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