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Update app.py
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app.py
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import appStore.vulnerability_analysis as vulnerability_analysis
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import appStore.
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import streamlit as st
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st.set_page_config(page_title = 'Vulnerability
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initial_sidebar_state='expanded', layout="wide")
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import appStore.vulnerability_analysis as vulnerability_analysis
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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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#rom PIL import Image
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import streamlit as st
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st.set_page_config(page_title = 'Vulnerability Analysis',
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initial_sidebar_state='expanded', layout="wide")
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with st.sidebar:
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# upload and example doc
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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;'> Vulnerability Analysis </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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The Vulnerability Analysis 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 related to different vulnerable groups 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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# - 'Non Netzero Target': target_labels_neg = ['T_Economy_C',
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# 'T_Economy_Unc','T_Adaptation_C','T_Adaptation_Unc','T_Transport_C',
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# 'T_Transport_O_C','T_Transport_O_Unc','T_Transport_Unc']
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# - 'Others': Other Targets beside covered above
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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': target_labels_ghg_yes = ['T_Transport_Unc','T_Transport_C']
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# - 'NON GHG TRANSPORT TARGET': target_labels_ghg_no = ['T_Adaptation_Unc',\
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# 'T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C']
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# - 'OTHERS': Other Targets beside covered above.
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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 then fed to the **Vulnerability 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 reference \
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to a vulnerable group are then fed to multiple classifier to enrich the
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Information Extraction.
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""")
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st.write("")
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apps = [vulnerability_analysis.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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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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('Vulnerability')
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if topic == 'Vulnerability':
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target_extraction.vulnerability_display()
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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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st.write(st.session_state.key1)
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