Update appStore/target.py

appStore/target.py

@@ -27,90 +27,6 @@ from pandas.api.types import (
 classifier_identifier = 'target'
 params  = get_classifier_params(classifier_identifier)
 
-## Labels dictionary ###
-_lab_dict = {
-            'NEGATIVE':'NO TARGET INFO',
-            'TARGET':'TARGET',
-            }
-
-# @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():
@@ -127,272 +43,6 @@ def app():
                 
             df  = target_classification(haystack_doc=df,
                                     threshold= params['threshold'])
-            st.dataframe(df)
+            st.write(df)
             st.session_state.key1 = df
 
-def filter_for_tracs(df):
-    sector_list = ['Transport','Energy','Economy-wide']
-    df['check'] = df['Sector Label'].apply(lambda x: any(i in x for i in sector_list))
-    df = df[df.check == True].reset_index(drop=True)
-    df['Sector Label'] = df['Sector Label'].apply(lambda x: [i for i in x if i in sector_list])
-    df.drop(columns = ['check'],inplace=True)
-    return 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
-
-
-# df = pd.read_csv(
-#     "https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins.csv"
-# )
-
-
-        # else:
-        #     st.info("🤔 No Targets found")
-            # 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])
-                # 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']])
-          # # excel part
-            # temp = df[df['Relevancy']>threshold]
-            
-            # df['Validation'] =  'No'
-            # df_xlsx = to_excel(df)
-            # st.download_button(label='📥 Download Current Result',
-            #                 data=df_xlsx ,
-            #                 file_name= 'file_target.xlsx')