import copy import re from rag.app import callback__, tokenize from rag.nlp import huqie from rag.parser.pdf_parser import HuParser from rag.utils import num_tokens_from_string 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__(0.2, "OCR finished.", callback) from timeit import default_timer as timer start = timer() self._layouts_paddle(zoomin) callback__(0.5, "Layout analysis finished.", callback) print("paddle layouts:", timer() - start) self._table_transformer_job(zoomin) callback__(0.7, "Table analysis finished.", callback) self._text_merge() self._concat_downward(concat_between_pages=False) self._filter_forpages() callback__(0.77, "Text merging finished", callback) tbls = self._extract_table_figure(True, zoomin, False) # clean mess for b in self.boxes: b["text"] = re.sub(r"([\t  ]|\u3000){2,}", " ", b["text"].strip()) # merge chunks with the same bullets i = 0 while i + 1 < len(self.boxes): b = self.boxes[i] b_ = self.boxes[i + 1] if b["text"].strip()[0] != b_["text"].strip()[0] \ or b["page_number"]!=b_["page_number"] \ or b["top"] > b_["bottom"]: i += 1 continue b_["text"] = b["text"] + "\n" + b_["text"] b_["x0"] = min(b["x0"], b_["x0"]) b_["x1"] = max(b["x1"], b_["x1"]) b_["top"] = b["top"] self.boxes.pop(i) # merge title with decent chunk i = 0 while i + 1 < len(self.boxes): b = self.boxes[i] if b.get("layoutno","").find("title") < 0: i += 1 continue b_ = self.boxes[i + 1] b_["text"] = b["text"] + "\n" + b_["text"] b_["x0"] = min(b["x0"], b_["x0"]) b_["x1"] = max(b["x1"], b_["x1"]) b_["top"] = b["top"] self.boxes.pop(i) callback__(0.8, "Parsing finished", callback) for b in self.boxes: print(b["text"], b.get("layoutno")) print(tbls) return [b["text"] + self._line_tag(b, zoomin) for b in self.boxes], tbls def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None): pdf_parser = None paper = {} if re.search(r"\.pdf$", filename, re.IGNORECASE): pdf_parser = Pdf() cks, tbls = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, callback=callback) else: raise NotImplementedError("file type not supported yet(pdf supported)") doc = { "docnm_kwd": filename } doc["title_tks"] = huqie.qie(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"])) doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"]) # is it English eng = pdf_parser.is_english 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) 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(cks): if tk_cnt > 128: add_chunk() txt = cks[i] txt_ = pdf_parser.remove_tag(txt) i += 1 cnt = num_tokens_from_string(txt_) 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 chunk(sys.argv[1])