ragflow/rag/app/paper.py

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9.3 KiB
Python
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import copy
import re
from collections import Counter
from api.db import ParserType
from rag.cv.ppdetection import PPDet
from rag.parser import tokenize
from rag.nlp import huqie
from rag.parser.pdf_parser import HuParser
import numpy as np
from rag.utils import num_tokens_from_string
class Pdf(HuParser):
def __init__(self):
self.model_speciess = ParserType.PAPER.value
super().__init__()
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(0.2, "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()
column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
self._concat_downward(concat_between_pages=False)
self._filter_forpages()
callback(0.75, "Text merging finished.")
tbls = self._extract_table_figure(True, zoomin, False)
# clean mess
if column_width < self.page_images[0].size[0] / zoomin / 2:
print("two_column...................", column_width,
self.page_images[0].size[0] / zoomin / 2)
self.boxes = self.sort_X_by_page(self.boxes, column_width / 2)
for b in self.boxes:
b["text"] = re.sub(r"([\t  ]|\u3000){2,}", " ", b["text"].strip())
freq = Counter([b["text"] for b in self.boxes])
garbage = set([k for k, v in freq.items() if v > self.total_page * 0.6])
i = 0
while i < len(self.boxes):
if self.boxes[i]["text"] in garbage \
or (re.match(r"[a-zA-Z0-9]+$", self.boxes[i]["text"]) and not self.boxes[i].get("layoutno")) \
or (i + 1 < len(self.boxes) and self.boxes[i]["text"] == self.boxes[i + 1]["text"]):
self.boxes.pop(i)
elif i + 1 < len(self.boxes) and self.boxes[i].get("layoutno", '0') == self.boxes[i + 1].get("layoutno",
'1'):
# merge within same layouts
self.boxes[i + 1]["top"] = self.boxes[i]["top"]
self.boxes[i + 1]["x0"] = min(self.boxes[i]["x0"], self.boxes[i + 1]["x0"])
self.boxes[i + 1]["x1"] = max(self.boxes[i]["x1"], self.boxes[i + 1]["x1"])
self.boxes[i + 1]["text"] = self.boxes[i]["text"] + " " + self.boxes[i + 1]["text"]
self.boxes.pop(i)
else:
i += 1
def _begin(txt):
return re.match(
"[0-9. 一、i]*(introduction|abstract|摘要|引言|keywords|key words|关键词|background|背景|目录|前言|contents)",
txt.lower().strip())
if from_page > 0:
return {
"title":"",
"authors": "",
"abstract": "",
"lines": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes[i:] if
re.match(r"(text|title)", b.get("layoutno", "text"))],
"tables": tbls
}
# get title and authors
title = ""
authors = []
i = 0
while i < min(32, len(self.boxes)):
b = self.boxes[i]
i += 1
if b.get("layoutno", "").find("title") >= 0:
title = b["text"]
if _begin(title):
title = ""
break
for j in range(3):
if _begin(self.boxes[i + j]["text"]): break
authors.append(self.boxes[i + j]["text"])
break
break
# get abstract
abstr = ""
i = 0
while i + 1 < min(32, len(self.boxes)):
b = self.boxes[i]
i += 1
txt = b["text"].lower().strip()
if re.match("(abstract|摘要)", txt):
if len(txt.split(" ")) > 32 or len(txt) > 64:
abstr = txt + self._line_tag(b, zoomin)
i += 1
break
txt = self.boxes[i + 1]["text"].lower().strip()
if len(txt.split(" ")) > 32 or len(txt) > 64:
abstr = txt + self._line_tag(self.boxes[i + 1], zoomin)
i += 1
break
if not abstr: i = 0
callback(0.8, "Page {}~{}: Text merging finished".format(from_page, min(to_page, self.total_page)))
for b in self.boxes: print(b["text"], b.get("layoutno"))
print(tbls)
return {
"title": title if title else filename,
"authors": " ".join(authors),
"abstract": abstr,
"lines": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes[i:] if
re.match(r"(text|title)", b.get("layoutno", "text"))],
"tables": tbls
}
def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None, **kwargs):
pdf_parser = None
if re.search(r"\.pdf$", filename, re.IGNORECASE):
pdf_parser = Pdf()
paper = pdf_parser(filename if not binary else binary,
from_page=from_page, to_page=to_page, callback=callback)
else: raise NotImplementedError("file type not supported yet(pdf supported)")
doc = {"docnm_kwd": filename, "authors_tks": paper["authors"],
"title_tks": huqie.qie(paper["title"] if paper["title"] else filename)}
doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
doc["authors_sm_tks"] = huqie.qieqie(doc["authors_tks"])
# is it English
eng = pdf_parser.is_english
print("It's English.....", eng)
res = []
# add tables
for img, rows in paper["tables"]:
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)
d["image"] = img
res.append(d)
if paper["abstract"]:
d = copy.deepcopy(doc)
txt = pdf_parser.remove_tag(paper["abstract"])
d["important_kwd"] = ["abstract", "总结", "概括", "summary", "summarize"]
d["important_tks"] = " ".join(d["important_kwd"])
d["image"] = pdf_parser.crop(paper["abstract"])
tokenize(d, txt, eng)
res.append(d)
readed = [0] * len(paper["lines"])
# find colon firstly
i = 0
while i + 1 < len(paper["lines"]):
txt = pdf_parser.remove_tag(paper["lines"][i][0])
j = i
if txt.strip("\n").strip()[-1] not in ":":
i += 1
continue
i += 1
while i < len(paper["lines"]) and not paper["lines"][i][0]:
i += 1
if i >= len(paper["lines"]): break
proj = [paper["lines"][i][0].strip()]
i += 1
while i < len(paper["lines"]) and paper["lines"][i][0].strip()[0] == proj[-1][0]:
proj.append(paper["lines"][i])
i += 1
for k in range(j, i): readed[k] = True
txt = txt[::-1]
if eng:
r = re.search(r"(.*?) ([\.;?!]|$)", txt)
txt = r.group(1)[::-1] if r else txt[::-1]
else:
r = re.search(r"(.*?) ([。?;!]|$)", txt)
txt = r.group(1)[::-1] if r else txt[::-1]
for p in proj:
d = copy.deepcopy(doc)
txt += "\n" + pdf_parser.remove_tag(p)
d["image"] = pdf_parser.crop(p)
tokenize(d, txt)
res.append(d)
i = 0
chunk = []
tk_cnt = 0
def add_chunk():
nonlocal chunk, res, doc, pdf_parser, tk_cnt
d = copy.deepcopy(doc)
ck = "\n".join(chunk)
tokenize(d, pdf_parser.remove_tag(ck), pdf_parser.is_english)
d["image"] = pdf_parser.crop(ck)
res.append(d)
chunk = []
tk_cnt = 0
while i < len(paper["lines"]):
if tk_cnt > 128:
add_chunk()
if readed[i]:
i += 1
continue
readed[i] = True
txt, layouts = paper["lines"][i]
txt_ = pdf_parser.remove_tag(txt)
i += 1
cnt = num_tokens_from_string(txt_)
if any([
layouts.find("title") >= 0 and chunk,
cnt + tk_cnt > 128 and tk_cnt > 32,
]):
add_chunk()
chunk = [txt]
tk_cnt = cnt
else:
chunk.append(txt)
tk_cnt += cnt
if chunk: add_chunk()
for i, d in enumerate(res):
print(d)
# d["image"].save(f"./logs/{i}.jpg")
return res
if __name__ == "__main__":
import sys
def dummy(a, b):
pass
chunk(sys.argv[1], callback=dummy)