target check

appStore/target.py

@@ -104,72 +104,73 @@ def target_display():
                 
         
         hits  = df[df['Target Label'] == 'TARGET']
-        # hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
-        range_val = min(5,len(hits))
-        if range_val !=0:
-            count_target = sum(hits['Target Label'] == 'TARGET')
-            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']])
+        st.table(hits)
+        # # hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
+        # range_val = min(5,len(hits))
+        # if range_val !=0:
+        #     count_target = sum(hits['Target Label'] == 'TARGET')
+        #     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])
+        #     # 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))
+        #     # 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)
+        #         # 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")
+        #     # 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.markdown("###### Top few Target Classified paragraph/text results ######")
-            range_val = min(5,len(hits))
-            for i in range(range_val):
-                # the page number reflects the page that contains the main paragraph 
-                # according to split limit, the overlapping part can be on a separate page
-                st.write('**Result {}** (Relevancy Score: {:.2f}): `page {}`, `Sector: {}`,\
-                            `GHG: {}`, `Adapt-Mitig :{}`'\
-                    .format(i+1,hits.iloc[i]['Relevancy'],
-                            hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
-                            hits.iloc[i]['GHG Label'],hits.iloc[i]['Adapt-Mitig Label']))                        
-                st.write("\t Text: \t{}".format(hits.iloc[i]['text'].replace("\n", " ")))
-            hits = hits.reset_index(drop =True)
-            st.write('----------------')
-            st.write('Explore the data')
-            st.write(hits)
-            df_xlsx = to_excel(df)
+        #     # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
+        #     st.write('-------------------')
+        #     st.markdown("###### Top few Target Classified paragraph/text results ######")
+        #     range_val = min(5,len(hits))
+        #     for i in range(range_val):
+        #         # the page number reflects the page that contains the main paragraph 
+        #         # according to split limit, the overlapping part can be on a separate page
+        #         st.write('**Result {}** (Relevancy Score: {:.2f}): `page {}`, `Sector: {}`,\
+        #                     `GHG: {}`, `Adapt-Mitig :{}`'\
+        #             .format(i+1,hits.iloc[i]['Relevancy'],
+        #                     hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
+        #                     hits.iloc[i]['GHG Label'],hits.iloc[i]['Adapt-Mitig Label']))                        
+        #         st.write("\t Text: \t{}".format(hits.iloc[i]['text'].replace("\n", " ")))
+        #     hits = hits.reset_index(drop =True)
+        #     st.write('----------------')
+        #     st.write('Explore the data')
+        #     st.write(hits)
+        #     df_xlsx = to_excel(df)
             
-            with st.sidebar:
-                st.write('-------------')
-                st.download_button(label='📥 Download Result',
-                            data=df_xlsx ,
-                            file_name= os.path.splitext(st.session_state['filename'])[0]+'.xlsx')
+        #     with st.sidebar:
+        #         st.write('-------------')
+        #         st.download_button(label='📥 Download Result',
+        #                     data=df_xlsx ,
+        #                     file_name= os.path.splitext(st.session_state['filename'])[0]+'.xlsx')
 
-        else:
-            st.info("🤔 No Targets found")
+        # else:
+        #     st.info("🤔 No Targets found")