File size: 17,267 Bytes
8f420e0 d43a0b6 8f420e0 bc6302c 8f420e0 d43a0b6 8f420e0 d43a0b6 bc6302c 8f420e0 bc6302c 8f420e0 e10deaa 8f420e0 d43a0b6 8f420e0 d43a0b6 8f420e0 d43a0b6 8f420e0 d43a0b6 8f420e0 d43a0b6 8f420e0 d43a0b6 8f420e0 d43a0b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 |
# # 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
|