File size: 6,298 Bytes
579b090 1847572 579b090 af925e8 579b090 af925e8 785df60 af925e8 579b090 95c0e35 579b090 95c0e35 579b090 95c0e35 579b090 1847572 579b090 95c0e35 579b090 95c0e35 aaa7178 e000f68 579b090 e000f68 579b090 95c0e35 579b090 e000f68 579b090 af925e8 2604e77 af925e8 b758017 2604e77 e000f68 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
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):
# 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 = "NA"
# 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 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/vulnerable_groups")
# 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
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 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)
st.write(len(pred_labels))
st.write(pred_labels)
st.write(len(haystack_doc))
# 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 |