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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',
}
@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 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 df
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