Update appStore/groups.py

appStore/groups.py

@@ -22,61 +22,15 @@ import plotly.express as px
 classifier_identifier = 'group_classification'
 params  = get_classifier_params(classifier_identifier)
 
-@st.cache_data
-def to_excel(df):
-    df['Target Validation'] = 'No'
-    df['Netzero Validation'] = 'No'
-    df['GHG Validation'] = 'No'
-    df['Adapt-Mitig Validation'] = 'No'
-    df['Sector'] = 'No'
-    len_df = len(df)
-    output = BytesIO()
-    writer = pd.ExcelWriter(output, engine='xlsxwriter')
-    df.to_excel(writer, index=False, sheet_name='Sheet1')
-    workbook = writer.book
-    worksheet = writer.sheets['Sheet1']
-    worksheet.data_validation('L2:L{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})
-    worksheet.data_validation('M2:L{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})
-    worksheet.data_validation('N2:L{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})
-    worksheet.data_validation('O2:L{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})
-    worksheet.data_validation('P2:L{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})                                                                                                         
-    writer.save()
-    processed_data = output.getvalue()
-    return processed_data
-
 def app():
 
     ### Main app code ###
     with st.container():
-                   
-            if 'key1' in st.session_state:
-                df = st.session_state.key1
-                classifier = load_policyactionClassifier(classifier_name=params['model_name'])
-                st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
-
-                if sum(df['Target Label'] == 'TARGET') > 100:
-                    warning_msg = ": This might take sometime, please sit back and relax."
-                else:
-                    warning_msg = ""
-                    
-                df = policyaction_classification(haystack_doc=df,
-                                            threshold= params['threshold'])
-
-                st.session_state.key1 = df
 
-
-
-def action_display():
+        # Classify groups
+        df = group_classification(haystack_doc=df, threshold= params['threshold'])
+        
+def groups_display():
     if  'key1' in st.session_state:
         df = st.session_state.key1
                 
@@ -87,46 +41,7 @@ def action_display():
         range_val = min(5,len(hits))
         if range_val !=0:
             count_action = len(hits)
-            #count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
-            #count_ghg = sum(hits['GHG Label'] == 'GHG')
-            #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('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
-       #         st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
-        #    st.write('-------------------')    
-    #        hits = hits.sort_values(by=['Relevancy'], ascending=False)
-     #       netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
-      #      if not netzerohit.empty:
-       #         netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
-        #        # st.write('-------------------')
-                # st.markdown("###### Netzero paragraph ######")
-      #          st.write('**Netzero paragraph** `page {}`: {}'.format(netzerohit.iloc[0]['page'],
-      #                          netzerohit.iloc[0]['text'].replace("\n", " ")))                        
-       #         st.write("")
-        #    else:
-         #       st.info("🤔 No Netzero paragraph found")
 
-            # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
-           # st.write('-------------------')
             st.write("")
             st.markdown("###### Top few Action Classified paragraph/text results from list of {} classified paragraphs ######".format(count_action))
             st.markdown("""<hr style="height:10px;border:none;color:#097969;background-color:#097969;" /> """, unsafe_allow_html=True)
@@ -157,7 +72,7 @@ def action_display():
             st.info("🤔 No Actions found")
 
 
-def policy_display():
+def groups_display():
     if  'key1' in st.session_state:
         df = st.session_state.key1
                 
@@ -168,46 +83,7 @@ def policy_display():
         range_val = min(5,len(hits))
         if range_val !=0:
             count_policy = len(hits)
-            #count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
-            #count_ghg = sum(hits['GHG Label'] == 'GHG')
-            #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('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
-       #         st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
-        #    st.write('-------------------')    
-    #        hits = hits.sort_values(by=['Relevancy'], ascending=False)
-     #       netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
-      #      if not netzerohit.empty:
-       #         netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
-        #        # st.write('-------------------')
-                # st.markdown("###### Netzero paragraph ######")
-      #          st.write('**Netzero paragraph** `page {}`: {}'.format(netzerohit.iloc[0]['page'],
-      #                          netzerohit.iloc[0]['text'].replace("\n", " ")))                        
-       #         st.write("")
-        #    else:
-         #       st.info("🤔 No Netzero paragraph found")
-
-            # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
-           # st.write('-------------------')
+    
             st.write("")
             st.markdown("###### Top few Policy/Plans Classified paragraph/text results from list of {} classified paragraphs ######".format(count_policy))
             st.markdown("""<hr style="height:10px;border:none;color:#097969;background-color:#097969;" /> """, unsafe_allow_html=True)
@@ -232,7 +108,7 @@ def policy_display():
                 st.write('-------------')
                 st.download_button(label='📥 Download Result',
                             data=df_xlsx ,
-                            file_name= 'cpu_analysis.xlsx')
+                            file_name= 'vulnerable_groups.xlsx')
 
         else:
-            st.info("🤔 No Policy/Plans found")
+            st.info("🤔 No Groups found")