStore.ghg as ghg |
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import appStore.policyaction as policyaction |
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import appStore.conditional as conditional |
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import appStore.indicator as indicator |
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import appStore.doc_processing as processing |
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from utils.uploadAndExample import add_upload |
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import streamlit as st |
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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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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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add_upload(choice) |
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with st.container(): |
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st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Understanding App </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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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 **Target Classifier** which detects if |
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the paragraph contains any *Target* related information or not. |
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- Step 3: The paragraphs which are detected containing some target \ |
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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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Classifers: |
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- **Netzero**: Detects if any Netzero commitment is present in paragraph or not. |
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- **GHG**: Detects if any GHG related information present in paragraph or not. |
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- **Sector**: Detects which sectors are spoken/discussed about in paragraph. |
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- **Adaptation-Mitigation**: Detects if the paragraph is related to Adaptation and/or Mitigation. |
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""") |
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st.caption("""**Target** (Definition): 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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st.caption("""**Economy-wide Target** (Definition): 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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st.write("") |
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apps = [processing.app, target_extraction.app, netzero.app, ghg.app, |
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policyaction.app, indicator.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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with st.sidebar: |
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topic = st.radio( |
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"Which category you want to explore?", |
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('Target', 'Action', 'Policies/Plans')) |
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if topic == 'Target': |
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st.dataframe(st.session_state['key1']) |
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elif topic == 'Action': |
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policyaction.action_display() |
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else: |
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policyaction.policy_display() |
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