Create conditional.py

appStore/conditional.py

@@ -0,0 +1,42 @@
+# 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.conditional_classifier import load_conditionalClassifier, conditional_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 = 'conditional'
+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
+
+            # Load the classifier model
+            classifier = load_conditionalClassifier(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 = conditional_classification(haystack_doc=df,
+                                        threshold= params['threshold'])
+            st.session_state.key1 = df