File size: 11,817 Bytes
1e0e995 a64ae04 1e0e995 a64ae04 1e0e995 |
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 |
# 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 utils.policyaction_classifier import load_policyactionClassifier, policyaction_classification
import logging
logger = logging.getLogger(__name__)
from utils.config import get_classifier_params
from utils.preprocessing import paraLengthCheck
from io import BytesIO
import xlsxwriter
import plotly.express as px
# Declare all the necessary variables
classifier_identifier = 'policyaction'
params = get_classifier_params(classifier_identifier)
@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='Sheet1')
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 'key1' in st.session_state:
df = st.session_state.key1
classifier = load_policyactionClassifier(classifier_name=params['model_name'])
st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
if sum(df['Target Label'] == 'TARGET') > 100:
warning_msg = ": This might take sometime, please sit back and relax."
else:
warning_msg = ""
df = policyaction_classification(haystack_doc=df,
threshold= params['threshold'])
st.session_state.key1 = df
def action_display():
if 'key1' in st.session_state:
df = st.session_state.key1
df['Action_check'] = df['Policy-Action Label'].apply(lambda x: True if 'Action' in x else False)
hits = df[df['Action_check'] == True]
# hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
range_val = min(5,len(hits))
if range_val !=0:
count_action = len(hits)
#count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
#count_ghg = sum(hits['GHG Label'] == 'GHG')
#count_economy = sum([True if 'Economy-wide' in x else False
# for x in hits['Sector Label']])
# 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])
# fig = px.bar(count_df, y="Label_def", x="count", orientation='h', height=200)
# c1, c2 = st.columns([1,1])
# with c1:
# st.write('**Target Paragraphs**: `{}`'.format(count_target))
# st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
#
# # 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']])
# with c2:
# st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
# st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
# st.write('-------------------')
# hits = hits.sort_values(by=['Relevancy'], ascending=False)
# 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.write("")
st.markdown("###### Top few Action Classified paragraph/text results from list of {} classified paragraphs ######".format(count_action))
st.markdown("""<hr style="height:10px;border:none;color:#097969;background-color:#097969;" /> """, unsafe_allow_html=True)
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 {}** : `page {}`, `Sector: {}`,\
`Indicators: {}`, `Adapt-Mitig :{}`'\
.format(i+1,
hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
hits.iloc[i]['Indicator 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.write('Explore the data')
st.write(hits)
df.drop(columns = ['Action_check'],inplace=True)
df_xlsx = to_excel(df)
with st.sidebar:
st.write('-------------')
st.download_button(label='📥 Download Result',
data=df_xlsx ,
file_name= os.path.splitext(st.session_state['filename'])[0]+'.xlsx')
else:
st.info("🤔 No Actions found")
def policy_display():
if 'key1' in st.session_state:
df = st.session_state.key1
df['Policy_check'] = df['Policy-Action Label'].apply(lambda x: True if 'Policies & Plans' in x else False)
hits = df[df['Policy_check'] == True]
# hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
range_val = min(5,len(hits))
if range_val !=0:
count_policy = len(hits)
#count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
#count_ghg = sum(hits['GHG Label'] == 'GHG')
#count_economy = sum([True if 'Economy-wide' in x else False
# for x in hits['Sector Label']])
# 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])
# fig = px.bar(count_df, y="Label_def", x="count", orientation='h', height=200)
# c1, c2 = st.columns([1,1])
# with c1:
# st.write('**Target Paragraphs**: `{}`'.format(count_target))
# st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
#
# # 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']])
# with c2:
# st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
# st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
# st.write('-------------------')
# hits = hits.sort_values(by=['Relevancy'], ascending=False)
# 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.write("")
st.markdown("###### Top few Policy/Plans Classified paragraph/text results from list of {} classified paragraphs ######".format(count_policy))
st.markdown("""<hr style="height:10px;border:none;color:#097969;background-color:#097969;" /> """, unsafe_allow_html=True)
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 {}** : `page {}`, `Sector: {}`,\
`Indicators: {}`, `Adapt-Mitig :{}`'\
.format(i+1,
hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
hits.iloc[i]['Indicator 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.write('Explore the data')
st.write(hits)
df.drop(columns = ['Policy_check'],inplace=True)
df_xlsx = to_excel(df)
with st.sidebar:
st.write('-------------')
st.download_button(label='📥 Download Result',
data=df_xlsx ,
file_name= os.path.splitext(st.session_state['filename'])[0]+'.xlsx')
else:
st.info("🤔 No Policy/Plans found") |