add conditionality

app.py

@@ -61,7 +61,7 @@ with st.expander("ℹ️ - About this app", expanded=False):
                 wide scale. """)               
     st.write("")
 apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
-        policyaction.app]#, indicator.app]
+        policyaction.app, conditional.app, sector.app]#, indicator.app]
  #conditional.app, sector.app]
         #adapmit.app]
          

utils/conditional_classifier.py

@@ -71,10 +71,15 @@ def conditional_classification(haystack_doc:pd.DataFrame,
     logging.info("Working on Conditionality Identification")
     haystack_doc['Conditional Label'] = 'NA'
     haystack_doc['Conditional Score'] = 'NA'
+    haystack_doc['cond_check'] = False
+    haystack_doc['cond_check'] = haystack_doc.apply(lambda x: True if (
+                (x['Target Label'] == 'TARGET') | (x['Action Label'] == 'Action') |
+                (x['Policies_Plans Label'] == 'Policies and Plans')) else
+                False, axis=1)
     # we apply Netzero to only paragraphs which are classified as 'Target' related
-    temp = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
+    temp = haystack_doc[haystack_doc['cond_check'] == True]
     temp = temp.reset_index(drop=True)
-    df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
+    df = haystack_doc[haystack_doc['cond_check'] == False]
     df = df.reset_index(drop=True)
 
     if not classifier_model:

utils/policyaction_classifier.py

@@ -88,7 +88,7 @@ def policyaction_classification(haystack_doc:pd.DataFrame,
     truth_df = truth_df.astype(str)
     truth_df.rename(columns = {'Action':'Action Label','Policies & Plans':'Policies_Plans Label'},
                                  inplace =True)
-    st.dataframe(truth_df)                              
+    # st.dataframe(truth_df)                              
     truth_df['Action Label'] = truth_df['Action Label'].apply(lambda x: 'Action' if x == 'True'
                                                           else 'NEGATIVE')
     truth_df['Policies_Plans Label'] = truth_df['Policies_Plans Label'].apply(lambda x: