| |
| 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("preprocessing") |
|
|
| @st.cache_data |
| 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(): |
| all_files_df = pd.DataFrame() |
|
|
| for key in st.session_state: |
| if key.startswith('filepath_'): |
| file_path = st.session_state[key] |
| file_name = st.session_state['filename' + key[-2:]] |
|
|
| 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) |
| file_df = pd.DataFrame(paralist, columns=['text', 'page']) |
| file_df['filename'] = file_name |
|
|
| all_files_df = pd.concat([all_files_df, file_df], ignore_index=True) |
|
|
| if not all_files_df.empty: |
| st.session_state['combined_files_df'] = all_files_df |
| else: |
| st.info("🤔 No document found, please try to upload it at the sidebar!") |
| logging.warning("Terminated as no document provided") |
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