|
|
| import glob, os, sys;
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| sys.path.append('../utils')
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| from typing import List, Tuple
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| from typing_extensions import Literal
|
| from haystack.schema import Document
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| from utils.config import get_classifier_params
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| from utils.preprocessing import processingpipeline,paraLengthCheck
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| import streamlit as st
|
| import logging
|
| import pandas as pd
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| params = get_classifier_params("preprocessing")
|
|
|
| @st.cache_data
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| def runPreprocessingPipeline(file_name:str, file_path:str,
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| split_by: Literal["sentence", "word"] = 'sentence',
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| split_length:int = 2, split_respect_sentence_boundary:bool = False,
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| split_overlap:int = 0,remove_punc:bool = False)->List[Document]:
|
| """
|
| creates the pipeline and runs the preprocessing pipeline,
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| the params for pipeline are fetched from paramconfig
|
| Params
|
| ------------
|
| file_name: filename, in case of streamlit application use
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| st.session_state['filename']
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| file_path: filepath, in case of streamlit application use st.session_state['filepath']
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| split_by: document splitting strategy either as word or sentence
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| split_length: when synthetically creating the paragrpahs from document,
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| it defines the length of paragraph.
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| split_respect_sentence_boundary: Used when using 'word' strategy for
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| splititng of text.
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| split_overlap: Number of words or sentences that overlap when creating
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| the paragraphs. This is done as one sentence or 'some words' make sense
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| when read in together with others. Therefore the overlap is used.
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| remove_punc: to remove all Punctuation including ',' and '.' or not
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| 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,
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| params= {"FileConverter": {"file_path": file_path, \
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| "file_name": file_name},
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| "UdfPreProcessor": {"remove_punc": remove_punc, \
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| "split_by": split_by, \
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| "split_length":split_length,\
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| "split_overlap": split_overlap, \
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| "split_respect_sentence_boundary":split_respect_sentence_boundary}})
|
|
|
| return output_pre
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|
|
|
|
| def app():
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| with st.container():
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| if 'filepath' in st.session_state:
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| file_name = st.session_state['filename']
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| file_path = st.session_state['filepath']
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|
|
|
|
| all_documents = runPreprocessingPipeline(file_name= file_name,
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| file_path= file_path, split_by= params['split_by'],
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| split_length= params['split_length'],
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| split_respect_sentence_boundary= params['split_respect_sentence_boundary'],
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| split_overlap= params['split_overlap'], remove_punc= params['remove_punc'])
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| paralist = paraLengthCheck(all_documents['documents'], 100)
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| df = pd.DataFrame(paralist,columns = ['text','page'])
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|
|
| st.session_state['key0'] = df
|
|
|
| else:
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| st.info("🤔 No document found, please try to upload it at the sidebar!")
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| logging.warning("Terminated as no document provided") |
