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Update app.py
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app.py
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@@ -1,5 +1,15 @@
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import
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from utils.uploadAndExample import add_upload
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####################################### Dashboard ######################################################
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@@ -43,22 +53,22 @@ with st.expander("ℹ️ - About this app", expanded=False):
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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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st.write("")
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apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
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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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from PIL import Image
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import streamlit as st
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####################################### Dashboard ######################################################
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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 the 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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# The Step 2 and 3 are repated then similarly for Action and Policies & Plans.
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# """)
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#st.write("")
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apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
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