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from typing import List, Tuple |
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from typing_extensions import Literal |
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import logging |
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import pandas as pd |
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from pandas import DataFrame, Series |
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from utils.config import getconfig |
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from utils.preprocessing import processingpipeline |
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import streamlit as st |
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from transformers import pipeline |
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_lab_dict = { |
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0: 'Children and Youth', |
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1: 'Informal sector workers', |
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2: 'Other', |
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3: 'Rural populations', |
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4: 'Sexual minorities (LGBTQI+)', |
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5: 'Urban populations', |
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6: 'Women'} |
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@st.cache_resource |
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def load_groupsClassifier(config_file:str = None, classifier_name:str = None): |
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""" |
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loads the document classifier using haystack, where the name/path of model |
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in HF-hub as string is used to fetch the model object.Either configfile or |
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model should be passed. |
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1. https://docs.haystack.deepset.ai/reference/document-classifier-api |
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2. https://docs.haystack.deepset.ai/docs/document_classifier |
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Params |
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-------- |
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config_file: config file path from which to read the model name |
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classifier_name: if modelname is passed, it takes a priority if not \ |
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found then will look for configfile, else raise error. |
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Return: document classifier model |
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""" |
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if not classifier_name: |
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if not config_file: |
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logging.warning("Pass either model name or config file") |
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return |
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else: |
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config = getconfig(config_file) |
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classifier_name = config.get('group_classification','MODEL') |
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logging.info("Loading classifier") |
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doc_classifier = pipeline("text-classification", |
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model=classifier_name, |
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top_k =1) |
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return doc_classifier |
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@st.cache_data |
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def groups_classification(haystack_doc:pd.DataFrame, |
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threshold:float = 0.5, |
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classifier_model:pipeline= None |
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)->Tuple[DataFrame,Series]: |
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""" |
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Text-Classification on the list of texts provided. Classifier provides the |
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most appropriate label for each text. these labels are in terms of if text |
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belongs to which particular Sustainable Devleopment Goal (SDG). |
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Params |
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--------- |
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haystack_doc: List of haystack Documents. The output of Preprocessing Pipeline |
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contains the list of paragraphs in different format,here the list of |
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Haystack Documents is used. |
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threshold: threshold value for the model to keep the results from classifier |
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classifiermodel: you can pass the classifier model directly,which takes priority |
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however if not then looks for model in streamlit session. |
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In case of streamlit avoid passing the model directly. |
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Returns |
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---------- |
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df: Dataframe with two columns['SDG:int', 'text'] |
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x: Series object with the unique SDG covered in the document uploaded and |
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the number of times it is covered/discussed/count_of_paragraphs. |
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""" |
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logging.info("Working on Group Extraction") |
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if not classifier_model: |
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classifier_model = st.session_state['group_classifier'] |
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results = classifier_model(list(haystack_doc.text)) |
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labels_= [(l[0]['label'], |
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l[0]['score']) for l in results] |
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df1 = DataFrame(labels_, columns=["Group Label","Relevancy"]) |
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df = pd.concat([haystack_doc,df1],axis=1) |
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df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True) |
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df.index += 1 |
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df['Label_def'] = df['Group Label'].apply(lambda i: _lab_dict[i]) |
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return df |
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