mirror of
https://github.com/arc53/DocsGPT
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84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
"""HTML parser.
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Contains parser for html files.
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"""
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import re
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from pathlib import Path
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from typing import Dict, Union
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from parser.file.base_parser import BaseParser
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class HTMLParser(BaseParser):
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"""HTML parser."""
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def _init_parser(self) -> Dict:
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"""Init parser."""
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return {}
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def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
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"""Parse file.
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Returns:
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Union[str, List[str]]: a string or a List of strings.
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"""
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try:
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from unstructured.partition.html import partition_html
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from unstructured.staging.base import convert_to_isd
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from unstructured.cleaners.core import clean
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except ImportError:
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raise ValueError("unstructured package is required to parse HTML files.")
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# Using the unstructured library to convert the html to isd format
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# isd sample : isd = [
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# {"text": "My Title", "type": "Title"},
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# {"text": "My Narrative", "type": "NarrativeText"}
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# ]
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with open(file, "r", encoding="utf-8") as fp:
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elements = partition_html(file=fp)
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isd = convert_to_isd(elements)
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# Removing non ascii charactwers from isd_el['text']
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for isd_el in isd:
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isd_el['text'] = isd_el['text'].encode("ascii", "ignore").decode()
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# Removing all the \n characters from isd_el['text'] using regex and replace with single space
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# Removing all the extra spaces from isd_el['text'] using regex and replace with single space
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for isd_el in isd:
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isd_el['text'] = re.sub(r'\n', ' ', isd_el['text'], flags=re.MULTILINE | re.DOTALL)
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isd_el['text'] = re.sub(r"\s{2,}", " ", isd_el['text'], flags=re.MULTILINE | re.DOTALL)
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# more cleaning: extra_whitespaces, dashes, bullets, trailing_punctuation
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for isd_el in isd:
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clean(isd_el['text'], extra_whitespace=True, dashes=True, bullets=True, trailing_punctuation=True)
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# Creating a list of all the indexes of isd_el['type'] = 'Title'
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title_indexes = [i for i, isd_el in enumerate(isd) if isd_el['type'] == 'Title']
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# Creating 'Chunks' - List of lists of strings
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# each list starting with with isd_el['type'] = 'Title' and all the data till the next 'Title'
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# Each Chunk can be thought of as an individual set of data, which can be sent to the model
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# Where Each Title is grouped together with the data under it
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Chunks = [[]]
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final_chunks = list(list())
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for i, isd_el in enumerate(isd):
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if i in title_indexes:
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Chunks.append([])
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Chunks[-1].append(isd_el['text'])
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# Removing all the chunks with sum of lenth of all the strings in the chunk < 25
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# TODO: This value can be a user defined variable
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for chunk in Chunks:
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# sum of lenth of all the strings in the chunk
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sum = 0
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sum += len(str(chunk))
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if sum < 25:
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Chunks.remove(chunk)
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else:
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# appending all the approved chunks to final_chunks as a single string
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final_chunks.append(" ".join([str(item) for item in chunk]))
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return final_chunks
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