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- import copy
- import random
- import re
- import numpy as np
- from rag.parser import bullets_category, BULLET_PATTERN, is_english, tokenize, remove_contents_table, \
- hierarchical_merge, make_colon_as_title, naive_merge, random_choices
- from rag.nlp import huqie
- from rag.parser.docx_parser import HuDocxParser
- from rag.parser.pdf_parser import HuParser
-
-
- class Pdf(HuParser):
- def __call__(self, filename, binary=None, from_page=0,
- to_page=100000, zoomin=3, callback=None):
- self.__images__(
- filename if not binary else binary,
- zoomin,
- from_page,
- to_page)
- callback(0.1, "OCR finished")
-
- from timeit import default_timer as timer
- start = timer()
- self._layouts_paddle(zoomin)
- callback(0.47, "Layout analysis finished")
- print("paddle layouts:", timer() - start)
- self._table_transformer_job(zoomin)
- callback(0.68, "Table analysis finished")
- self._text_merge()
- self._concat_downward(concat_between_pages=False)
- self._filter_forpages()
- self._merge_with_same_bullet()
- callback(0.75, "Text merging finished.")
- tbls = self._extract_table_figure(True, zoomin, False)
-
- callback(0.8, "Text extraction finished")
-
- return [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno","")) for b in self.boxes], tbls
-
-
- def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None, **kwargs):
- doc = {
- "docnm_kwd": filename,
- "title_tks": huqie.qie(re.sub(r"\.[a-zA-Z]+$", "", filename))
- }
- doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
- pdf_parser = None
- sections,tbls = [], []
- if re.search(r"\.docx?$", filename, re.IGNORECASE):
- callback(0.1, "Start to parse.")
- doc_parser = HuDocxParser()
- # TODO: table of contents need to be removed
- sections, tbls = doc_parser(binary if binary else filename, from_page=from_page, to_page=to_page)
- remove_contents_table(sections, eng=is_english(random_choices([t for t,_ in sections], k=200)))
- callback(0.8, "Finish parsing.")
- elif re.search(r"\.pdf$", filename, re.IGNORECASE):
- pdf_parser = Pdf()
- sections,tbls = pdf_parser(filename if not binary else binary,
- from_page=from_page, to_page=to_page, callback=callback)
- elif re.search(r"\.txt$", filename, re.IGNORECASE):
- callback(0.1, "Start to parse.")
- txt = ""
- if binary:txt = binary.decode("utf-8")
- else:
- with open(filename, "r") as f:
- while True:
- l = f.readline()
- if not l:break
- txt += l
- sections = txt.split("\n")
- sections = [(l,"") for l in sections if l]
- remove_contents_table(sections, eng = is_english(random_choices([t for t,_ in sections], k=200)))
- callback(0.8, "Finish parsing.")
- else: raise NotImplementedError("file type not supported yet(docx, pdf, txt supported)")
-
- make_colon_as_title(sections)
- bull = bullets_category([t for t in random_choices([t for t,_ in sections], k=100)])
- if bull >= 0: cks = hierarchical_merge(bull, sections, 3)
- else: cks = naive_merge(sections, kwargs.get("chunk_token_num", 256), kwargs.get("delimer", "\n。;!?"))
-
- sections = [t for t, _ in sections]
- # is it English
- eng = is_english(random_choices(sections, k=218))
-
- res = []
- # add tables
- for img, rows in tbls:
- bs = 10
- de = ";" if eng else ";"
- for i in range(0, len(rows), bs):
- d = copy.deepcopy(doc)
- r = de.join(rows[i:i + bs])
- r = re.sub(r"\t——(来自| in ).*”%s" % de, "", r)
- tokenize(d, r, eng)
- d["image"] = img
- res.append(d)
- print("TABLE", d["content_with_weight"])
- # wrap up to es documents
- for ck in cks:
- d = copy.deepcopy(doc)
- ck = "\n".join(ck)
- if pdf_parser:
- d["image"] = pdf_parser.crop(ck)
- ck = pdf_parser.remove_tag(ck)
- tokenize(d, ck, eng)
- res.append(d)
- return res
-
-
- if __name__ == "__main__":
- import sys
- def dummy(a, b):
- pass
- chunk(sys.argv[1], from_page=1, to_page=10, callback=dummy)
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