appStore/policyaction.py

# 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 utils.policyaction_classifier import load_policyactionClassifier, policyaction_classification
import logging
logger = logging.getLogger(__name__)
from utils.config import get_classifier_params
from utils.preprocessing import paraLengthCheck
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 = 'policyaction'
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='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')

    if 'policy_hits' in st.session_state:
        policy_hits = st.session_state['policy_hits']
        if 'keep' in policy_hits.columns:
            policy_hits = policy_hits[policy_hits.keep == True]
            policy_hits = policy_hits.reset_index(drop=True)
            policy_hits.drop(columns = ['keep'], inplace=True)
            policy_hits.to_excel(writer,index=False,sheet_name = 'Policy')  
        else:
            policy_hits = policy_hits.sort_values(by=['Policies_Plans Score'], ascending=False)
            policy_hits = policy_hits.reset_index(drop=True)
            policy_hits.to_excel(writer,index=False,sheet_name = 'Policy') 
    else:
        policy_hits = df[df['Action Label'] == True]
        policy_hits.drop(columns=['Target Label','Target Score','Netzero Score',
                                'Netzero Label','GHG Label',
                                'GHG Score','Action Label','Policies_Plans Label',
                                'Action Score','Conditional Score'],inplace=True)
        policy_hits = policy_hits.sort_values(by=['Policies_Plans Score'], ascending=False)
        policy_hits = policy_hits.reset_index(drop=True)
        policy_hits.to_excel(writer,index=False,sheet_name = 'Policy') 
            
    # 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 to_excel(df, hits):
#     len_df = len(df)
#     output = BytesIO()
#     writer = pd.ExcelWriter(output, engine='xlsxwriter')
#     df.to_excel(writer, index=False, sheet_name='rawdata')
#     if 'keep' in hits.columns:
#         hits = hits[hits.keep == True]
#         hits = hits.reset_index(drop=True)
#         hits.drop(columns = ['keep'], inplace=True)
#     # hits = hits.drop(columns = ['Target Score','Netzero Score','GHG Score'])
#     hits.to_excel(writer,index=False,sheet_name = 'Action')
#     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 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 action_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['Action Label'] == 'Action']
        range_val = min(5,len(hits))
        if range_val !=0:
            count_action = len(hits)
            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)
            hits = hits.sort_values(by=['Action Score'], ascending=False)
            hits = hits.reset_index(drop=True)

            st.write('----------------')


            st.caption("Filter table to select rows to keep for Action category")
            hits = filter_for_tracs(hits)
            convert_type = {'Conditional Label':'category',
                                                        }
            hits = hits.astype(convert_type)
            filter_dataframe_action(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')

def filter_dataframe_action(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['action_hits'] = df
        return 


    df = df.copy()

    # 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
                                )
        # to_filter_columns = st.multiselect("Filter dataframe on", df.columns)
        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]):
                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.contains(user_text_input)]
        df['keep'] = True
        df = df[['keep','text','Action Score','Conditional Label',
        'Sector Label','Adapt-Mitig Label','Indicator Label','page']]
        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.session_state['action_hits'] = df
        
    return
def filter_dataframe_policy(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['policy_hits'] = df
        return 


    df = df.copy()

    # 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','Adapt-Mitig Label',
                                        'Conditional Label', 'Indicator Label'})
        cols.sort()
        to_filter_columns = st.multiselect("Filter dataframe on", cols
                                )
        # to_filter_columns = st.multiselect("Filter dataframe on", df.columns)
        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]):
                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.contains(user_text_input)]
        df['keep'] = True
        df = df[['keep','text','Policies_Plans Score',
        'Sector Label','page']]
        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.session_state['policy_hits'] = df
        
    return

def policy_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['Policies_Plans Label'] == 'Policy and Plans']
        range_val = min(5,len(hits))
        if range_val !=0:
            count_action = len(hits)
            hits.drop(columns=['Target Label','Target Score','Netzero Score',
                                'Netzero Label','GHG Label',
                                'GHG Score','Action Label','Policies_Plans Label',
                                'Action Score','Conditional Score'],inplace=True)
            hits = hits.sort_values(by=['Policies_Plans Score'], ascending=False)
            hits = hits.reset_index(drop=True)

            st.write('----------------')


            st.caption("Filter table to select rows to keep for Policies and Plans category")
            hits = filter_for_tracs(hits)
            convert_type = {'Conditional Label':'category',
                                                        }
            hits = hits.astype(convert_type)
            filter_dataframe_policy(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')
            #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)
        #     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 {}** : `page {}`, `Sector: {}`,\
        #                     `Indicators: {}`, `Adapt-Mitig :{}`'\
        #             .format(i+1,
        #                     hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
        #                     hits.iloc[i]['Indicator 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.drop(columns = ['Action_check'],inplace=True)
        #     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')

        # else:
        #     st.info("🤔 No Actions found")


# def policy_display():
#     if  'key1' in st.session_state:
#         df = st.session_state.key1
                
        
#         df['Policy_check']  = df['Policy-Action Label'].apply(lambda x: True if 'Policies & Plans' in x else False)
#         hits  = df[df['Policy_check'] == True]
#         # hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
#         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)
#             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 {}** : `page {}`, `Sector: {}`,\
#                             `Indicators: {}`, `Adapt-Mitig :{}`'\
#                     .format(i+1,
#                             hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
#                             hits.iloc[i]['Indicator 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.drop(columns = ['Policy_check'],inplace=True)
#             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')

#         else:
#             st.info("🤔 No Policy/Plans found")