ppsingh commited on
Commit
bc82aca
1 Parent(s): 155bcb1
Files changed (1) hide show
  1. app.py +24 -24
app.py CHANGED
@@ -23,30 +23,30 @@ with st.container():
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  st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Intelligence 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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-
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- # What Happens in background?
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-
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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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-
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- # Classifiers
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- # - Netzero:
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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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  sector.app, adapmit.app]
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  multiplier_val =1/len(apps)
@@ -57,5 +57,5 @@ if st.button("Get the work done"):
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  prg.progress((i+1)*multiplier_val)
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  if 'key1' in st.session_state:
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- # target_extraction.target_display()
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  st.write(st.session_state.key1)
 
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  st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Intelligence 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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+
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+ What Happens in background?
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+
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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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+
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+ Classifiers
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+ - Netzero:
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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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  sector.app, adapmit.app]
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  multiplier_val =1/len(apps)
 
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  prg.progress((i+1)*multiplier_val)
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  if 'key1' in st.session_state:
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+ target_extraction.target_display()
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  st.write(st.session_state.key1)