appStore/groups.py

# 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.group_classifier import load_groupClassifier, group_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)

@st.cache_data
def to_excel(df):
    df['Target Validation'] = 'No'
    df['Netzero Validation'] = 'No'
    df['GHG Validation'] = 'No'
    df['Adapt-Mitig Validation'] = 'No'
    df['Sector'] = 'No'
    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('L2:L{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': ['No', 'Yes', 'Discard']})
    worksheet.data_validation('M2:L{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': ['No', 'Yes', 'Discard']})
    worksheet.data_validation('N2:L{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': ['No', 'Yes', 'Discard']})
    worksheet.data_validation('O2:L{}'.format(len_df), 
                              {'validate': 'list', 
                               'source': ['No', 'Yes', 'Discard']})
    worksheet.data_validation('P2:L{}'.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_policyactionClassifier(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 = policyaction_classification(haystack_doc=df,
                                            threshold= params['threshold'])

                st.session_state.key1 = df



def action_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)
            #count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
            #count_ghg = sum(hits['GHG Label'] == 'GHG')
            #count_economy = sum([True if 'Economy-wide' in x else False 
            #                  for x in hits['Sector Label']])
            
            # count_df = df['Target Label'].value_counts()
            # count_df = count_df.rename('count')
            # count_df = count_df.rename_axis('Target Label').reset_index()
            # count_df['Label_def'] = count_df['Target 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.write('**Target Paragraphs**: `{}`'.format(count_target))
        #        st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
#
 #               # st.plotly_chart(fig,use_container_width= True)
  #          
            # count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
            # count_ghg = sum(hits['GHG Label'] == 'LABEL_2')
            # count_economy = sum([True if 'Economy-wide' in x else False 
            #                   for x in hits['Sector Label']])
     #       with c2:
      #          st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
       #         st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
        #    st.write('-------------------')    
    #        hits = hits.sort_values(by=['Relevancy'], ascending=False)
     #       netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
      #      if not netzerohit.empty:
       #         netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
        #        # st.write('-------------------')
                # st.markdown("###### Netzero paragraph ######")
      #          st.write('**Netzero paragraph** `page {}`: {}'.format(netzerohit.iloc[0]['page'],
      #                          netzerohit.iloc[0]['text'].replace("\n", " ")))                        
       #         st.write("")
        #    else:
         #       st.info("🤔 No Netzero paragraph found")

            # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
           # st.write('-------------------')
            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 policy_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)
            #count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
            #count_ghg = sum(hits['GHG Label'] == 'GHG')
            #count_economy = sum([True if 'Economy-wide' in x else False 
            #                  for x in hits['Sector Label']])
            
            # count_df = df['Target Label'].value_counts()
            # count_df = count_df.rename('count')
            # count_df = count_df.rename_axis('Target Label').reset_index()
            # count_df['Label_def'] = count_df['Target 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.write('**Target Paragraphs**: `{}`'.format(count_target))
        #        st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
#
 #               # st.plotly_chart(fig,use_container_width= True)
  #          
            # count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
            # count_ghg = sum(hits['GHG Label'] == 'LABEL_2')
            # count_economy = sum([True if 'Economy-wide' in x else False 
            #                   for x in hits['Sector Label']])
     #       with c2:
      #          st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
       #         st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
        #    st.write('-------------------')    
    #        hits = hits.sort_values(by=['Relevancy'], ascending=False)
     #       netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
      #      if not netzerohit.empty:
       #         netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
        #        # st.write('-------------------')
                # st.markdown("###### Netzero paragraph ######")
      #          st.write('**Netzero paragraph** `page {}`: {}'.format(netzerohit.iloc[0]['page'],
      #                          netzerohit.iloc[0]['text'].replace("\n", " ")))                        
       #         st.write("")
        #    else:
         #       st.info("🤔 No Netzero paragraph found")

            # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
           # st.write('-------------------')
            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= 'cpu_analysis.xlsx')

        else:
            st.info("🤔 No Policy/Plans found")