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| from haystack.nodes import TransformersDocumentClassifier | |
| from haystack.schema import Document | |
| from typing import List, Tuple | |
| from typing_extensions import Literal | |
| import logging | |
| import pandas as pd | |
| from pandas import DataFrame, Series | |
| from utils.checkconfig import getconfig | |
| from utils.streamlitcheck import check_streamlit | |
| from utils.preprocessing import processingpipeline | |
| try: | |
| import streamlit as st | |
| except ImportError: | |
| logging.info("Streamlit not installed") | |
| ## Labels dictionary ### | |
| _lab_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 load_Classifier(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 classifier") | |
| doc_classifier = TransformersDocumentClassifier( | |
| model_name_or_path=classifier_name, | |
| task="text-classification") | |
| return doc_classifier | |
| def vulnerability_classification(haystack_doc:List[Document], | |
| threshold:float = 0.8, | |
| classifier_model:TransformersDocumentClassifier= 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 Classification") | |
| if not classifier_model: | |
| if check_streamlit(): | |
| classifier_model = st.session_state['vulnerability_classifier'] | |
| else: | |
| logging.warning("No streamlit envinornment found, Pass the classifier") | |
| return | |
| results = classifier_model.predict(haystack_doc) | |
| labels_= [(l.meta['classification']['label'], | |
| l.meta['classification']['score'],l.content,) for l in results] | |
| df = DataFrame(labels_, columns=["vulnerability","Relevancy","text"]) | |
| df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True) | |
| df.index += 1 | |
| df =df[df['Relevancy']>threshold] | |
| # creating the dataframe for value counts of SDG, along with 'title' of SDGs | |
| x = df['vulnerability'].value_counts() | |
| x = x.rename('count') | |
| x = x.rename_axis('vulnerability').reset_index() | |
| x["Vulnerability"] = pd.to_numeric(x["vulnerability"]) | |
| x = x.sort_values(by=['count'], ascending=False) | |
| x['vulnerability_name'] = x['vulnerability'].apply(lambda x: _lab_dict[x]) | |
| x['vulnerability_Num'] = x['vulnerability'].apply(lambda x: "vulnerability "+str(x)) | |
| df['vulnerability'] = pd.to_numeric(df['vulnerability']) | |
| df = df.sort_values('vulnerability') | |
| return df, x | |
| def runPreprocessingPipeline(file_name:str, file_path:str, | |
| split_by: Literal["sentence", "word"] = 'sentence', | |
| split_length:int = 2, split_respect_sentence_boundary:bool = False, | |
| split_overlap:int = 0,remove_punc:bool = False)->List[Document]: | |
| """ | |
| creates the pipeline and runs the preprocessing pipeline, | |
| the params for pipeline are fetched from paramconfig | |
| Params | |
| ------------ | |
| file_name: filename, in case of streamlit application use | |
| st.session_state['filename'] | |
| file_path: filepath, in case of streamlit application use st.session_state['filepath'] | |
| split_by: document splitting strategy either as word or sentence | |
| split_length: when synthetically creating the paragrpahs from document, | |
| it defines the length of paragraph. | |
| split_respect_sentence_boundary: Used when using 'word' strategy for | |
| splititng of text. | |
| split_overlap: Number of words or sentences that overlap when creating | |
| the paragraphs. This is done as one sentence or 'some words' make sense | |
| when read in together with others. Therefore the overlap is used. | |
| remove_punc: to remove all Punctuation including ',' and '.' or not | |
| Return | |
| -------------- | |
| List[Document]: When preprocessing pipeline is run, the output dictionary | |
| has four objects. For the Haysatck implementation of SDG classification we, | |
| need to use the List of Haystack Document, which can be fetched by | |
| key = 'documents' on output. | |
| """ | |
| processing_pipeline = processingpipeline() | |
| output_pre = processing_pipeline.run(file_paths = file_path, | |
| params= {"FileConverter": {"file_path": file_path, \ | |
| "file_name": file_name}, | |
| "UdfPreProcessor": {"remove_punc": remove_punc, \ | |
| "split_by": split_by, \ | |
| "split_length":split_length,\ | |
| "split_overlap": split_overlap, \ | |
| "split_respect_sentence_boundary":split_respect_sentence_boundary}}) | |
| return output_pre |