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# # 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():
# ### 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