# 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