Delete appStore/netzero.py

appStore/netzero.py

@@ -1,95 +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.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)
-
-# Labels dictionary ###
-_lab_dict = {
-            'NEGATIVE':'NO NETZERO TARGET',
-            'NA':'NOT APPLICABLE',
-            'NETZERO':'NETZERO TARGET',
-            }
-
-
-@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 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
-               
-
-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")
-   
-