| 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) |
| |
| |
| |
| haystack_doc['cond_check'] = haystack_doc.apply(lambda x: True if ( |
| (x['Target Label'] == 'TARGET') | (x['PA_check'] == True)) else |
| False, axis=1) |
| |
| 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_) |
| |
| |
| df = pd.concat([df,temp]) |
| df = df.drop(columns = ['cond_check','PA_check']) |
| df = df.reset_index(drop =True) |
| df.index += 1 |
|
|
| return df |
