statistics

app.py

@@ -23,20 +23,35 @@ with st.container():
         st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Intelligence App </h2>", unsafe_allow_html=True)
         st.write(' ')
 
-# with st.expander("ℹ️ - About this app", expanded=False):
-#     st.write(
-#         """
-#         Climate Policy Understanding App is an open-source\
-#         digital tool which aims to assist policy analysts and \
-#         other users in extracting and filtering relevant \
-#           information from public documents.     
-#         """)
-#     st.write("")
+with st.expander("ℹ️ - About this app", expanded=False):
+    st.write(
+        """
+        Climate Policy Understanding App is an open-source\
+        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.
+
+        Classifiers
+        - Netzero: 
+               
+        """)
+    st.write("")
 apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
         sector.app, adapmit.app]
-multiplier_val = int(100/len(apps))
+multiplier_val =100/len(apps)
 if st.button("Get the work done"):
-    prg = st.progress(0)
+    prg = st.progress(0.0)
     for i,func in enumerate(apps):
         func()
         prg.progress((i+1)*multiplier_val)

appStore/target.py

@@ -87,25 +87,33 @@ def target_display():
         hits  = df[df['Target Label'] == 'TARGET']
         range_val = min(5,len(hits))
         if range_val !=0:
-            count_df = df['Target Label'].value_counts()
-            count_df = count_df.rename('count')
-            count_df = count_df.rename_axis('Target Label').reset_index()
-            count_df['Label_def'] = count_df['Target Label'].apply(lambda x: _lab_dict[x])
-
-            fig = px.bar(count_df, y="Label_def", x="count", orientation='h', height=200)
-            c1, c2 = st.columns([1,1])
-            with c1:
-                st.plotly_chart(fig,use_container_width= True)
-            
-            count_netzeo = sum(hits['Netzero Label'] == 'NETZERO')
+            count_target = sum(hits['Target Label'] == 'TARGET')
+            count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
             count_ghg = sum(hits['GHG Label'] == 'LABEL_2')
             count_economy = sum([True if 'Economy-wide' in x else False 
                               for x in hits['Sector Label']])
+            
+            # count_df = df['Target Label'].value_counts()
+            # count_df = count_df.rename('count')
+            # count_df = count_df.rename_axis('Target Label').reset_index()
+            # count_df['Label_def'] = count_df['Target Label'].apply(lambda x: _lab_dict[x])
 
+            # fig = px.bar(count_df, y="Label_def", x="count", orientation='h', height=200)
+            c1, c2 = st.columns([1,1])
+            with c1:
+                st.write('**Target Paragraphs**: `{}`'.format(count_target))
+                st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
+
+                # st.plotly_chart(fig,use_container_width= True)
+            
+            # count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
+            # count_ghg = sum(hits['GHG Label'] == 'LABEL_2')
+            # count_economy = sum([True if 'Economy-wide' in x else False 
+            #                   for x in hits['Sector Label']])
             with c2:
-                st.write('**NetZero Targets**: `{}`'.format(count_netzeo))
-                st.write('**GHG Targets**: `{}`'.format(count_ghg))
-                st.write('**Economy-wide Targets**: `{}`'.format(count_economy))
+                st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
+                st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
+                
             hits = hits.sort_values(by=['Relevancy'], ascending=False)
             st.write("")
             st.markdown("###### Top few Target Classified paragraph/text results ######")