Update appStore/target.py

appStore/target.py

@@ -1,368 +1,368 @@
-# set path
-import glob, os, sys; 
-sys.path.append('../utils')
-
-#import needed libraries
-import seaborn as sns
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import streamlit as st
-from st_aggrid import AgGrid
-from utils.target_classifier import load_targetClassifier, target_classification 
-import logging
-logger = logging.getLogger(__name__)
-from utils.config import get_classifier_params
-from io import BytesIO
-import xlsxwriter
-import plotly.express as px
-from pandas.api.types import (
-    is_categorical_dtype,
-    is_datetime64_any_dtype,
-    is_numeric_dtype,
-    is_object_dtype,
-    is_list_like)
-
-# Declare all the necessary variables
-classifier_identifier = 'target'
-params  = get_classifier_params(classifier_identifier)
-
-## Labels dictionary ###
-_lab_dict = {
-            '0':'NO',
-            '1':'YES',
-            }
-
-# # @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='rawdata')
-#     if 'target_hits' in st.session_state:
-#         target_hits = st.session_state['target_hits']
-#         if 'keep' in target_hits.columns:
-
-#             target_hits = target_hits[target_hits.keep == True]
-#             target_hits = target_hits.reset_index(drop=True)
-#             target_hits.drop(columns = ['keep'], inplace=True)
-#             target_hits.to_excel(writer,index=False,sheet_name = 'Target')
-#         else:
-
-#             target_hits = target_hits.sort_values(by=['Target Score'], ascending=False)
-#             target_hits = target_hits.reset_index(drop=True)
-#             target_hits.to_excel(writer,index=False,sheet_name = 'Target')
-
-#     else:
-#         target_hits = df[df['Target Label'] == True]
-#         target_hits.drop(columns=['Target Label','Netzero Score','GHG Score','Action Label',
-#                                 'Action Score','Policies_Plans Label','Indicator Label',
-#                                 'Policies_Plans Score','Conditional Score'],inplace=True)
-#         target_hits = target_hits.sort_values(by=['Target Score'], ascending=False)
-#         target_hits = target_hits.reset_index(drop=True)
-#         target_hits.to_excel(writer,index=False,sheet_name = 'Target')
-
-
-#     if 'action_hits' in st.session_state:
-#         action_hits = st.session_state['action_hits']
-#         if 'keep' in action_hits.columns:
-#             action_hits = action_hits[action_hits.keep == True]
-#             action_hits = action_hits.reset_index(drop=True)
-#             action_hits.drop(columns = ['keep'], inplace=True)
-#             action_hits.to_excel(writer,index=False,sheet_name = 'Action')  
-#         else:
-#             action_hits = action_hits.sort_values(by=['Action Score'], ascending=False)
-#             action_hits = action_hits.reset_index(drop=True)
-#             action_hits.to_excel(writer,index=False,sheet_name = 'Action') 
-#     else:
-#         action_hits = df[df['Action Label'] == True]
-#         action_hits.drop(columns=['Target Label','Target Score','Netzero Score',
-#                                 'Netzero Label','GHG Label',
-#                                 'GHG Score','Action Label','Policies_Plans Label',
-#                                 'Policies_Plans Score','Conditional Score'],inplace=True)
-#         action_hits = action_hits.sort_values(by=['Action Score'], ascending=False)
-#         action_hits = action_hits.reset_index(drop=True)
-#         action_hits.to_excel(writer,index=False,sheet_name = 'Action') 
+# # set path
+# import glob, os, sys; 
+# sys.path.append('../utils')
+
+# #import needed libraries
+# import seaborn as sns
+# import matplotlib.pyplot as plt
+# import numpy as np
+# import pandas as pd
+# import streamlit as st
+# from st_aggrid import AgGrid
+# from utils.target_classifier import load_targetClassifier, target_classification 
+# import logging
+# logger = logging.getLogger(__name__)
+# from utils.config import get_classifier_params
+# from io import BytesIO
+# import xlsxwriter
+# import plotly.express as px
+# from pandas.api.types import (
+#     is_categorical_dtype,
+#     is_datetime64_any_dtype,
+#     is_numeric_dtype,
+#     is_object_dtype,
+#     is_list_like)
+
+# # Declare all the necessary variables
+# classifier_identifier = 'target'
+# params  = get_classifier_params(classifier_identifier)
+
+# ## Labels dictionary ###
+# _lab_dict = {
+#             '0':'NO',
+#             '1':'YES',
+#             }
+
+# # # @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='rawdata')
+# #     if 'target_hits' in st.session_state:
+# #         target_hits = st.session_state['target_hits']
+# #         if 'keep' in target_hits.columns:
+
+# #             target_hits = target_hits[target_hits.keep == True]
+# #             target_hits = target_hits.reset_index(drop=True)
+# #             target_hits.drop(columns = ['keep'], inplace=True)
+# #             target_hits.to_excel(writer,index=False,sheet_name = 'Target')
+# #         else:
+
+# #             target_hits = target_hits.sort_values(by=['Target Score'], ascending=False)
+# #             target_hits = target_hits.reset_index(drop=True)
+# #             target_hits.to_excel(writer,index=False,sheet_name = 'Target')
+
+# #     else:
+# #         target_hits = df[df['Target Label'] == True]
+# #         target_hits.drop(columns=['Target Label','Netzero Score','GHG Score','Action Label',
+# #                                 'Action Score','Policies_Plans Label','Indicator Label',
+# #                                 'Policies_Plans Score','Conditional Score'],inplace=True)
+# #         target_hits = target_hits.sort_values(by=['Target Score'], ascending=False)
+# #         target_hits = target_hits.reset_index(drop=True)
+# #         target_hits.to_excel(writer,index=False,sheet_name = 'Target')
+
+
+# #     if 'action_hits' in st.session_state:
+# #         action_hits = st.session_state['action_hits']
+# #         if 'keep' in action_hits.columns:
+# #             action_hits = action_hits[action_hits.keep == True]
+# #             action_hits = action_hits.reset_index(drop=True)
+# #             action_hits.drop(columns = ['keep'], inplace=True)
+# #             action_hits.to_excel(writer,index=False,sheet_name = 'Action')  
+# #         else:
+# #             action_hits = action_hits.sort_values(by=['Action Score'], ascending=False)
+# #             action_hits = action_hits.reset_index(drop=True)
+# #             action_hits.to_excel(writer,index=False,sheet_name = 'Action') 
+# #     else:
+# #         action_hits = df[df['Action Label'] == True]
+# #         action_hits.drop(columns=['Target Label','Target Score','Netzero Score',
+# #                                 'Netzero Label','GHG Label',
+# #                                 'GHG Score','Action Label','Policies_Plans Label',
+# #                                 'Policies_Plans Score','Conditional Score'],inplace=True)
+# #         action_hits = action_hits.sort_values(by=['Action Score'], ascending=False)
+# #         action_hits = action_hits.reset_index(drop=True)
+# #         action_hits.to_excel(writer,index=False,sheet_name = 'Action') 
             
-#     # hits = hits.drop(columns = ['Target Score','Netzero Score','GHG Score'])
-#     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():
+# #     # hits = hits.drop(columns = ['Target Score','Netzero Score','GHG Score'])
+# #     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 'key0' in st.session_state:
-            df = st.session_state.key0
-
-            #load Classifier
-            classifier = load_targetClassifier(classifier_name=params['model_name'])
-            st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
-            if len(df) > 100:
-                warning_msg = ": This might take sometime, please sit back and relax."
-            else:
-                warning_msg = ""
+#     ### Main app code ###
+#     with st.container():
+#         if 'key0' in st.session_state:
+#             df = st.session_state.key0
+
+#             #load Classifier
+#             classifier = load_targetClassifier(classifier_name=params['model_name'])
+#             st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
+#             if len(df) > 100:
+#                 warning_msg = ": This might take sometime, please sit back and relax."
+#             else:
+#                 warning_msg = ""
                 
-            df  = target_classification(haystack_doc=df,
-                                    threshold= params['threshold'])
-            st.session_state.key1 = df
+#             df  = target_classification(haystack_doc=df,
+#                                     threshold= params['threshold'])
+#             st.session_state.key1 = df
 
 
-# def target_display():
+# # def target_display():
 
-#     if  'key1' in st.session_state:
-#         df = st.session_state.key1   
-#         st.caption(""" **{}** is splitted into **{}** paragraphs/text chunks."""\
-#                       .format(os.path.basename(st.session_state['filename']),
-#                              len(df)))         
-#         hits  = df[df['Target Label'] == 'TARGET'].reset_index(drop=True)
-#         range_val = min(5,len(hits))
-#         if range_val !=0:
+# #     if  'key1' in st.session_state:
+# #         df = st.session_state.key1   
+# #         st.caption(""" **{}** is splitted into **{}** paragraphs/text chunks."""\
+# #                       .format(os.path.basename(st.session_state['filename']),
+# #                              len(df)))         
+# #         hits  = df[df['Target Label'] == 'TARGET'].reset_index(drop=True)
+# #         range_val = min(5,len(hits))
+# #         if range_val !=0:
             
-#             # collecting some statistics
-#             count_target = sum(hits['Target Label'] == 'TARGET')
-#             count_netzero = sum(hits['Netzero Label'] == 'NETZERO TARGET')
-#             count_ghg = sum(hits['GHG Label'] == 'GHG')
-#             count_transport = sum([True if 'Transport' in x else False 
-#                               for x in hits['Sector Label']])
-
-#             c1, c2 = st.columns([1,1])
-#             with c1:
-#                 st.write('**Target Paragraphs**: `{}`'.format(count_target))
-#                 st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
-#             with c2:
-#                 st.write('**GHG Target Related Paragraphs**: `{}`'.format(count_ghg))
-#                 st.write('**Transport Related Paragraphs**: `{}`'.format(count_transport))
-#             # st.write('-------------------')    
-#             hits.drop(columns=['Target Label','Netzero Score','GHG Score','Action Label',
-#                                 'Action Score','Policies_Plans Label','Indicator Label',
-#                                 'Policies_Plans Score','Conditional Score'],inplace=True)
-#             hits = hits.sort_values(by=['Target Score'], ascending=False)
-#             hits = hits.reset_index(drop=True)
-
-#             # 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.caption("Filter table to select rows to keep for Target category")
-            hits = filter_for_tracs(hits)
-            convert_type = {'Netzero Label': 'category',
-                            'Conditional Label':'category',
-                            'GHG Label':'category',
-                            }
-            hits = hits.astype(convert_type)
-            filter_dataframe(hits)
+# #             # collecting some statistics
+# #             count_target = sum(hits['Target Label'] == 'TARGET')
+# #             count_netzero = sum(hits['Netzero Label'] == 'NETZERO TARGET')
+# #             count_ghg = sum(hits['GHG Label'] == 'GHG')
+# #             count_transport = sum([True if 'Transport' in x else False 
+# #                               for x in hits['Sector Label']])
+
+# #             c1, c2 = st.columns([1,1])
+# #             with c1:
+# #                 st.write('**Target Paragraphs**: `{}`'.format(count_target))
+# #                 st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
+# #             with c2:
+# #                 st.write('**GHG Target Related Paragraphs**: `{}`'.format(count_ghg))
+# #                 st.write('**Transport Related Paragraphs**: `{}`'.format(count_transport))
+# #             # st.write('-------------------')    
+# #             hits.drop(columns=['Target Label','Netzero Score','GHG Score','Action Label',
+# #                                 'Action Score','Policies_Plans Label','Indicator Label',
+# #                                 'Policies_Plans Score','Conditional Score'],inplace=True)
+# #             hits = hits.sort_values(by=['Target Score'], ascending=False)
+# #             hits = hits.reset_index(drop=True)
+
+# #             # 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.caption("Filter table to select rows to keep for Target category")
+#             hits = filter_for_tracs(hits)
+#             convert_type = {'Netzero Label': 'category',
+#                             'Conditional Label':'category',
+#                             'GHG Label':'category',
+#                             }
+#             hits = hits.astype(convert_type)
+#             filter_dataframe(hits)
             
-            # filtered_df = filtered_df[filtered_df.keep == True]
-            # st.write('Explore the data')
-            # AgGrid(hits)
+#             # filtered_df = filtered_df[filtered_df.keep == True]
+#             # st.write('Explore the data')
+#             # AgGrid(hits)
             
             
-            with st.sidebar:
-                st.write('-------------')
-                df_xlsx = to_excel(df)
-                st.download_button(label='📥 Download Result',
-                            data=df_xlsx ,
-                            file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx')
-
-# st.write(
-#     """This app accomodates the blog [here](https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/)
-#     and walks you through one example of how the Streamlit
-#     Data Science Team builds add-on functions to Streamlit.
-#     """
-# )
-
-
-# def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
-#     """
-#     Adds a UI on top of a dataframe to let viewers filter columns
-
-#     Args:
-#         df (pd.DataFrame): Original dataframe
-
-#     Returns:
-#         pd.DataFrame: Filtered dataframe
-#     """
-#     modify = st.checkbox("Add filters")
-
-#     if not modify:
-#         st.session_state['target_hits'] = df
-#         return 
-
-
-    # # df = df.copy()
-    # # st.write(len(df))
-
-    # # Try to convert datetimes into a standard format (datetime, no timezone)
-    # # for col in df.columns:
-    # #     if is_object_dtype(df[col]):
-    # #         try:
-    # #             df[col] = pd.to_datetime(df[col])
-    # #         except Exception:
-    # #             pass
-
-    # #     if is_datetime64_any_dtype(df[col]):
-    # #         df[col] = df[col].dt.tz_localize(None)
-
-    # modification_container = st.container()
-
-    # with modification_container:
-    #     cols = list(set(df.columns) -{'page','Extracted Text'})
-    #     cols.sort()
-    #     to_filter_columns = st.multiselect("Filter dataframe on", cols
-    #                             )
-    #     for column in to_filter_columns:
-    #         left, right = st.columns((1, 20))
-    #         left.write("↳")
-    #         # Treat columns with < 10 unique values as categorical
-    #         if is_categorical_dtype(df[column]):
-    #             # st.write(type(df[column][0]), column)
-    #             user_cat_input = right.multiselect(
-    #                 f"Values for {column}",
-    #                 df[column].unique(),
-    #                 default=list(df[column].unique()),
-    #             )
-    #             df = df[df[column].isin(user_cat_input)]
-    #         elif is_numeric_dtype(df[column]):
-    #             _min = float(df[column].min())
-    #             _max = float(df[column].max())
-    #             step = (_max - _min) / 100
-    #             user_num_input = right.slider(
-    #                 f"Values for {column}",
-    #                 _min,
-    #                 _max,
-    #                 (_min, _max),
-    #                 step=step,
-    #             )
-    #             df = df[df[column].between(*user_num_input)]
-    #         elif is_list_like(df[column]) & (type(df[column][0]) == list) :
-    #             list_vals = set(x for lst in df[column].tolist() for x in lst)
-    #             user_multi_input = right.multiselect(
-    #                 f"Values for {column}",
-    #                 list_vals,
-    #                 default=list_vals,
-    #             )   
-    #             df['check'] = df[column].apply(lambda x: any(i in x for i in user_multi_input))
-    #             df = df[df.check == True]
-    #             df.drop(columns = ['check'],inplace=True)
+#             with st.sidebar:
+#                 st.write('-------------')
+#                 df_xlsx = to_excel(df)
+#                 st.download_button(label='📥 Download Result',
+#                             data=df_xlsx ,
+#                             file_name= os.path.splitext(os.path.basename(st.session_state['filename']))[0]+'.xlsx')
+
+# # st.write(
+# #     """This app accomodates the blog [here](https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/)
+# #     and walks you through one example of how the Streamlit
+# #     Data Science Team builds add-on functions to Streamlit.
+# #     """
+# # )
+
+
+# # def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+# #     """
+# #     Adds a UI on top of a dataframe to let viewers filter columns
+
+# #     Args:
+# #         df (pd.DataFrame): Original dataframe
+
+# #     Returns:
+# #         pd.DataFrame: Filtered dataframe
+# #     """
+# #     modify = st.checkbox("Add filters")
+
+# #     if not modify:
+# #         st.session_state['target_hits'] = df
+# #         return 
+
+
+#     # # df = df.copy()
+#     # # st.write(len(df))
+
+#     # # Try to convert datetimes into a standard format (datetime, no timezone)
+#     # # for col in df.columns:
+#     # #     if is_object_dtype(df[col]):
+#     # #         try:
+#     # #             df[col] = pd.to_datetime(df[col])
+#     # #         except Exception:
+#     # #             pass
+
+#     # #     if is_datetime64_any_dtype(df[col]):
+#     # #         df[col] = df[col].dt.tz_localize(None)
+
+#     # modification_container = st.container()
+
+#     # with modification_container:
+#     #     cols = list(set(df.columns) -{'page','Extracted Text'})
+#     #     cols.sort()
+#     #     to_filter_columns = st.multiselect("Filter dataframe on", cols
+#     #                             )
+#     #     for column in to_filter_columns:
+#     #         left, right = st.columns((1, 20))
+#     #         left.write("↳")
+#     #         # Treat columns with < 10 unique values as categorical
+#     #         if is_categorical_dtype(df[column]):
+#     #             # st.write(type(df[column][0]), column)
+#     #             user_cat_input = right.multiselect(
+#     #                 f"Values for {column}",
+#     #                 df[column].unique(),
+#     #                 default=list(df[column].unique()),
+#     #             )
+#     #             df = df[df[column].isin(user_cat_input)]
+#     #         elif is_numeric_dtype(df[column]):
+#     #             _min = float(df[column].min())
+#     #             _max = float(df[column].max())
+#     #             step = (_max - _min) / 100
+#     #             user_num_input = right.slider(
+#     #                 f"Values for {column}",
+#     #                 _min,
+#     #                 _max,
+#     #                 (_min, _max),
+#     #                 step=step,
+#     #             )
+#     #             df = df[df[column].between(*user_num_input)]
+#     #         elif is_list_like(df[column]) & (type(df[column][0]) == list) :
+#     #             list_vals = set(x for lst in df[column].tolist() for x in lst)
+#     #             user_multi_input = right.multiselect(
+#     #                 f"Values for {column}",
+#     #                 list_vals,
+#     #                 default=list_vals,
+#     #             )   
+#     #             df['check'] = df[column].apply(lambda x: any(i in x for i in user_multi_input))
+#     #             df = df[df.check == True]
+#     #             df.drop(columns = ['check'],inplace=True)
             
-    #             # df[df[column].between(*user_num_input)]
-    #         # elif is_datetime64_any_dtype(df[column]):
-    #         #     user_date_input = right.date_input(
-    #         #         f"Values for {column}",
-    #         #         value=(
-    #         #             df[column].min(),
-    #         #             df[column].max(),
-    #         #         ),
-    #         #     )
-    #         #     if len(user_date_input) == 2:
-    #         #         user_date_input = tuple(map(pd.to_datetime, user_date_input))
-    #         #         start_date, end_date = user_date_input
-    #         #         df = df.loc[df[column].between(start_date, end_date)]
-    #         else:
-    #             user_text_input = right.text_input(
-    #                 f"Substring or regex in {column}",
-    #             )
-    #             if user_text_input:
-    #                 df = df[df[column].str.lower().str.contains(user_text_input)]
+#     #             # df[df[column].between(*user_num_input)]
+#     #         # elif is_datetime64_any_dtype(df[column]):
+#     #         #     user_date_input = right.date_input(
+#     #         #         f"Values for {column}",
+#     #         #         value=(
+#     #         #             df[column].min(),
+#     #         #             df[column].max(),
+#     #         #         ),
+#     #         #     )
+#     #         #     if len(user_date_input) == 2:
+#     #         #         user_date_input = tuple(map(pd.to_datetime, user_date_input))
+#     #         #         start_date, end_date = user_date_input
+#     #         #         df = df.loc[df[column].between(start_date, end_date)]
+#     #         else:
+#     #             user_text_input = right.text_input(
+#     #                 f"Substring or regex in {column}",
+#     #             )
+#     #             if user_text_input:
+#     #                 df = df[df[column].str.lower().str.contains(user_text_input)]
             
-    #         df = df.reset_index(drop=True)
+#     #         df = df.reset_index(drop=True)
         
-    #     st.session_state['target_hits'] = df
-    #     df['IKI_Netzero'] = df.apply(lambda x: 'T_NETZERO' if ((x['Netzero Label'] == 'NETZERO TARGET') & 
-    #                           (x['Conditional Label'] == 'UNCONDITIONAL'))
-    #                           else 'T_NETZERO_C' if ((x['Netzero Label'] == 'NETZERO TARGET') & 
-    #                           (x['Conditional Label'] == 'CONDITIONAL')
-    #                           )
-    #                           else None, axis=1
-    #                           )
-    #     def check_t(s,c):
-    #         temp = []
-    #         if (('Transport' in s) & (c== 'UNCONDITIONAL')):
-    #             temp.append('T_Transport_Unc')
-    #         if (('Transport' in s) & (c == 'CONDITIONAL')):
-    #             temp.append('T_Transport_C')
-    #         if (('Economy-wide' in s) & (c == 'CONDITIONAL')):
-    #             temp.append('T_Economy_C')
-    #         if (('Economy-wide' in s) & (c == 'UNCONDITIONAL')):
-    #             temp.append('T_Economy_Unc')
-    #         if (('Energy' in s) & (c == 'CONDITIONAL')):
-    #             temp.append('T_Energy_C')
-    #         if (('Energy' in s) & (c == 'UNCONDITIONAL')):
-    #             temp.append('T_Economy_Unc')
-    #         return temp
-    #     df['IKI_Target'] = df.apply(lambda x:check_t(x['Sector Label'], x['Conditional Label']),
-    #                                     axis=1 )
-
-    #     #  target_hits = st.session_state['target_hits']
-    #     df['keep'] = True
-
-
-    #     df = df[['text','IKI_Netzero','IKI_Target','Target Score','Netzero Label','GHG Label',
-    #         'Conditional Label','Sector Label','Adapt-Mitig Label','page','keep']]
-    #     st.dataframe(df)
-    #     # df = st.data_editor(
-    #     #           df,
-    #     #           column_config={
-    #     #               "keep": st.column_config.CheckboxColumn(
-    #     #                   help="Select which rows to keep",
-    #     #                   default=False,
-    #     #               )
-    #     #           },
-    #     #           disabled=list(set(df.columns) - {'keep'}),
-    #     #           hide_index=True,
-    #     #             )
-    #     # st.write("updating target hits....")
-    #     # st.write(len(df[df.keep == True]))
-    #     st.session_state['target_hits'] = df
+#     #     st.session_state['target_hits'] = df
+#     #     df['IKI_Netzero'] = df.apply(lambda x: 'T_NETZERO' if ((x['Netzero Label'] == 'NETZERO TARGET') & 
+#     #                           (x['Conditional Label'] == 'UNCONDITIONAL'))
+#     #                           else 'T_NETZERO_C' if ((x['Netzero Label'] == 'NETZERO TARGET') & 
+#     #                           (x['Conditional Label'] == 'CONDITIONAL')
+#     #                           )
+#     #                           else None, axis=1
+#     #                           )
+#     #     def check_t(s,c):
+#     #         temp = []
+#     #         if (('Transport' in s) & (c== 'UNCONDITIONAL')):
+#     #             temp.append('T_Transport_Unc')
+#     #         if (('Transport' in s) & (c == 'CONDITIONAL')):
+#     #             temp.append('T_Transport_C')
+#     #         if (('Economy-wide' in s) & (c == 'CONDITIONAL')):
+#     #             temp.append('T_Economy_C')
+#     #         if (('Economy-wide' in s) & (c == 'UNCONDITIONAL')):
+#     #             temp.append('T_Economy_Unc')
+#     #         if (('Energy' in s) & (c == 'CONDITIONAL')):
+#     #             temp.append('T_Energy_C')
+#     #         if (('Energy' in s) & (c == 'UNCONDITIONAL')):
+#     #             temp.append('T_Economy_Unc')
+#     #         return temp
+#     #     df['IKI_Target'] = df.apply(lambda x:check_t(x['Sector Label'], x['Conditional Label']),
+#     #                                     axis=1 )
+
+#     #     #  target_hits = st.session_state['target_hits']
+#     #     df['keep'] = True
+
+
+#     #     df = df[['text','IKI_Netzero','IKI_Target','Target Score','Netzero Label','GHG Label',
+#     #         'Conditional Label','Sector Label','Adapt-Mitig Label','page','keep']]
+#     #     st.dataframe(df)
+#     #     # df = st.data_editor(
+#     #     #           df,
+#     #     #           column_config={
+#     #     #               "keep": st.column_config.CheckboxColumn(
+#     #     #                   help="Select which rows to keep",
+#     #     #                   default=False,
+#     #     #               )
+#     #     #           },
+#     #     #           disabled=list(set(df.columns) - {'keep'}),
+#     #     #           hide_index=True,
+#     #     #             )
+#     #     # st.write("updating target hits....")
+#     #     # st.write(len(df[df.keep == True]))
+#     #     st.session_state['target_hits'] = df
         
-    # return
+#     # return