# # 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