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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 = {
'LABEL_0':'NEGATIVE',
'LABEL_1':'NOT GHG',
'LABEL_2':'GHG',
'NA':'NA',
}
@st.cache_resource
def load_ghgClassifier(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('ghg','MODEL')
logging.info("Loading ghg classifier")
doc_classifier = pipeline("text-classification",
model=classifier_name,
top_k =1)
return doc_classifier
@st.cache_data
def ghg_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. It identifies if text contains 'GHG'
related information or not.
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
"""
logging.info("Working on GHG Extraction")
haystack_doc['GHG Label'] = 'NA'
haystack_doc['GHG Score'] = 'NA'
# applying GHG Identifier to only 'Target' paragraphs.
temp = haystack_doc[haystack_doc['Target Label'] == 'TARGET']
temp = temp.reset_index(drop=True)
df = haystack_doc[haystack_doc['Target Label'] == 'NEGATIVE']
df = df.reset_index(drop=True)
if not classifier_model:
classifier_model = st.session_state['ghg_classifier']
results = classifier_model(list(temp.text))
labels_= [(l[0]['label'],l[0]['score']) for l in results]
temp['GHG Label'],temp['GHG Score'] = zip(*labels_)
# merge back Target and non-Target dataframe
df = pd.concat([df,temp])
df['GHG Label'] = df['GHG Label'].apply(lambda i: _lab_dict[i])
df = df.reset_index(drop =True)
df.index += 1
return df
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