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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
## Labels dictionary ###
_lab_dict = {
'0':'NO',
'1':'YES',
}
def get_target_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()
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_targetClassifier(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('target','MODEL')
logging.info("Loading classifier")
doc_classifier = pipeline("text-classification",
model=classifier_name,
top_k =1)
return doc_classifier
@st.cache_data
def target_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. There labels indicate whether the paragraph
references a specific action, target or measure in the paragraph.
---------
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 target/action identification")
haystack_doc['Vulnerability Label'] = 'NA'
if not classifier_model:
classifier_model = st.session_state['target_classifier']
# Get predictions
predictions = classifier_model(list(haystack_doc.text))
# Get labels for predictions
pred_labels = getlabels(predictions)
# Save labels
haystack_doc['Target Label'] = pred_labels
# logging.info("Working on action/target extraction")
# if not classifier_model:
# classifier_model = st.session_state['target_classifier']
# results = classifier_model(list(haystack_doc.text))
# labels_= [(l[0]['label'],
# l[0]['score']) for l in results]
# df1 = DataFrame(labels_, columns=["Target Label","Target Score"])
# df = pd.concat([haystack_doc,df1],axis=1)
# df = df.sort_values(by="Target Score", ascending=False).reset_index(drop=True)
# df['Target Score'] = df['Target Score'].round(2)
# df.index += 1
# # df['Label_def'] = df['Target Label'].apply(lambda i: _lab_dict[i])
return haystack_doc
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