Delete appStore/indicator.py

appStore/indicator.py

@@ -1,166 +0,0 @@
-# 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.indicator_classifier import load_indicatorClassifier, indicator_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 = 'indicator'
-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:
-                df = st.session_state.key1
-                classifier = load_indicatorClassifier(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 = indicator_classification(haystack_doc=df,
-                                            threshold= params['threshold'])
-
-                st.session_state.key1 = df
-
-
-                # # st.write(df)
-                # 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, x='category', y='count',
-                #             color='truth_value')
-                # # 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)
-                # df['Validation'] =  'No'
-                # df['Sector1'] = 'Blank'
-                # df['Sector2'] = 'Blank'
-                # df['Sector3'] = 'Blank'
-                # df['Sector4'] = 'Blank'
-                # df['Sector5'] = 'Blank'
-                # df_xlsx = to_excel(df,categories)
-                # st.download_button(label='📥 Download Current Result',
-                #                 data=df_xlsx ,
-            #     #               file_name= 'file_sector.xlsx')
-            # 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 'key' in st.session_state:
-        #     if st.session_state.key is not None:
-        #         df = st.session_state.key
-        #         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" )
-        #         sectors =set(df.columns)
-        #         removecols = {'Validation','Sector1','Sector2','Sector3','Sector4',
-        #                       'Sector5','text'}
-        #         sectors  = list(sectors - removecols)
-
-        #         placeholder = {}
-        #         for val in sectors:
-        #             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 = ['sector','count','truth_value'])
-        #         fig = px.bar(count_df, x='sector', y='count',
-        #                     color='truth_value',
-        #                     height=400)
-        #         st.write("")
-        #         st.plotly_chart(fig)
-
-                # df['Validation'] =  'No'
-                # df['Sector1'] = 'Blank'
-                # df['Sector2'] = 'Blank'
-                # df['Sector3'] = 'Blank'
-                # df['Sector4'] = 'Blank'
-                # df['Sector5'] = 'Blank'
-                # df_xlsx = to_excel(df,sectors)
-                # st.download_button(label='📥 Download Current Result',
-                #                 data=df_xlsx ,
-                #               file_name= 'file_sector.xlsx')