# 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')