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						|  | import logging | 
					
						
						|  | import copy | 
					
						
						|  | import re | 
					
						
						|  |  | 
					
						
						|  | from api.db import ParserType | 
					
						
						|  | from io import BytesIO | 
					
						
						|  | from rag.nlp import rag_tokenizer, tokenize, tokenize_table, bullets_category, title_frequency, tokenize_chunks, docx_question_level | 
					
						
						|  | from rag.utils import num_tokens_from_string | 
					
						
						|  | from deepdoc.parser import PdfParser, PlainParser, DocxParser | 
					
						
						|  | from docx import Document | 
					
						
						|  | from PIL import Image | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | class Pdf(PdfParser): | 
					
						
						|  | def __init__(self): | 
					
						
						|  | self.model_speciess = ParserType.MANUAL.value | 
					
						
						|  | super().__init__() | 
					
						
						|  |  | 
					
						
						|  | def __call__(self, filename, binary=None, from_page=0, | 
					
						
						|  | to_page=100000, zoomin=3, callback=None): | 
					
						
						|  | from timeit import default_timer as timer | 
					
						
						|  | start = timer() | 
					
						
						|  | callback(msg="OCR is running...") | 
					
						
						|  | self.__images__( | 
					
						
						|  | filename if not binary else binary, | 
					
						
						|  | zoomin, | 
					
						
						|  | from_page, | 
					
						
						|  | to_page, | 
					
						
						|  | callback | 
					
						
						|  | ) | 
					
						
						|  | callback(msg="OCR finished.") | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | logging.debug("OCR: {}".format(timer() - start)) | 
					
						
						|  |  | 
					
						
						|  | self._layouts_rec(zoomin) | 
					
						
						|  | callback(0.65, "Layout analysis finished.") | 
					
						
						|  | logging.debug("layouts: {}".format(timer() - start)) | 
					
						
						|  | self._table_transformer_job(zoomin) | 
					
						
						|  | callback(0.67, "Table analysis finished.") | 
					
						
						|  | self._text_merge() | 
					
						
						|  | tbls = self._extract_table_figure(True, zoomin, True, True) | 
					
						
						|  | self._concat_downward() | 
					
						
						|  | self._filter_forpages() | 
					
						
						|  | callback(0.68, "Text merging finished") | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | for b in self.boxes: | 
					
						
						|  | b["text"] = re.sub(r"([\t  ]|\u3000){2,}", " ", b["text"].strip()) | 
					
						
						|  |  | 
					
						
						|  | return [(b["text"], b.get("layout_no", ""), self.get_position(b, zoomin)) | 
					
						
						|  | for i, b in enumerate(self.boxes)], tbls | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | class Docx(DocxParser): | 
					
						
						|  | def __init__(self): | 
					
						
						|  | pass | 
					
						
						|  |  | 
					
						
						|  | def get_picture(self, document, paragraph): | 
					
						
						|  | img = paragraph._element.xpath('.//pic:pic') | 
					
						
						|  | if not img: | 
					
						
						|  | return None | 
					
						
						|  | img = img[0] | 
					
						
						|  | embed = img.xpath('.//a:blip/@r:embed')[0] | 
					
						
						|  | related_part = document.part.related_parts[embed] | 
					
						
						|  | image = related_part.image | 
					
						
						|  | image = Image.open(BytesIO(image.blob)) | 
					
						
						|  | return image | 
					
						
						|  |  | 
					
						
						|  | def concat_img(self, img1, img2): | 
					
						
						|  | if img1 and not img2: | 
					
						
						|  | return img1 | 
					
						
						|  | if not img1 and img2: | 
					
						
						|  | return img2 | 
					
						
						|  | if not img1 and not img2: | 
					
						
						|  | return None | 
					
						
						|  | width1, height1 = img1.size | 
					
						
						|  | width2, height2 = img2.size | 
					
						
						|  |  | 
					
						
						|  | new_width = max(width1, width2) | 
					
						
						|  | new_height = height1 + height2 | 
					
						
						|  | new_image = Image.new('RGB', (new_width, new_height)) | 
					
						
						|  |  | 
					
						
						|  | new_image.paste(img1, (0, 0)) | 
					
						
						|  | new_image.paste(img2, (0, height1)) | 
					
						
						|  |  | 
					
						
						|  | return new_image | 
					
						
						|  |  | 
					
						
						|  | def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None): | 
					
						
						|  | self.doc = Document( | 
					
						
						|  | filename) if not binary else Document(BytesIO(binary)) | 
					
						
						|  | pn = 0 | 
					
						
						|  | last_answer, last_image = "", None | 
					
						
						|  | question_stack, level_stack = [], [] | 
					
						
						|  | ti_list = [] | 
					
						
						|  | for p in self.doc.paragraphs: | 
					
						
						|  | if pn > to_page: | 
					
						
						|  | break | 
					
						
						|  | question_level, p_text = 0, '' | 
					
						
						|  | if from_page <= pn < to_page and p.text.strip(): | 
					
						
						|  | question_level, p_text = docx_question_level(p) | 
					
						
						|  | if not question_level or question_level > 6: | 
					
						
						|  | last_answer = f'{last_answer}\n{p_text}' | 
					
						
						|  | current_image = self.get_picture(self.doc, p) | 
					
						
						|  | last_image = self.concat_img(last_image, current_image) | 
					
						
						|  | else: | 
					
						
						|  | if last_answer or last_image: | 
					
						
						|  | sum_question = '\n'.join(question_stack) | 
					
						
						|  | if sum_question: | 
					
						
						|  | ti_list.append((f'{sum_question}\n{last_answer}', last_image)) | 
					
						
						|  | last_answer, last_image = '', None | 
					
						
						|  |  | 
					
						
						|  | i = question_level | 
					
						
						|  | while question_stack and i <= level_stack[-1]: | 
					
						
						|  | question_stack.pop() | 
					
						
						|  | level_stack.pop() | 
					
						
						|  | question_stack.append(p_text) | 
					
						
						|  | level_stack.append(question_level) | 
					
						
						|  | for run in p.runs: | 
					
						
						|  | if 'lastRenderedPageBreak' in run._element.xml: | 
					
						
						|  | pn += 1 | 
					
						
						|  | continue | 
					
						
						|  | if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: | 
					
						
						|  | pn += 1 | 
					
						
						|  | if last_answer: | 
					
						
						|  | sum_question = '\n'.join(question_stack) | 
					
						
						|  | if sum_question: | 
					
						
						|  | ti_list.append((f'{sum_question}\n{last_answer}', last_image)) | 
					
						
						|  |  | 
					
						
						|  | tbls = [] | 
					
						
						|  | for tb in self.doc.tables: | 
					
						
						|  | html= "<table>" | 
					
						
						|  | for r in tb.rows: | 
					
						
						|  | html += "<tr>" | 
					
						
						|  | i = 0 | 
					
						
						|  | while i < len(r.cells): | 
					
						
						|  | span = 1 | 
					
						
						|  | c = r.cells[i] | 
					
						
						|  | for j in range(i+1, len(r.cells)): | 
					
						
						|  | if c.text == r.cells[j].text: | 
					
						
						|  | span += 1 | 
					
						
						|  | i = j | 
					
						
						|  | i += 1 | 
					
						
						|  | html += f"<td>{c.text}</td>" if span == 1 else f"<td colspan='{span}'>{c.text}</td>" | 
					
						
						|  | html += "</tr>" | 
					
						
						|  | html += "</table>" | 
					
						
						|  | tbls.append(((None, html), "")) | 
					
						
						|  | return ti_list, tbls | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | def chunk(filename, binary=None, from_page=0, to_page=100000, | 
					
						
						|  | lang="Chinese", callback=None, **kwargs): | 
					
						
						|  | """ | 
					
						
						|  | Only pdf is supported. | 
					
						
						|  | """ | 
					
						
						|  | pdf_parser = None | 
					
						
						|  | doc = { | 
					
						
						|  | "docnm_kwd": filename | 
					
						
						|  | } | 
					
						
						|  | doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"])) | 
					
						
						|  | doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) | 
					
						
						|  |  | 
					
						
						|  | eng = lang.lower() == "english" | 
					
						
						|  | if re.search(r"\.pdf$", filename, re.IGNORECASE): | 
					
						
						|  | pdf_parser = Pdf() if kwargs.get( | 
					
						
						|  | "parser_config", {}).get( | 
					
						
						|  | "layout_recognize", True) else PlainParser() | 
					
						
						|  | sections, tbls = pdf_parser(filename if not binary else binary, | 
					
						
						|  | from_page=from_page, to_page=to_page, callback=callback) | 
					
						
						|  | if sections and len(sections[0]) < 3: | 
					
						
						|  | sections = [(t, l, [[0] * 5]) for t, l in sections] | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | if len(sections) > 0 and len(pdf_parser.outlines) / len(sections) > 0.1: | 
					
						
						|  | max_lvl = max([lvl for _, lvl in pdf_parser.outlines]) | 
					
						
						|  | most_level = max(0, max_lvl - 1) | 
					
						
						|  | levels = [] | 
					
						
						|  | for txt, _, _ in sections: | 
					
						
						|  | for t, lvl in pdf_parser.outlines: | 
					
						
						|  | tks = set([t[i] + t[i + 1] for i in range(len(t) - 1)]) | 
					
						
						|  | tks_ = set([txt[i] + txt[i + 1] | 
					
						
						|  | for i in range(min(len(t), len(txt) - 1))]) | 
					
						
						|  | if len(set(tks & tks_)) / max([len(tks), len(tks_), 1]) > 0.8: | 
					
						
						|  | levels.append(lvl) | 
					
						
						|  | break | 
					
						
						|  | else: | 
					
						
						|  | levels.append(max_lvl + 1) | 
					
						
						|  |  | 
					
						
						|  | else: | 
					
						
						|  | bull = bullets_category([txt for txt, _, _ in sections]) | 
					
						
						|  | most_level, levels = title_frequency( | 
					
						
						|  | bull, [(txt, l) for txt, l, poss in sections]) | 
					
						
						|  |  | 
					
						
						|  | assert len(sections) == len(levels) | 
					
						
						|  | sec_ids = [] | 
					
						
						|  | sid = 0 | 
					
						
						|  | for i, lvl in enumerate(levels): | 
					
						
						|  | if lvl <= most_level and i > 0 and lvl != levels[i - 1]: | 
					
						
						|  | sid += 1 | 
					
						
						|  | sec_ids.append(sid) | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | sections = [(txt, sec_ids[i], poss) | 
					
						
						|  | for i, (txt, _, poss) in enumerate(sections)] | 
					
						
						|  | for (img, rows), poss in tbls: | 
					
						
						|  | if not rows: continue | 
					
						
						|  | sections.append((rows if isinstance(rows, str) else rows[0], -1, | 
					
						
						|  | [(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss])) | 
					
						
						|  |  | 
					
						
						|  | def tag(pn, left, right, top, bottom): | 
					
						
						|  | if pn + left + right + top + bottom == 0: | 
					
						
						|  | return "" | 
					
						
						|  | return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \ | 
					
						
						|  | .format(pn, left, right, top, bottom) | 
					
						
						|  |  | 
					
						
						|  | chunks = [] | 
					
						
						|  | last_sid = -2 | 
					
						
						|  | tk_cnt = 0 | 
					
						
						|  | for txt, sec_id, poss in sorted(sections, key=lambda x: ( | 
					
						
						|  | x[-1][0][0], x[-1][0][3], x[-1][0][1])): | 
					
						
						|  | poss = "\t".join([tag(*pos) for pos in poss]) | 
					
						
						|  | if tk_cnt < 32 or (tk_cnt < 1024 and (sec_id == last_sid or sec_id == -1)): | 
					
						
						|  | if chunks: | 
					
						
						|  | chunks[-1] += "\n" + txt + poss | 
					
						
						|  | tk_cnt += num_tokens_from_string(txt) | 
					
						
						|  | continue | 
					
						
						|  | chunks.append(txt + poss) | 
					
						
						|  | tk_cnt = num_tokens_from_string(txt) | 
					
						
						|  | if sec_id > -1: | 
					
						
						|  | last_sid = sec_id | 
					
						
						|  |  | 
					
						
						|  | res = tokenize_table(tbls, doc, eng) | 
					
						
						|  | res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser)) | 
					
						
						|  | return res | 
					
						
						|  |  | 
					
						
						|  | if re.search(r"\.docx$", filename, re.IGNORECASE): | 
					
						
						|  | docx_parser = Docx() | 
					
						
						|  | ti_list, tbls = docx_parser(filename, binary, | 
					
						
						|  | from_page=0, to_page=10000, callback=callback) | 
					
						
						|  | res = tokenize_table(tbls, doc, eng) | 
					
						
						|  | for text, image in ti_list: | 
					
						
						|  | d = copy.deepcopy(doc) | 
					
						
						|  | d['image'] = image | 
					
						
						|  | tokenize(d, text, eng) | 
					
						
						|  | res.append(d) | 
					
						
						|  | return res | 
					
						
						|  | else: | 
					
						
						|  | raise NotImplementedError("file type not supported yet(pdf and docx supported)") | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | if __name__ == "__main__": | 
					
						
						|  | import sys | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | def dummy(prog=None, msg=""): | 
					
						
						|  | pass | 
					
						
						|  |  | 
					
						
						|  |  | 
					
						
						|  | chunk(sys.argv[1], callback=dummy) |