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# 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.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
# 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):
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('E2:E{}'.format(len_df),
{'validate': 'list',
'source': ['No', 'Yes', 'Discard']})
writer.save()
processed_data = output.getvalue()
return processed_data
def app():
#### APP INFO #####
# st.write(
# """
# The **Target Extraction** app is an easy-to-use interface built \
# in Streamlit for analyzing policy documents for \
# Classification of the paragraphs/texts in the document *If it \
# contains any Economy-Wide Targets related information* - \
# developed by GIZ Data Service Center, GFA, IKI Tracs, \
# SV Klima and SPA. \n
# """)
### 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
# # 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')
def target_display():
if 'key1' in st.session_state:
df = st.session_state.key1
hits = df[df['Target Label'] == 'TARGET']
_lab_dict = {
'LABEL_0':'NEGATIVE',
'LABEL_1':'NOT GHG',
'LABEL_2':'GHG',
}
hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
range_val = min(5,len(hits))
if range_val !=0:
count_target = sum(hits['Target Label'] == 'TARGET')
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']])
# 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))
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.markdown("###### Netzero paragraph ######")
st.write('** Text `page {}`: {}'.format(netzerohit.iloc[i]['page'],
netzerohit.iloc[i]['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.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(hits)
else:
st.info("🤔 No Targets found") |