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3894de895b
### What problem does this PR solve? Add license statement. ### Type of change - [x] Refactoring Signed-off-by: Jin Hai <haijin.chn@gmail.com>
283 lines
10 KiB
Python
283 lines
10 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import copy
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import re
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from api.db import ParserType
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from io import BytesIO
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from rag.nlp import rag_tokenizer, tokenize, tokenize_table, bullets_category, title_frequency, tokenize_chunks, docx_question_level
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from rag.utils import num_tokens_from_string
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from deepdoc.parser import PdfParser, PlainParser, DocxParser
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from docx import Document
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from PIL import Image
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class Pdf(PdfParser):
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def __init__(self):
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self.model_speciess = ParserType.MANUAL.value
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super().__init__()
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def __call__(self, filename, binary=None, from_page=0,
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to_page=100000, zoomin=3, callback=None):
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from timeit import default_timer as timer
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start = timer()
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callback(msg="OCR started")
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self.__images__(
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filename if not binary else binary,
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zoomin,
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from_page,
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to_page,
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callback
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)
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callback(msg="OCR finished ({:.2f}s)".format(timer() - start))
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# for bb in self.boxes:
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# for b in bb:
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# print(b)
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logging.debug("OCR: {}".format(timer() - start))
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start = timer()
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self._layouts_rec(zoomin)
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callback(0.65, "Layout analysis ({:.2f}s)".format(timer() - start))
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logging.debug("layouts: {}".format(timer() - start))
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start = timer()
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self._table_transformer_job(zoomin)
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callback(0.67, "Table analysis ({:.2f}s)".format(timer() - start))
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start = timer()
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self._text_merge()
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tbls = self._extract_table_figure(True, zoomin, True, True)
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self._concat_downward()
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self._filter_forpages()
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callback(0.68, "Text merged ({:.2f}s)".format(timer() - start))
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# clean mess
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for b in self.boxes:
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b["text"] = re.sub(r"([\t ]|\u3000){2,}", " ", b["text"].strip())
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return [(b["text"], b.get("layoutno", ""), self.get_position(b, zoomin))
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for i, b in enumerate(self.boxes)], tbls
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class Docx(DocxParser):
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def __init__(self):
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pass
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def get_picture(self, document, paragraph):
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img = paragraph._element.xpath('.//pic:pic')
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if not img:
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return None
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img = img[0]
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embed = img.xpath('.//a:blip/@r:embed')[0]
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related_part = document.part.related_parts[embed]
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image = related_part.image
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image = Image.open(BytesIO(image.blob))
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return image
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def concat_img(self, img1, img2):
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if img1 and not img2:
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return img1
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if not img1 and img2:
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return img2
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if not img1 and not img2:
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return None
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width1, height1 = img1.size
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width2, height2 = img2.size
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new_width = max(width1, width2)
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new_height = height1 + height2
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new_image = Image.new('RGB', (new_width, new_height))
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new_image.paste(img1, (0, 0))
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new_image.paste(img2, (0, height1))
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return new_image
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def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None):
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self.doc = Document(
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filename) if not binary else Document(BytesIO(binary))
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pn = 0
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last_answer, last_image = "", None
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question_stack, level_stack = [], []
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ti_list = []
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for p in self.doc.paragraphs:
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if pn > to_page:
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break
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question_level, p_text = 0, ''
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if from_page <= pn < to_page and p.text.strip():
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question_level, p_text = docx_question_level(p)
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if not question_level or question_level > 6: # not a question
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last_answer = f'{last_answer}\n{p_text}'
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current_image = self.get_picture(self.doc, p)
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last_image = self.concat_img(last_image, current_image)
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else: # is a question
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if last_answer or last_image:
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sum_question = '\n'.join(question_stack)
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if sum_question:
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ti_list.append((f'{sum_question}\n{last_answer}', last_image))
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last_answer, last_image = '', None
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i = question_level
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while question_stack and i <= level_stack[-1]:
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question_stack.pop()
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level_stack.pop()
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question_stack.append(p_text)
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level_stack.append(question_level)
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for run in p.runs:
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if 'lastRenderedPageBreak' in run._element.xml:
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pn += 1
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continue
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if 'w:br' in run._element.xml and 'type="page"' in run._element.xml:
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pn += 1
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if last_answer:
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sum_question = '\n'.join(question_stack)
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if sum_question:
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ti_list.append((f'{sum_question}\n{last_answer}', last_image))
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tbls = []
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for tb in self.doc.tables:
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html= "<table>"
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for r in tb.rows:
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html += "<tr>"
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i = 0
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while i < len(r.cells):
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span = 1
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c = r.cells[i]
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for j in range(i+1, len(r.cells)):
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if c.text == r.cells[j].text:
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span += 1
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i = j
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i += 1
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html += f"<td>{c.text}</td>" if span == 1 else f"<td colspan='{span}'>{c.text}</td>"
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html += "</tr>"
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html += "</table>"
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tbls.append(((None, html), ""))
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return ti_list, tbls
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def chunk(filename, binary=None, from_page=0, to_page=100000,
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lang="Chinese", callback=None, **kwargs):
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"""
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Only pdf is supported.
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"""
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pdf_parser = None
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doc = {
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"docnm_kwd": filename
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}
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doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"]))
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doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
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# is it English
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eng = lang.lower() == "english" # pdf_parser.is_english
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if re.search(r"\.pdf$", filename, re.IGNORECASE):
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pdf_parser = Pdf() if kwargs.get(
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"parser_config", {}).get(
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"layout_recognize", True) else PlainParser()
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sections, tbls = pdf_parser(filename if not binary else binary,
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from_page=from_page, to_page=to_page, callback=callback)
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if sections and len(sections[0]) < 3:
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sections = [(t, lvl, [[0] * 5]) for t, lvl in sections]
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# set pivot using the most frequent type of title,
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# then merge between 2 pivot
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if len(sections) > 0 and len(pdf_parser.outlines) / len(sections) > 0.03:
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max_lvl = max([lvl for _, lvl in pdf_parser.outlines])
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most_level = max(0, max_lvl - 1)
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levels = []
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for txt, _, _ in sections:
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for t, lvl in pdf_parser.outlines:
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tks = set([t[i] + t[i + 1] for i in range(len(t) - 1)])
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tks_ = set([txt[i] + txt[i + 1]
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for i in range(min(len(t), len(txt) - 1))])
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if len(set(tks & tks_)) / max([len(tks), len(tks_), 1]) > 0.8:
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levels.append(lvl)
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break
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else:
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levels.append(max_lvl + 1)
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else:
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bull = bullets_category([txt for txt, _, _ in sections])
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most_level, levels = title_frequency(
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bull, [(txt, lvl) for txt, lvl, _ in sections])
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assert len(sections) == len(levels)
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sec_ids = []
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sid = 0
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for i, lvl in enumerate(levels):
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if lvl <= most_level and i > 0 and lvl != levels[i - 1]:
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sid += 1
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sec_ids.append(sid)
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# print(lvl, self.boxes[i]["text"], most_level, sid)
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sections = [(txt, sec_ids[i], poss)
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for i, (txt, _, poss) in enumerate(sections)]
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for (img, rows), poss in tbls:
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if not rows:
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continue
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sections.append((rows if isinstance(rows, str) else rows[0], -1,
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[(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss]))
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def tag(pn, left, right, top, bottom):
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if pn + left + right + top + bottom == 0:
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return ""
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return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \
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.format(pn, left, right, top, bottom)
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chunks = []
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last_sid = -2
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tk_cnt = 0
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for txt, sec_id, poss in sorted(sections, key=lambda x: (
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x[-1][0][0], x[-1][0][3], x[-1][0][1])):
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poss = "\t".join([tag(*pos) for pos in poss])
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if tk_cnt < 32 or (tk_cnt < 1024 and (sec_id == last_sid or sec_id == -1)):
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if chunks:
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chunks[-1] += "\n" + txt + poss
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tk_cnt += num_tokens_from_string(txt)
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continue
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chunks.append(txt + poss)
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tk_cnt = num_tokens_from_string(txt)
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if sec_id > -1:
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last_sid = sec_id
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res = tokenize_table(tbls, doc, eng)
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res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
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return res
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elif re.search(r"\.docx?$", filename, re.IGNORECASE):
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docx_parser = Docx()
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ti_list, tbls = docx_parser(filename, binary,
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from_page=0, to_page=10000, callback=callback)
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res = tokenize_table(tbls, doc, eng)
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for text, image in ti_list:
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d = copy.deepcopy(doc)
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d['image'] = image
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tokenize(d, text, eng)
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res.append(d)
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return res
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else:
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raise NotImplementedError("file type not supported yet(pdf and docx supported)")
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if __name__ == "__main__":
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import sys
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def dummy(prog=None, msg=""):
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pass
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chunk(sys.argv[1], callback=dummy)
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