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- import copy
- import re
- from io import BytesIO
- from docx import Document
- import numpy as np
- from rag.app import callback__, bullets_category, BULLET_PATTERN
- from rag.nlp import huqie
- from rag.parser.pdf_parser import HuParser
-
-
- class Docx(object):
- def __init__(self):
- pass
-
- def __clean(self, line):
- line = re.sub(r"\u3000", " ", line).strip()
- return line
-
- def __call__(self, filename, binary=None):
- self.doc = Document(
- filename) if not binary else Document(BytesIO(binary))
- lines = [self.__clean(p.text) for p in self.doc.paragraphs]
- return [l for l in lines if l]
-
-
- 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__((min(to_page, self.total_page) - from_page) / self.total_page / 2,
- "Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
-
- from timeit import default_timer as timer
- start = timer()
- self._layouts_paddle(zoomin)
- callback__((min(to_page, self.total_page) - from_page) / self.total_page / 2,
- "Page {}~{}: Layout analysis finished".format(from_page, min(to_page, self.total_page)), callback)
- print("paddle layouts:", timer()-start)
- bxs = self.sort_Y_firstly(self.boxes, np.median(self.mean_height) / 3)
- # is it English
- eng = 0
- for b in bxs:
- if re.match(r"[a-zA-Z]", b["text"].strip()):
- eng += 1
- if eng / len(bxs) > 0.8:
- eng = True
- else:
- eng = False
- # Merge vertically
- i = 0
- while i + 1 < len(bxs):
- b = bxs[i]
- b_ = bxs[i + 1]
- if b["page_number"] < b_["page_number"] and re.match(r"[0-9 •一—-]+$", b["text"]):
- bxs.pop(i)
- continue
- concatting_feats = [
- b["text"].strip()[-1] in ",;:'\",、‘“;:",
- len(b["text"].strip())>1 and b["text"].strip()[-2] in ",;:'\",‘“、;:",
- b["text"].strip()[0] in "。;?!?”)),,、:",
- ]
- # features for not concating
- feats = [
- b.get("layoutno",0) != b.get("layoutno",0),
- b["text"].strip()[-1] in "。?!?",
- eng and b["text"].strip()[-1] in ".!?",
- b["page_number"] == b_["page_number"] and b_["top"] - \
- b["bottom"] > self.mean_height[b["page_number"] - 1] * 1.5,
- b["page_number"] < b_["page_number"] and abs(
- b["x0"] - b_["x0"]) > self.mean_width[b["page_number"] - 1] * 4
- ]
- if any(feats) and not any(concatting_feats):
- i += 1
- continue
- # merge up and down
- b["bottom"] = b_["bottom"]
- b["text"] += b_["text"]
- b["x0"] = min(b["x0"], b_["x0"])
- b["x1"] = max(b["x1"], b_["x1"])
- bxs.pop(i + 1)
-
- callback__((min(to_page, self.total_page) - from_page) / self.total_page / 2,
- "Page {}~{}: Text extraction finished".format(from_page, min(to_page, self.total_page)), callback)
-
- return [b["text"] + self._line_tag(b, zoomin) for b in bxs]
-
-
- def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
- doc = {
- "docnm_kwd": filename,
- "title_tks": huqie.qie(re.sub(r"\.[a-zA-Z]+$", "", filename))
- }
- doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
- pdf_parser = None
- sections = []
- if re.search(r"\.docx?$", filename, re.IGNORECASE):
- for txt in Docx()(filename, binary):
- sections.append(txt)
- if re.search(r"\.pdf$", filename, re.IGNORECASE):
- pdf_parser = Pdf()
- for txt in pdf_parser(filename if not binary else binary,
- from_page=from_page, to_page=to_page, callback=callback):
- sections.append(txt)
- if re.search(r"\.txt$", filename, re.IGNORECASE):
- txt = ""
- if binary:txt = binary.decode("utf-8")
- else:
- with open(filename, "r") as f:
- while True:
- l = f.readline()
- if not l:break
- txt += l
- sections = txt.split("\n")
- sections = [l for l in sections if l]
-
- # is it English
- eng = 0
- for sec in sections:
- if re.match(r"[a-zA-Z]", sec.strip()):
- eng += 1
- if eng / len(sections) > 0.8:
- eng = True
- else:
- eng = False
- # Remove 'Contents' part
- i = 0
- while i < len(sections):
- if not re.match(r"(Contents|目录|目次)$", re.sub(r"( | |\u3000)+", "", sections[i].split("@@")[0])):
- i += 1
- continue
- sections.pop(i)
- if i >= len(sections): break
- prefix = sections[i].strip()[:3] if not eng else " ".join(sections[i].strip().split(" ")[:2])
- while not prefix:
- sections.pop(i)
- if i >= len(sections): break
- prefix = sections[i].strip()[:3] if not eng else " ".join(sections[i].strip().split(" ")[:2])
- sections.pop(i)
- if i >= len(sections) or not prefix: break
- for j in range(i, min(i+128, len(sections))):
- if not re.match(prefix, sections[j]):
- continue
- for k in range(i, j):sections.pop(i)
- break
-
- bull = bullets_category(sections)
- projs = [len(BULLET_PATTERN[bull])] * len(sections)
- for i, sec in enumerate(sections):
- for j,p in enumerate(BULLET_PATTERN[bull]):
- if re.match(p, sec.strip()):
- projs[i] = j
- break
- readed = [0] * len(sections)
- cks = []
- for pr in range(len(BULLET_PATTERN[bull])-1, 1, -1):
- for i in range(len(sections)):
- if readed[i] or projs[i] < pr:
- continue
- # find father and grand-father and grand...father
- p = projs[i]
- readed[i] = 1
- ck = [sections[i]]
- for j in range(i-1, -1, -1):
- if projs[j] >= p:continue
- ck.append(sections[j])
- readed[j] = 1
- p = projs[j]
- if p == 0: break
- cks.append(ck[::-1])
-
- res = []
- # wrap up to es documents
- for ck in cks:
- print("\n-".join(ck))
- ck = "\n".join(ck)
- d = copy.deepcopy(doc)
- if pdf_parser:
- d["image"] = pdf_parser.crop(ck)
- ck = pdf_parser.remove_tag(ck)
- d["content_ltks"] = huqie.qie(ck)
- d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
- res.append(d)
- return res
-
-
- if __name__ == "__main__":
- import sys
- chunk(sys.argv[1])
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