app info

app.py

@@ -51,29 +51,40 @@ with st.expander("ℹ️ - About this app", expanded=False):
                   '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.
               """)
-    # st.
 
-    #     What Happens in background?
+    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.
+        - 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 
-        
-    #     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.
-    #     """)
+        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,