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