appStore/appStore_target.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 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 = {
            '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():
        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 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')