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| # set path | |
| import glob, os, sys; | |
| sys.path.append('../utils') | |
| from typing import List, Tuple | |
| from typing_extensions import Literal | |
| from haystack.schema import Document | |
| from utils.config import get_classifier_params | |
| from utils.preprocessing import processingpipeline,paraLengthCheck | |
| import streamlit as st | |
| import logging | |
| import pandas as pd | |
| import nltk | |
| nltk.download('punkt_tab') | |
| params = get_classifier_params("preprocessing") | |
| 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 | |
| def app(): | |
| with st.container(): | |
| if 'filepath' in st.session_state: | |
| file_name = st.session_state['filename'] | |
| file_path = st.session_state['filepath'] | |
| all_documents = runPreprocessingPipeline(file_name= file_name, | |
| file_path= file_path, split_by= params['split_by'], | |
| split_length= params['split_length'], | |
| split_respect_sentence_boundary= params['split_respect_sentence_boundary'], | |
| split_overlap= params['split_overlap'], remove_punc= params['remove_punc']) | |
| paralist = paraLengthCheck(all_documents['documents'], 100) | |
| df = pd.DataFrame(paralist,columns = ['text','page']) | |
| # saving the dataframe to session state | |
| st.session_state['key0'] = df | |
| else: | |
| st.info("🤔 No document found, please try to upload it at the sidebar!") | |
| logging.warning("Terminated as no document provided") |