Create conditional_classifier.py

utils/conditional_classifier.py

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+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['cond_check'] = haystack_doc.apply(lambda x: True if (
+                (x['Target Label'] == 'TARGET') | (x['Action Label'] == 'Action') |
+                (x['Policies_Plans Label'] == 'Policies and Plans')) 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'])
+    df = df.reset_index(drop =True)
+    df.index += 1
+
+    return df