# 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 utils.preprocessing import paraLengthCheck
from io import BytesIO
import xlsxwriter
import plotly.express as px
from utils.target_classifier import label_dict
from appStore.rag import run_query

# Declare all the necessary variables
classifier_identifier = 'target'
params  = get_classifier_params(classifier_identifier)

@st.cache_data
def to_excel(df,sectorlist):
    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('S2:S{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': ['No', 'Yes', 'Discard']})
    worksheet.data_validation('X2:X{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': sectorlist + ['Blank']})
    worksheet.data_validation('T2:T{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': sectorlist + ['Blank']})
    worksheet.data_validation('U2:U{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': sectorlist + ['Blank']})                               
    worksheet.data_validation('V2:V{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': sectorlist + ['Blank']})
    worksheet.data_validation('W2:U{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': sectorlist + ['Blank']})                            
    writer.save()
    processed_data = output.getvalue()
    return processed_data

def app():
    
    ### Main app code ###
    with st.container():
        
        if 'key1' in st.session_state:
           
            # Load the existing dataset
            df = st.session_state.key1

            # Filter out all paragraphs that do not have a reference to groups 
            df = df[df['Vulnerability Label'].apply(lambda x: len(x) > 0 and 'Other' not in x)]

            # Load the classifier model
            classifier = load_targetClassifier(classifier_name=params['model_name'])
         
            st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
                
            df = target_classification(haystack_doc=df,
                                        threshold= params['threshold'])

            # Rename column 
            df.rename(columns={'Target Label': 'Specific action/target/measure mentioned'}, inplace=True)


            st.session_state.key2 = df


def target_display(model_sel_name): 
    
    ### TABLE Output ###

    # Assign dataframe a name
    df = st.session_state['key2']
    st.write(df)

    ### RAG Output by group ##

    # Expand the DataFrame
    df_expand = (
        df.query("`Specific action/target/measure mentioned` == 'YES'")
        .explode('Vulnerability Label')
        )
    # Group by 'Vulnerability Label' and concatenate 'text'
    df_agg = df_expand.groupby('Vulnerability Label')['text'].agg('; '.join).reset_index()

    # st.write(df_agg)

    st.markdown("----")
    st.markdown('**DOCUMENT FINDINGS SUMMARY BY VULNERABILITY LABEL:**')

    # construct RAG query for each label, send to openai and process response
    for i in range(0,len(df_agg)):
        st.write(df_agg['Vulnerability Label'].iloc[i])
        run_query(context = df_agg['text'].iloc[i], label = df_agg['Vulnerability Label'].iloc[i], model_sel_name=model_sel_name)