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 from setfit import SetFitModel label_dict= {0: 'Agricultural communities', 1: 'Children', 2: 'Coastal communities', 3: 'Ethnic, racial or other minorities', 4: 'Fishery communities', 5: 'Informal sector workers', 6: 'Members of indigenous and local communities', 7: 'Migrants and displaced persons', 8: 'Older persons', 9: 'Other', 10: 'Persons living in poverty', 11: 'Persons with disabilities', 12: 'Persons with pre-existing health conditions', 13: 'Residents of drought-prone regions', 14: 'Rural populations', 15: 'Sexual minorities (LGBTQI+)', 16: 'Urban populations', 17: 'Women and other genders'} def getlabels(preds): """ Function that takes the numerical predictions as an input and returns a list of the labels. """ # Get label names preds_list = preds.tolist() predictions_names=[] # loop through each prediction for ele in preds_list: # see if there is a value 1 and retrieve index try: index_of_one = ele.index(1) except ValueError: index_of_one = "NA" # Retrieve the name of the label (if no prediction made = NA) if index_of_one != "NA": name = label_dict[index_of_one] else: name = "Other" # Append name to list predictions_names.append(name) return predictions_names @st.cache_resource def load_vulnerabilityClassifier(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 no classifier given 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('vulnerability','MODEL') logging.info("Loading vulnerability 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) # Download model from HF Hub doc_classifier = SetFitModel.from_pretrained("leavoigt/vulnerability_multilabel") # doc_classifier = pipeline("text-classification", # model=classifier_name, # return_all_scores=True, # function_to_apply= "sigmoid") return doc_classifier @st.cache_data def vulnerability_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 reference a group in a vulnerable situation. --------- 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 vulnerability Identification") haystack_doc['Vulnerability 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['vulnerability_classifier'] predictions = classifier_model(list(haystack_doc.text)) pred_labels = getlabels(predictions) haystack_doc['Vulnerability Label'] = pred_labels # 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['Vulnerability Label'] = truth_df.apply(lambda x: {i if x[i]=='True' else # None for i in categories}, axis=1) # truth_df['Vulnerability Label'] = truth_df.apply(lambda x: list(x['Vulnerability Label'] # -{None}),axis=1) # haystack_doc['Vulnerability Label'] = list(truth_df['Vulnerability Label']) return haystack_doc