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from haystack.schema import Document
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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 haystack.nodes import TransformersDocumentClassifier
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from transformers import pipeline
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@st.cache_resource
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def load_sectorClassifier(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('sector','MODEL')
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logging.info("Loading sector classifier")
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doc_classifier = pipeline("text-classification",
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model=classifier_name,
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return_all_scores=True,
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function_to_apply= "sigmoid")
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return doc_classifier
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@st.cache_data
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def sector_classification(haystack_doc:pd.DataFrame,
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threshold:float = 0.5,
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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. these labels are in terms of if text
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belongs to which particular Sustainable Devleopment Goal (SDG).
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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 with two columns['SDG:int', 'text']
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x: Series object with the unique SDG covered in the document uploaded and
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the number of times it is covered/discussed/count_of_paragraphs.
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"""
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logging.info("Working on Sector Identification")
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haystack_doc['Sector Label'] = 'NA'
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df1 = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
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df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
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if not classifier_model:
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classifier_model = st.session_state['sector_classifier']
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predictions = classifier_model(list(df1.text))
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list_ = []
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for i in range(len(predictions)):
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temp = predictions[i]
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placeholder = {}
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for j in range(len(temp)):
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placeholder[temp[j]['label']] = temp[j]['score']
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list_.append(placeholder)
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labels_ = [{**list_[l]} for l in range(len(predictions))]
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truth_df = DataFrame.from_dict(labels_)
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truth_df = truth_df.round(2)
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truth_df = truth_df.astype(float) >= threshold
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truth_df = truth_df.astype(str)
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categories = list(truth_df.columns)
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truth_df['Sector Label'] = truth_df.apply(lambda x: {i if x[i]=='True' else
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None for i in categories}, axis=1)
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truth_df['Sector Label'] = truth_df.apply(lambda x: list(x['Sector Label']
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-{None}),axis=1)
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df1['Sector Label'] = list(truth_df['Sector Label'])
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df = pd.concat([df,df1])
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return df
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