leavoigt commited on
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0487009
1 Parent(s): c2f3090

Delete appStore/doc_processing.py

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  1. appStore/doc_processing.py +0 -77
appStore/doc_processing.py DELETED
@@ -1,77 +0,0 @@
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- # set path
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- 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
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- 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
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- import logging
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- import pandas as pd
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- params = get_classifier_params("preprocessing")
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-
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- @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]:
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- """
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- creates the pipeline and runs the preprocessing pipeline,
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- the params for pipeline are fetched from paramconfig
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- Params
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- ------------
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- 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
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- --------------
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- List[Document]: When preprocessing pipeline is run, the output dictionary
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- has four objects. For the Haysatck implementation of SDG classification we,
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- need to use the List of Haystack Document, which can be fetched by
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- key = 'documents' on output.
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- """
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-
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- processing_pipeline = processingpipeline()
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-
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- 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}})
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-
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- return output_pre
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-
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-
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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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-
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-
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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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- # saving the dataframe to session state
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- st.session_state['key0'] = df
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-
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- 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")