add conditional

app.py

@@ -4,6 +4,7 @@ import appStore.sector as sector
 import appStore.adapmit as adapmit
 import appStore.ghg as ghg
 import appStore.policyaction as policyaction
+import appStore.conditional as conditional
 import appStore.indicator as indicator
 import appStore.doc_processing as processing
 from utils.uploadAndExample import add_upload
@@ -60,7 +61,7 @@ with st.expander("ℹ️ - About this app", expanded=False):
                 wide scale. """)               
     st.write("")
 apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
-        policyaction.app, indicator.app]
+        policyaction.app, indicator.app, conditional.app]
        #  sector.app, adapmit.app]
          
         #  policyaction.app, indicator.app, ]

appStore/conditional.py

@@ -8,7 +8,7 @@ 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
+from utils.netzero_classifier import load_conditionalClassifier, conditional_classification
 import logging
 logger = logging.getLogger(__name__)
 from utils.config import get_classifier_params
@@ -18,7 +18,7 @@ import plotly.express as px
 
 
 # Declare all the necessary variables
-classifier_identifier = 'netzero'
+classifier_identifier = 'conditional'
 params  = get_classifier_params(classifier_identifier)
 
 
@@ -29,7 +29,7 @@ def app():
             df = st.session_state.key1
 
             # Load the classifier model
-            classifier = load_netzeroClassifier(classifier_name=params['model_name'])
+            classifier = load_conditionalClassifier(classifier_name=params['model_name'])
             st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
 
             if sum(df['Target Label'] == 'TARGET') > 100:
@@ -37,7 +37,7 @@ def app():
             else:
                 warning_msg = ""
             
-            df = netzero_classification(haystack_doc=df,
+            df = conditional_classification(haystack_doc=df,
                                         threshold= params['threshold'])
             st.session_state.key1 = df
 

utils/conditional_classifier.py

@@ -16,7 +16,7 @@ _lab_dict = {
             }
 
 @st.cache_resource
-def load_netzeroClassifier(config_file:str = None, classifier_name:str = None):
+def load_conditionalClassifier(config_file:str = None, classifier_name:str = None):
     """
     loads the document classifier using haystack, where the name/path of model
     in HF-hub as string is used to fetch the model object.Either configfile or 
@@ -36,9 +36,9 @@ def load_netzeroClassifier(config_file:str = None, classifier_name:str = None):
             return
         else:
             config = getconfig(config_file)
-            classifier_name = config.get('netzero','MODEL')
+            classifier_name = config.get('conditional','MODEL')
     
-    logging.info("Loading netzero classifier")    
+    logging.info("Loading conditional classifier")    
     doc_classifier = pipeline("text-classification", 
                             model=classifier_name, 
                             top_k =1)
@@ -47,7 +47,7 @@ def load_netzeroClassifier(config_file:str = None, classifier_name:str = None):
 
 
 @st.cache_data
-def netzero_classification(haystack_doc:pd.DataFrame,
+def conditional_classification(haystack_doc:pd.DataFrame,
                         threshold:float = 0.8, 
                         classifier_model:pipeline= None
                         )->Tuple[DataFrame,Series]:
@@ -68,9 +68,9 @@ def netzero_classification(haystack_doc:pd.DataFrame,
     ----------
     df: Dataframe 
     """
-    logging.info("Working on Netzero Extraction")
-    haystack_doc['Netzero Label'] = 'NA'
-    haystack_doc['Netzero Score'] = 'NA'
+    logging.info("Working on Conditionality Identification")
+    haystack_doc['Conditional Label'] = 'NA'
+    haystack_doc['Conditional Score'] = 'NA'
     # we apply Netzero to only paragraphs which are classified as 'Target' related
     temp = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
     temp = temp.reset_index(drop=True)
@@ -78,12 +78,12 @@ def netzero_classification(haystack_doc:pd.DataFrame,
     df = df.reset_index(drop=True)
 
     if not classifier_model:
-        classifier_model = st.session_state['netzero_classifier']
+        classifier_model = st.session_state['conditional_classifier']
     
     results = classifier_model(list(temp.text))
     labels_= [(l[0]['label'],l[0]['score']) for l in results]
-    temp['Netzero Label'],temp['Netzero Score'] = zip(*labels_)
-    temp['Netzero Label'] = temp['Netzero Label'].apply(lambda x: _lab_dict[x])
+    temp['Conditional Label'],temp['Conditional Score'] = zip(*labels_)
+    # temp[' Label'] = temp['Netzero Label'].apply(lambda x: _lab_dict[x])
     # merging Target with Non Target dataframe
     df = pd.concat([df,temp])
     df = df.reset_index(drop =True)