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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 | |
| def load_indicatorClassifier(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('indicator','MODEL') | |
| logging.info("Loading indicator classifier") | |
| # we are using the pipeline as the model is multilabel and DocumentClassifier | |
| # from Haystack doesnt support multilabel | |
| # in pipeline we use 'sigmoid' to explicitly tell pipeline to make it multilabel | |
| # if not then it will automatically use softmax, which is not a desired thing. | |
| # doc_classifier = TransformersDocumentClassifier( | |
| # model_name_or_path=classifier_name, | |
| # task="text-classification", | |
| # top_k = None) | |
| doc_classifier = pipeline("text-classification", | |
| model=classifier_name, | |
| return_all_scores=True, | |
| function_to_apply= "sigmoid") | |
| return doc_classifier | |
| def indicator_classification(haystack_doc:pd.DataFrame, | |
| threshold:float = 0.5, | |
| classifier_model:pipeline= None | |
| )->Tuple[DataFrame,Series]: | |
| """ | |
| Text-Classification on the list of texts provided. Classifier provides the | |
| most appropriate label for each text. these labels are in terms of if text | |
| belongs to which particular Sustainable Devleopment Goal (SDG). | |
| 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 with two columns['SDG:int', 'text'] | |
| x: Series object with the unique SDG covered in the document uploaded and | |
| the number of times it is covered/discussed/count_of_paragraphs. | |
| """ | |
| logging.info("Working on Indicator Identification") | |
| haystack_doc['Indicator Label'] = 'NA' | |
| 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] | |
| if not classifier_model: | |
| classifier_model = st.session_state['indicator_classifier'] | |
| predictions = classifier_model(list(df1.text)) | |
| list_ = [] | |
| for i in range(len(predictions)): | |
| temp = predictions[i] | |
| placeholder = {} | |
| for j in range(len(temp)): | |
| placeholder[temp[j]['label']] = temp[j]['score'] | |
| list_.append(placeholder) | |
| labels_ = [{**list_[l]} for l in range(len(predictions))] | |
| truth_df = DataFrame.from_dict(labels_) | |
| truth_df = truth_df.round(2) | |
| truth_df = truth_df.astype(float) >= threshold | |
| truth_df = truth_df.astype(str) | |
| categories = list(truth_df.columns) | |
| truth_df['Indicator Label'] = truth_df.apply(lambda x: {i if x[i]=='True' else | |
| None for i in categories}, axis=1) | |
| truth_df['Indicator Label'] = truth_df.apply(lambda x: list(x['Indicator Label'] | |
| -{None}),axis=1) | |
| df1['Indicator Label'] = list(truth_df['Indicator Label']) | |
| df = pd.concat([df,df1]) | |
| df = df.drop(columns = ['PA_check']) | |
| return df |