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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 | |
params = get_classifier_params("vulnerable_groups") | |
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") |