# 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.netzero_classifier import load_netzeroClassifier, netzero_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 = 'netzero'
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_netzeroClassifier(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 = netzero_classification(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")