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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.groups_classifier import load_groupsClassifier, groups_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 = 'group_classification'
params = get_classifier_params(classifier_identifier)
def app():
### Main app code ###
with st.container():
# Classify groups
df = group_classification(haystack_doc=df, threshold= params['threshold'])
def groups_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)
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= 'cpu_analysis.xlsx')
else:
st.info("🤔 No Actions found")
def groups_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)
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= 'vulnerable_groups.xlsx')
else:
st.info("🤔 No Groups found")
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