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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.reader_qa import load_reader, reader_highlight
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 = 'reader'
params = get_classifier_params(classifier_identifier)
def app():
### Main app code ###
with st.container():
if 'key1' in st.session_state:
df = st.session_state.key1
# Load the classifier model
classifier = load_reader(classifier_name=params['model_name'])
st.session_state['{}_qa'.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 = reader_highlight(haystack_doc=df,
threshold= params['threshold'])
st.session_state.key1 = df
# @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 netzero_display():
# if 'key1' in st.session_state:
# df = st.session_state.key2
# hits = df[df['Netzero Label'] == 'NETZERO']
# range_val = min(5,len(hits))
# if range_val !=0:
# count_df = df['Netzero Label'].value_counts()
# count_df = count_df.rename('count')
# count_df = count_df.rename_axis('Netzero Label').reset_index()
# count_df['Label_def'] = count_df['Netzero 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.plotly_chart(fig,use_container_width= True)
# hits = hits.sort_values(by=['Netzero Score'], ascending=False)
# st.write("")
# st.markdown("###### Top few NetZero 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 {}** `page {}` (Relevancy Score: {:.2f})'.format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Netzero Score']))
# st.write("\t Text: \t{}".format(hits.iloc[i]['text']))
# else:
# st.info("🤔 No Netzero target found")
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