appStore/adapmit.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.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)


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

# @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



                
            #     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')