leavoigt commited on
Commit
1e5a262
1 Parent(s): afff22e

Update app.py

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Files changed (1) hide show
  1. app.py +24 -14
app.py CHANGED
@@ -1,5 +1,15 @@
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- import streamlit as st
 
 
 
 
 
 
 
 
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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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- 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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+ 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,