Delete utils/conditional_classifier.py

utils/conditional_classifier.py

@@ -1,95 +0,0 @@
-from typing import List, Tuple
-from typing_extensions import Literal
-import logging
-import pandas as pd
-from pandas import DataFrame, Series
-from utils.config import getconfig
-from utils.preprocessing import processingpipeline
-import streamlit as st
-from transformers import pipeline
-
-
-@st.cache_resource
-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 
-    model should be passed.
-    1. https://docs.haystack.deepset.ai/reference/document-classifier-api
-    2. https://docs.haystack.deepset.ai/docs/document_classifier
-    Params
-    --------
-    config_file: config file path from which to read the model name
-    classifier_name: if modelname is passed, it takes a priority if not \
-    found then will look for configfile, else raise error.
-    Return: document classifier model
-    """
-    if not classifier_name:
-        if not config_file:
-            logging.warning("Pass either model name or config file")
-            return
-        else:
-            config = getconfig(config_file)
-            classifier_name = config.get('conditional','MODEL')
-    
-    logging.info("Loading conditional classifier")    
-    doc_classifier = pipeline("text-classification", 
-                            model=classifier_name, 
-                            top_k =1)
-
-    return doc_classifier
-
-
-@st.cache_data
-def conditional_classification(haystack_doc:pd.DataFrame,
-                        threshold:float = 0.8, 
-                        classifier_model:pipeline= None
-                        )->Tuple[DataFrame,Series]:
-    """
-    Text-Classification on the list of texts provided. Classifier provides the 
-    most appropriate label for each text. It informs if paragraph contains any 
-    netzero information or not.
-    Params
-    ---------
-    haystack_doc: List of haystack Documents. The output of Preprocessing Pipeline 
-    contains the list of paragraphs in different format,here the list of 
-    Haystack Documents is used.
-    threshold: threshold value for the model to keep the results from classifier
-    classifiermodel: you can pass the classifier model directly,which takes priority
-    however if not then looks for model in streamlit session.
-    In case of streamlit avoid passing the model directly.
-    Returns
-    ----------
-    df: Dataframe 
-    """
-    logging.info("Working on Conditionality Identification")
-    haystack_doc['Conditional Label'] = 'NA'
-    haystack_doc['Conditional Score'] = 0.0
-    haystack_doc['cond_check'] = False
-    haystack_doc['PA_check'] = haystack_doc['Policy-Action Label'].apply(lambda x: True if len(x) != 0 else False)
-                            
-    #df1 = haystack_doc[haystack_doc['PA_check'] == True]
-    #df = haystack_doc[haystack_doc['PA_check'] == False]                        
-    haystack_doc['cond_check'] = haystack_doc.apply(lambda x: True if (
-                (x['Target Label'] == 'TARGET') | (x['PA_check'] == True)) else
-                False, axis=1)
-    # we apply Netzero to only paragraphs which are classified as 'Target' related
-    temp = haystack_doc[haystack_doc['cond_check'] == True]
-    temp = temp.reset_index(drop=True)
-    df = haystack_doc[haystack_doc['cond_check'] == False]
-    df = df.reset_index(drop=True)
-
-    if not classifier_model:
-        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['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.drop(columns = ['cond_check','PA_check'])
-    df = df.reset_index(drop =True)
-    df.index += 1
-
-    return df