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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.adapmit_classifier import load_adapmitClassifier,adapmit_classification
# from utils.keyword_extraction import textrank
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 = 'adapmit'
params = get_classifier_params(classifier_identifier)
@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']})
worksheet.data_validation('F2:F{}'.format(len_df),
{'validate': 'list',
'source': ['No', 'Yes', 'Discard']})
worksheet.data_validation('G2:G{}'.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_adapmitClassifier(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 = adapmit_classification(haystack_doc=df,
threshold= params['threshold'])
st.session_state.key1 = df
# threshold= params['threshold']
# truth_df = df.drop(['text'],axis=1)
# truth_df = truth_df.astype(float) >= threshold
# truth_df = truth_df.astype(str)
# categories = list(truth_df.columns)
# placeholder = {}
# for val in categories:
# placeholder[val] = dict(truth_df[val].value_counts())
# count_df = pd.DataFrame.from_dict(placeholder)
# count_df = count_df.T
# count_df = count_df.reset_index()
# # st.write(count_df)
# placeholder = []
# for i in range(len(count_df)):
# placeholder.append([count_df.iloc[i]['index'],count_df['True'][i],'Yes'])
# placeholder.append([count_df.iloc[i]['index'],count_df['False'][i],'No'])
# count_df = pd.DataFrame(placeholder, columns = ['category','count','truth_value'])
# # st.write("Total Paragraphs: {}".format(len(df)))
# fig = px.bar(count_df, y='category', x='count',
# color='truth_value',orientation='h', height =200)
# c1, c2 = st.columns([1,1])
# with c1:
# st.plotly_chart(fig,use_container_width= True)
# truth_df['labels'] = truth_df.apply(lambda x: {i if x[i]=='True' else None for i in categories}, axis=1)
# truth_df['labels'] = truth_df.apply(lambda x: list(x['labels'] -{None}),axis=1)
# # st.write(truth_df)
# df = pd.concat([df,truth_df['labels']],axis=1)
# st.markdown("###### Top few 'Mitigation' related paragraph/text ######")
# df = df.sort_values(by = ['Mitigation'], ascending=False)
# for i in range(3):
# if df.iloc[i]['Mitigation'] >= 0.50:
# st.write('**Result {}** (Relevancy Score: {:.2f})'.format(i+1,df.iloc[i]['Mitigation']))
# st.write("\t Text: \t{}".format(df.iloc[i]['text'].replace("\n", " ")))
# st.markdown("###### Top few 'Adaptation' related paragraph/text ######")
# df = df.sort_values(by = ['Adaptation'], ascending=False)
# for i in range(3):
# if df.iloc[i]['Adaptation'] > 0.5:
# st.write('**Result {}** (Relevancy Score: {:.2f})'.format(i+1,df.iloc[i]['Adaptation']))
# st.write("\t Text: \t{}".format(df.iloc[i]['text'].replace("\n", " ")))
# # st.write(df[['text','labels']])
# df['Validation'] = 'No'
# df['Val-Mitigation'] = 'No'
# df['Val-Adaptation'] = 'No'
# df_xlsx = to_excel(df)
# st.download_button(label='📥 Download Current Result',
# data=df_xlsx ,
# file_name= 'file_adaptation-mitigation.xlsx')
# # st.session_state.key4 =
# # category =set(df.columns)
# # removecols = {'Validation','Val-Adaptation','Val-Mitigation','text'}
# # category = list(category - removecols)
# else:
# st.info("🤔 No document found, please try to upload it at the sidebar!")
# logging.warning("Terminated as no document provided")
# # Creating truth value dataframe
# if 'key4' in st.session_state:
# if st.session_state.key4 is not None:
# df = st.session_state.key4
# st.markdown("###### Select the threshold for classifier ######")
# c4, c5 = st.columns([1,1])
# with c4:
# threshold = st.slider("Threshold", min_value=0.00, max_value=1.0,
# step=0.01, value=0.5,
# help = "Keep High Value if want refined result, low if dont want to miss anything" )
# category =set(df.columns)
# removecols = {'Validation','Val-Adaptation','Val-Mitigation','text'}
# category = list(category - removecols)
# placeholder = {}
# for val in category:
# temp = df[val].astype(float) > threshold
# temp = temp.astype(str)
# placeholder[val] = dict(temp.value_counts())
# count_df = pd.DataFrame.from_dict(placeholder)
# count_df = count_df.T
# count_df = count_df.reset_index()
# placeholder = []
# for i in range(len(count_df)):
# placeholder.append([count_df.iloc[i]['index'],count_df['False'][i],'False'])
# placeholder.append([count_df.iloc[i]['index'],count_df['True'][i],'True'])
# count_df = pd.DataFrame(placeholder, columns = ['category','count','truth_value'])
# fig = px.bar(count_df, x='category', y='count',
# color='truth_value',
# height=400)
# st.write("")
# st.plotly_chart(fig)
# df['Validation'] = 'No'
# df['Val-Mitigation'] = 'No'
# df['Val-Adaptation'] = 'No'
# df_xlsx = to_excel(df)
# st.download_button(label='📥 Download Current Result',
# data=df_xlsx ,
# file_name= 'file_adaptation-mitigation.xlsx')
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