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"""HTML parser.
Contains parser for html files.
"""
import re
from pathlib import Path
from typing import Dict, Union
from application.parser.file.base_parser import BaseParser
class HTMLParser(BaseParser):
"""HTML parser."""
def _init_parser(self) -> Dict:
"""Init parser."""
return {}
def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
"""Parse file.
Returns:
Union[str, List[str]]: a string or a List of strings.
"""
try:
from unstructured.partition.html import partition_html
from unstructured.staging.base import convert_to_isd
from unstructured.cleaners.core import clean
except ImportError:
raise ValueError("unstructured package is required to parse HTML files.")
# Using the unstructured library to convert the html to isd format
# isd sample : isd = [
# {"text": "My Title", "type": "Title"},
# {"text": "My Narrative", "type": "NarrativeText"}
# ]
with open(file, "r", encoding="utf-8") as fp:
elements = partition_html(file=fp)
isd = convert_to_isd(elements)
# Removing non ascii charactwers from isd_el['text']
for isd_el in isd:
isd_el['text'] = isd_el['text'].encode("ascii", "ignore").decode()
# Removing all the \n characters from isd_el['text'] using regex and replace with single space
# Removing all the extra spaces from isd_el['text'] using regex and replace with single space
for isd_el in isd:
isd_el['text'] = re.sub(r'\n', ' ', isd_el['text'], flags=re.MULTILINE | re.DOTALL)
isd_el['text'] = re.sub(r"\s{2,}", " ", isd_el['text'], flags=re.MULTILINE | re.DOTALL)
# more cleaning: extra_whitespaces, dashes, bullets, trailing_punctuation
for isd_el in isd:
clean(isd_el['text'], extra_whitespace=True, dashes=True, bullets=True, trailing_punctuation=True)
# Creating a list of all the indexes of isd_el['type'] = 'Title'
title_indexes = [i for i, isd_el in enumerate(isd) if isd_el['type'] == 'Title']
# Creating 'Chunks' - List of lists of strings
# each list starting with isd_el['type'] = 'Title' and all the data till the next 'Title'
# Each Chunk can be thought of as an individual set of data, which can be sent to the model
# Where Each Title is grouped together with the data under it
Chunks = [[]]
final_chunks = list(list())
for i, isd_el in enumerate(isd):
if i in title_indexes:
Chunks.append([])
Chunks[-1].append(isd_el['text'])
# Removing all the chunks with sum of length of all the strings in the chunk < 25
# TODO: This value can be an user defined variable
for chunk in Chunks:
# sum of length of all the strings in the chunk
sum = 0
sum += len(str(chunk))
if sum < 25:
Chunks.remove(chunk)
else:
# appending all the approved chunks to final_chunks as a single string
final_chunks.append(" ".join([str(item) for item in chunk]))
return final_chunks
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