Update appStore/target.py

appStore/target.py

@@ -64,11 +64,9 @@ def app():
 
             # Load the classifier model
             classifier = load_targetClassifier(classifier_name=params['model_name'])
-            st.write("classifier")
-            st.write(classifier)
+         
             st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
-            st.write("classifier identifier")
-            st.write(classifier_identifier)
+      
             # test
             if "target_classifier" not in st.session_state: 
                 st.write("target classifier not saved :(")
@@ -84,347 +82,3 @@ def target_display():
     df = st.session_state['key1']
 
     st.write(df)
-
-# # 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():
-    
-#     ### 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
-
-
-# # 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:
-            
-# #             # 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)
-            
-            
-#             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.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
-        
-#     # return
-
-