Update app.py

app.py

@@ -4,9 +4,12 @@ import appStore.sector as sector
 import appStore.adapmit as adapmit
 import appStore.ghg as ghg
 import appStore.policyaction as policyaction
+import appStore.conditional as conditional
 import appStore.indicator as indicator
+import appStore.reader as reader
 import appStore.doc_processing as processing
 from utils.uploadAndExample import add_upload
+from PIL import Image
 import streamlit as st
 
 st.set_page_config(page_title = 'Climate Policy Intelligence', 
@@ -32,29 +35,69 @@ with st.expander("ℹ️ - About this app", expanded=False):
         digital tool which aims to assist policy analysts and \
         other users in extracting and filtering relevant \
         information from public documents.
-
-        What Happens in background?
-
-        - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
-        In this step the document is broken into smaller paragraphs \
-        (based on word/sentence count).
-        - Step 2: The paragraphs are fed to **Target Classifier** which detects if
-        the paragraph contains any *Target* related information or not.
-        - Step 3: The paragraphs which are detected containing some target \
-        related information are then fed to multiple classifier to enrich the 
-        Information Extraction.
-        
-        Classifers:
-          - **Netzero**: Detects if any Netzero commitment is present in paragraph or not.
-          - **GHG**: Detects if any GHG related information present in paragraph or not.
-          - **Sector**: Detects which sectors are spoken/discussed about in paragraph.
-          - **Adaptation-Mitigation**: Detects if the paragraph is related to Adaptation and/or Mitigation.
-
-               
         """)
+    st.write('**Definitions**')
+
+    st.caption("""
+            - **Target**: Targets are an intention to achieve a specific result, \
+            for example, to reduce GHG emissions to a specific level \
+            (a GHG target) or increase energy efficiency or renewable \
+            energy to a specific level (a non-GHG target), typically by \ 
+            a certain date.
+            - **Economy-wide Target**: Certain Target are applicable \
+                not at specific Sector level but are applicable at economic \
+                wide scale.
+            - **Netzero**: Identifies if its Netzero Target or not.
+                - 'NET-ZERO': target_labels = ['T_Netzero','T_Netzero_C']
+                - 'Non Netzero Target': target_labels_neg = ['T_Economy_C',
+                  'T_Economy_Unc','T_Adaptation_C','T_Adaptation_Unc','T_Transport_C',
+                  'T_Transport_O_C','T_Transport_O_Unc','T_Transport_Unc']
+                - 'Others': Other Targets beside covered above
+            - **GHG Target**: GHG targets refer to contributions framed as targeted \
+                              outcomes in GHG terms.
+                - 'GHG': target_labels_ghg_yes = ['T_Transport_Unc','T_Transport_C']
+                - 'NON GHG TRANSPORT TARGET': target_labels_ghg_no = ['T_Adaptation_Unc',\
+                   'T_Adaptation_C', 'T_Transport_O_Unc', 'T_Transport_O_C']
+                - 'OTHERS': Other Targets beside covered above.
+            - **Conditionality**: An “unconditional contribution” is what countries \
+             could implement without any conditions and based on their own \
+             resources and capabilities. A “conditional contribution” is one \
+             that countries would undertake if international means of support \
+             are provided, or other conditions are met.
+            - **Action**: Actions are an intention to implement specific means of \
+             achieving GHG reductions, usually in forms of concrete projects.
+            - **Policies and Plans**: Policies are domestic planning documents \
+              such as policies, regulations or guidlines, and Plans  are broader \
+             than specific policies or actions, such as a general intention \ 
+             to ‘improve efficiency’, ‘develop renewable energy’, etc. \
+            The terms come from the World Bank's NDC platform and WRI's publication.
+              """)
+    c1, c2, c3 =  st.columns([12,1,10])
+    with c1:
+        image = Image.open('docStore/img/flow.jpg') 
+        st.image(image)
+    with c3:
+        st.write("""
+            What Happens in background?
+            
+            
+    
+            - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
+            In this step the document is broken into smaller paragraphs \
+            (based on word/sentence count).
+            - Step 2: The paragraphs are fed to **Target Classifier** which detects if
+            the paragraph contains any *Target* related information or not.
+            - Step 3: The paragraphs which are detected containing some target \
+            related information are then fed to multiple classifier to enrich the 
+            Information Extraction.
+    
+            The Step 2 and 3 are repated then similarly for Action and  Policies & Plans.
+            """)
+                  
     st.write("")
 apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
-        sector.app, policyaction.app, indicator.app, adapmit.app]
+        policyaction.app, conditional.app, sector.app, adapmit.app,indicator.app]
+
 multiplier_val =1/len(apps)
 if st.button("Analyze Document"):
     prg = st.progress(0.0)