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init README of deepdoc, add picture processer. (#71)
* init README of deepdoc, add picture processer. * add resume parsing
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KevinHuSh
@ -1,223 +1,8 @@
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import random
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from .pdf_parser import HuParser as PdfParser
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from .docx_parser import HuDocxParser as DocxParser
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from .excel_parser import HuExcelParser as ExcelParser
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import re
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from nltk import word_tokenize
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from rag.nlp import stemmer, huqie
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from rag.utils import num_tokens_from_string
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BULLET_PATTERN = [[
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r"第[零一二三四五六七八九十百0-9]+(分?编|部分)",
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r"第[零一二三四五六七八九十百0-9]+章",
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r"第[零一二三四五六七八九十百0-9]+节",
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r"第[零一二三四五六七八九十百0-9]+条",
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r"[\(（][零一二三四五六七八九十百]+[\)）]",
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], [
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r"第[0-9]+章",
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r"第[0-9]+节",
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r"[0-9]{,3}[\. 、]",
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r"[0-9]{,2}\.[0-9]{,2}",
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r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
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r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
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], [
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r"第[零一二三四五六七八九十百0-9]+章",
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r"第[零一二三四五六七八九十百0-9]+节",
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r"[零一二三四五六七八九十百]+[ 、]",
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r"[\(（][零一二三四五六七八九十百]+[\)）]",
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r"[\(（][0-9]{,2}[\)）]",
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], [
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r"PART (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)",
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r"Chapter (I+V?|VI*|XI|IX|X)",
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r"Section [0-9]+",
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r"Article [0-9]+"
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]
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]
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def random_choices(arr, k):
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k = min(len(arr), k)
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return random.choices(arr, k=k)
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def bullets_category(sections):
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global BULLET_PATTERN
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hits = [0] * len(BULLET_PATTERN)
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for i, pro in enumerate(BULLET_PATTERN):
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for sec in sections:
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for p in pro:
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if re.match(p, sec):
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hits[i] += 1
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break
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maxium = 0
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res = -1
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for i, h in enumerate(hits):
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if h <= maxium: continue
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res = i
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maxium = h
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return res
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def is_english(texts):
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eng = 0
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for t in texts:
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if re.match(r"[a-zA-Z]{2,}", t.strip()):
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eng += 1
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if eng / len(texts) > 0.8:
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return True
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return False
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def tokenize(d, t, eng):
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d["content_with_weight"] = t
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if eng:
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t = re.sub(r"([a-z])-([a-z])", r"\1\2", t)
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d["content_ltks"] = " ".join([stemmer.stem(w) for w in word_tokenize(t)])
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else:
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d["content_ltks"] = huqie.qie(t)
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d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
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def remove_contents_table(sections, eng=False):
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i = 0
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while i < len(sections):
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def get(i):
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nonlocal sections
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return (sections[i] if type(sections[i]) == type("") else sections[i][0]).strip()
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if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$",
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re.sub(r"( | |\u3000)+", "", get(i).split("@@")[0], re.IGNORECASE)):
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i += 1
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continue
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sections.pop(i)
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if i >= len(sections): break
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prefix = get(i)[:3] if not eng else " ".join(get(i).split(" ")[:2])
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while not prefix:
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sections.pop(i)
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if i >= len(sections): break
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prefix = get(i)[:3] if not eng else " ".join(get(i).split(" ")[:2])
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sections.pop(i)
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if i >= len(sections) or not prefix: break
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for j in range(i, min(i + 128, len(sections))):
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if not re.match(prefix, get(j)):
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continue
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for _ in range(i, j): sections.pop(i)
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break
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def make_colon_as_title(sections):
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if not sections: return []
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if type(sections[0]) == type(""): return sections
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i = 0
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while i < len(sections):
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txt, layout = sections[i]
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i += 1
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txt = txt.split("@")[0].strip()
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if not txt:
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continue
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if txt[-1] not in ":：":
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continue
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txt = txt[::-1]
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arr = re.split(r"([。？！!?;；]| .)", txt)
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if len(arr) < 2 or len(arr[1]) < 32:
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continue
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sections.insert(i - 1, (arr[0][::-1], "title"))
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i += 1
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def hierarchical_merge(bull, sections, depth):
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if not sections or bull < 0: return []
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if type(sections[0]) == type(""): sections = [(s, "") for s in sections]
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sections = [(t,o) for t, o in sections if t and len(t.split("@")[0].strip()) > 1 and not re.match(r"[0-9]+$", t.split("@")[0].strip())]
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bullets_size = len(BULLET_PATTERN[bull])
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levels = [[] for _ in range(bullets_size + 2)]
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def not_title(txt):
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if re.match(r"第[零一二三四五六七八九十百0-9]+条", txt): return False
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if len(txt) >= 128: return True
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return re.search(r"[,;，。；！!]", txt)
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for i, (txt, layout) in enumerate(sections):
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for j, p in enumerate(BULLET_PATTERN[bull]):
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if re.match(p, txt.strip()) and not not_title(txt):
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levels[j].append(i)
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break
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else:
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if re.search(r"(title|head)", layout):
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levels[bullets_size].append(i)
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else:
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levels[bullets_size + 1].append(i)
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sections = [t for t, _ in sections]
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for s in sections: print("--", s)
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def binary_search(arr, target):
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if not arr: return -1
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if target > arr[-1]: return len(arr) - 1
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if target < arr[0]: return -1
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s, e = 0, len(arr)
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while e - s > 1:
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i = (e + s) // 2
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if target > arr[i]:
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s = i
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continue
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elif target < arr[i]:
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e = i
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continue
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else:
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assert False
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return s
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cks = []
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readed = [False] * len(sections)
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levels = levels[::-1]
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for i, arr in enumerate(levels[:depth]):
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for j in arr:
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if readed[j]: continue
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readed[j] = True
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cks.append([j])
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if i + 1 == len(levels) - 1: continue
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for ii in range(i + 1, len(levels)):
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jj = binary_search(levels[ii], j)
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if jj < 0: continue
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if jj > cks[-1][-1]: cks[-1].pop(-1)
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cks[-1].append(levels[ii][jj])
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for ii in cks[-1]: readed[ii] = True
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for i in range(len(cks)):
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cks[i] = [sections[j] for j in cks[i][::-1]]
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print("--------------\n", "\n* ".join(cks[i]))
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return cks
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def naive_merge(sections, chunk_token_num=128, delimiter="\n。；！？"):
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if not sections: return []
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if type(sections[0]) == type(""): sections = [(s, "") for s in sections]
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cks = [""]
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tk_nums = [0]
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def add_chunk(t, pos):
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nonlocal cks, tk_nums, delimiter
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tnum = num_tokens_from_string(t)
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if tnum < 8: pos = ""
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if tk_nums[-1] > chunk_token_num:
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cks.append(t + pos)
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tk_nums.append(tnum)
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else:
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cks[-1] += t + pos
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tk_nums[-1] += tnum
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for sec, pos in sections:
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s, e = 0, 1
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while e < len(sec):
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if sec[e] in delimiter:
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add_chunk(sec[s: e+1], pos)
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s = e + 1
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e = s + 1
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else:
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e += 1
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if s < e: add_chunk(sec[s: e], pos)
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return cks
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from .ppt_parser import HuPptParser as PptParser
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