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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
from setfit import SetFitModel
# labels dictionary
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 get_vulnerability_labels(preds):
"""
Function that takes the numerical predictions as an input and returns a list of the labels.
"""
# Get label names
preds_list = preds.tolist()
# Get the name of the group where the prediction is equal to "1"
result = []
for sublist in preds_list:
names = [label_dict[key] for key, value in enumerate(sublist) if value == 1]
result.append(names)
return result
@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(classifier_name)
# 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 = get_vulnerability_labels(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 |