Merge pull request #1261 from opendatalab/release-0.10.6

Release 0.10.6

.github/workflows/cli.yml

@@ -30,7 +30,7 @@ jobs:
         source activate mineru
         conda env list
         pip show coverage
-        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
         cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
         cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
         cd $GITHUB_WORKSPACE && python tests/get_coverage.py
@@ -41,22 +41,6 @@ jobs:
     needs: cli-test
     runs-on: pdf
     steps:
-    - name: get_actor
-      run: |
-          metion_list="dt-yy"
-          echo $GITHUB_ACTOR
-          if [[ $GITHUB_ACTOR == "drunkpig" ]]; then
-            metion_list="xuchao"
-          elif [[ $GITHUB_ACTOR == "myhloli" ]]; then
-            metion_list="zhaoxiaomeng"
-          elif [[ $GITHUB_ACTOR == "icecraft" ]]; then
-            metion_list="xurui1"
-          fi
-          echo $metion_list
-          echo "METIONS=$metion_list" >> "$GITHUB_ENV"
-          echo ${{ env.METIONS }}
-
     - name: notify
       run: |
-        echo ${{ secrets.USER_ID }}
-        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'${{ secrets.USER_ID }}'"}]]}}}}'  ${{ secrets.WEBHOOK_URL }}
+        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'$USER_ID'"}]]}}}}'  $WEBHOOK_URL

.github/workflows/huigui.yml

@@ -29,14 +29,14 @@ jobs:
         source activate mineru
         conda env list
         pip show coverage
-        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
         cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
         cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
         cd $GITHUB_WORKSPACE && python tests/get_coverage.py
         cd $GITHUB_WORKSPACE && pytest -s -v tests/test_cli/test_cli_sdk.py
 
   notify_to_feishu:
-    if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure') && (github.ref_name == 'master') }}
+    if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure')}}
     needs: cli-test
     runs-on: pdf
     steps:
@@ -57,5 +57,5 @@ jobs:
 
     - name: notify
       run: |
-        echo ${{ secrets.USER_ID }}
-        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'${{ secrets.USER_ID }}'"}]]}}}}'  ${{ secrets.WEBHOOK_URL }}
+        #echo ${{ secrets.USER_ID }}
+        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'$USER_ID'"}]]}}}}'  $WEBHOOK_URL

docs/README_Windows_CUDA_Acceleration_en_US.md

@@ -67,14 +67,6 @@ If your graphics card has at least 8GB of VRAM, follow these steps to test CUDA-
    ```
    pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
    ```
-   > [!IMPORTANT]
-   > Ensure the following versions are specified in the command:
-   >
-   > ```
-   > torch==2.3.1 torchvision==0.18.1
-   > ```
-   >
-   > These are the highest versions we support. Installing higher versions without specifying them will cause the program to fail.
 
 2. **Modify the value of `"device-mode"`** in the `magic-pdf.json` configuration file located in your user directory.
 

docs/README_Windows_CUDA_Acceleration_zh_CN.md

@@ -69,15 +69,6 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
 pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
 ```
 
-> [!IMPORTANT]
-> 务必在命令中指定以下版本
->
-> ```bash
-> torch==2.3.1 torchvision==0.18.1
-> ```
->
-> 这是我们支持的最高版本，如果不指定版本会自动安装更高版本导致程序无法运行
-
 **2.修改【用户目录】中配置文件magic-pdf.json中"device-mode"的值**
 
 ```json

magic_pdf/config/constants.py

@@ -51,3 +51,8 @@ class MODEL_NAME:
     UniMerNet_v2_Small = 'unimernet_small'
 
     RAPID_TABLE = 'rapid_table'
+
+
+PARSE_TYPE_TXT = 'txt'
+PARSE_TYPE_OCR = 'ocr'
+

magic_pdf/data/data_reader_writer/base.py

@@ -48,4 +48,16 @@ class DataWriter(ABC):
             path (str): the target file where to write
             data (str): the data want to write
         """
-        self.write(path, data.encode())
+
+        def safe_encode(data: str, method: str):
+            try:
+                bit_data = data.encode(encoding=method, errors='replace')
+                return bit_data, True
+            except:  # noqa
+                return None, False
+
+        for method in ['utf-8', 'ascii']:
+            bit_data, flag = safe_encode(data, method)
+            if flag:
+                self.write(path, bit_data)
+                break

magic_pdf/data/dataset.py

+import os
 from abc import ABC, abstractmethod
-from typing import Iterator
+from typing import Callable, Iterator
 
 import fitz
 
 from magic_pdf.config.enums import SupportedPdfParseMethod
 from magic_pdf.data.schemas import PageInfo
 from magic_pdf.data.utils import fitz_doc_to_image
+from magic_pdf.filter import classify
 
 
 class PageableData(ABC):
@@ -28,6 +30,32 @@ class PageableData(ABC):
         """
         pass
 
+    @abstractmethod
+    def draw_rect(self, rect_coords, color, fill, fill_opacity, width, overlay):
+        """draw rectangle.
+
+        Args:
+            rect_coords (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            color (list[float] | None): three element tuple which describe the RGB of the board line, None means no board line
+            fill (list[float] | None): fill the board with RGB, None means will not fill with color
+            fill_opacity (float): opacity of the fill, range from [0, 1]
+            width (float): the width of board
+            overlay (bool): fill the color in foreground or background. True means fill in background.
+        """
+        pass
+
+    @abstractmethod
+    def insert_text(self, coord, content, fontsize, color):
+        """insert text.
+
+        Args:
+            coord (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            content (str): the text content
+            fontsize (int): font size of the text
+            color (list[float] | None):  three element tuple which describe the RGB of the board line, None will use the default font color!
+        """
+        pass
+
 
 class Dataset(ABC):
     @abstractmethod
@@ -66,6 +94,43 @@ class Dataset(ABC):
         """
         pass
 
+    @abstractmethod
+    def dump_to_file(self, file_path: str):
+        """Dump the file
+
+        Args: 
+            file_path (str): the file path 
+        """
+        pass
+
+    @abstractmethod
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(self, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        pass
+
+    @abstractmethod
+    def classify(self) -> SupportedPdfParseMethod:
+        """classify the dataset 
+
+        Returns:
+            SupportedPdfParseMethod: _description_
+        """
+        pass
+
+    @abstractmethod
+    def clone(self):
+        """clone this dataset
+        """
+        pass
+
 
 class PymuDocDataset(Dataset):
     def __init__(self, bits: bytes):
@@ -74,7 +139,8 @@ class PymuDocDataset(Dataset):
         Args:
             bits (bytes): the bytes of the pdf
         """
-        self._records = [Doc(v) for v in fitz.open('pdf', bits)]
+        self._raw_fitz = fitz.open('pdf', bits)
+        self._records = [Doc(v) for v in self._raw_fitz]
         self._data_bits = bits
         self._raw_data = bits
 
@@ -109,6 +175,43 @@ class PymuDocDataset(Dataset):
         """
         return self._records[page_id]
 
+    def dump_to_file(self, file_path: str):
+        """Dump the file
+
+        Args: 
+            file_path (str): the file path 
+        """
+        
+        dir_name = os.path.dirname(file_path)
+        if dir_name not in ('', '.', '..'):
+            os.makedirs(dir_name, exist_ok=True)
+        self._raw_fitz.save(file_path)
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(dataset, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(self, *args, **kwargs)
+
+    def classify(self) -> SupportedPdfParseMethod:
+        """classify the dataset 
+
+        Returns:
+            SupportedPdfParseMethod: _description_
+        """
+        return classify(self._data_bits)
+
+    def clone(self):
+        """clone this dataset
+        """
+        return PymuDocDataset(self._raw_data)
+
 
 class ImageDataset(Dataset):
     def __init__(self, bits: bytes):
@@ -118,7 +221,8 @@ class ImageDataset(Dataset):
             bits (bytes): the bytes of the photo which will be converted to pdf first. then converted to pymudoc.
         """
         pdf_bytes = fitz.open(stream=bits).convert_to_pdf()
-        self._records = [Doc(v) for v in fitz.open('pdf', pdf_bytes)]
+        self._raw_fitz = fitz.open('pdf', pdf_bytes)
+        self._records = [Doc(v) for v in self._raw_fitz]
         self._raw_data = bits
         self._data_bits = pdf_bytes
 
@@ -153,14 +257,50 @@ class ImageDataset(Dataset):
         """
         return self._records[page_id]
 
+    def dump_to_file(self, file_path: str):
+        """Dump the file
+
+        Args: 
+            file_path (str): the file path 
+        """
+        dir_name = os.path.dirname(file_path)
+        if dir_name not in ('', '.', '..'):
+            os.makedirs(dir_name, exist_ok=True)
+        self._raw_fitz.save(file_path)
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(dataset, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(self, *args, **kwargs)
+
+    def classify(self) -> SupportedPdfParseMethod:
+        """classify the dataset 
+
+        Returns:
+            SupportedPdfParseMethod: _description_
+        """
+        return SupportedPdfParseMethod.OCR
+
+    def clone(self):
+        """clone this dataset
+        """
+        return ImageDataset(self._raw_data)
 
 class Doc(PageableData):
     """Initialized with pymudoc object."""
+
     def __init__(self, doc: fitz.Page):
         self._doc = doc
 
     def get_image(self):
-        """Return the imge info.
+        """Return the image info.
 
         Returns:
             dict: {
@@ -192,3 +332,34 @@ class Doc(PageableData):
     def __getattr__(self, name):
         if hasattr(self._doc, name):
             return getattr(self._doc, name)
+
+    def draw_rect(self, rect_coords, color, fill, fill_opacity, width, overlay):
+        """draw rectangle.
+
+        Args:
+            rect_coords (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            color (list[float] | None): three element tuple which describe the RGB of the board line, None means no board line
+            fill (list[float] | None): fill the board with RGB, None means will not fill with color
+            fill_opacity (float): opacity of the fill, range from [0, 1]
+            width (float): the width of board
+            overlay (bool): fill the color in foreground or background. True means fill in background.
+        """
+        self._doc.draw_rect(
+            rect_coords,
+            color=color,
+            fill=fill,
+            fill_opacity=fill_opacity,
+            width=width,
+            overlay=overlay,
+        )
+
+    def insert_text(self, coord, content, fontsize, color):
+        """insert text.
+
+        Args:
+            coord (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            content (str): the text content
+            fontsize (int): font size of the text
+            color (list[float] | None):  three element tuple which describe the RGB of the board line, None will use the default font color!
+        """
+        self._doc.insert_text(coord, content, fontsize=fontsize, color=color)

magic_pdf/dict2md/ocr_mkcontent.py

@@ -165,8 +165,8 @@ def merge_para_with_text(para_block):
             if content:
                 langs = ['zh', 'ja', 'ko']
                 # logger.info(f'block_lang: {block_lang}, content: {content}')
-                if block_lang in langs: # 中文/日语/韩文语境下，换行不需要空格分隔
-                    if j == len(line['spans']) - 1:
+                if block_lang in langs: # 中文/日语/韩文语境下，换行不需要空格分隔,但是如果是行内公式结尾，还是要加空格
+                    if j == len(line['spans']) - 1 and span_type not in [ContentType.InlineEquation]:
                         para_text += content
                     else:
                         para_text += f'{content} '

magic_pdf/filter/__init__.py

+
+from magic_pdf.config.drop_reason import DropReason
+from magic_pdf.config.enums import SupportedPdfParseMethod
+from magic_pdf.filter.pdf_classify_by_type import classify as do_classify
+from magic_pdf.filter.pdf_meta_scan import pdf_meta_scan
+
+
+def classify(pdf_bytes: bytes) -> SupportedPdfParseMethod:
+    """根据pdf的元数据，判断是文本pdf，还是ocr pdf."""
+    pdf_meta = pdf_meta_scan(pdf_bytes)
+    if pdf_meta.get('_need_drop', False):  # 如果返回了需要丢弃的标志，则抛出异常
+        raise Exception(f"pdf meta_scan need_drop,reason is {pdf_meta['_drop_reason']}")
+    else:
+        is_encrypted = pdf_meta['is_encrypted']
+        is_needs_password = pdf_meta['is_needs_password']
+        if is_encrypted or is_needs_password:  # 加密的，需要密码的，没有页面的，都不处理
+            raise Exception(f'pdf meta_scan need_drop,reason is {DropReason.ENCRYPTED}')
+        else:
+            is_text_pdf, results = do_classify(
+                pdf_meta['total_page'],
+                pdf_meta['page_width_pts'],
+                pdf_meta['page_height_pts'],
+                pdf_meta['image_info_per_page'],
+                pdf_meta['text_len_per_page'],
+                pdf_meta['imgs_per_page'],
+                pdf_meta['text_layout_per_page'],
+                pdf_meta['invalid_chars'],
+            )
+            if is_text_pdf:
+                return SupportedPdfParseMethod.TXT
+            else:
+                return SupportedPdfParseMethod.OCR

magic_pdf/filter/pdf_meta_scan.py

@@ -8,7 +8,7 @@ from loguru import logger
 from magic_pdf.config.drop_reason import DropReason
 from magic_pdf.libs.commons import get_top_percent_list, mymax
 from magic_pdf.libs.language import detect_lang
-from magic_pdf.libs.pdf_check import detect_invalid_chars_by_pymupdf
+from magic_pdf.libs.pdf_check import detect_invalid_chars_by_pymupdf, detect_invalid_chars
 
 scan_max_page = 50
 junk_limit_min = 10
@@ -323,7 +323,8 @@ def get_language(doc: fitz.Document):
 
 def check_invalid_chars(pdf_bytes):
     """乱码检测."""
-    return detect_invalid_chars_by_pymupdf(pdf_bytes)
+    # return detect_invalid_chars_by_pymupdf(pdf_bytes)
+    return detect_invalid_chars(pdf_bytes)
 
 
 def pdf_meta_scan(pdf_bytes: bytes):

magic_pdf/libs/draw_bbox.py

 import fitz
 from magic_pdf.config.constants import CROSS_PAGE
-from magic_pdf.config.ocr_content_type import BlockType, CategoryId, ContentType
-from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.config.ocr_content_type import (BlockType, CategoryId,
+                                               ContentType)
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.model.magic_model import MagicModel
 
 
@@ -194,7 +195,7 @@ def draw_layout_bbox(pdf_info, pdf_bytes, out_path, filename):
         )
 
     # Save the PDF
-    pdf_docs.save(f'{out_path}/{filename}_layout.pdf')
+    pdf_docs.save(f'{out_path}/{filename}')
 
 
 def draw_span_bbox(pdf_info, pdf_bytes, out_path, filename):
@@ -282,18 +283,17 @@ def draw_span_bbox(pdf_info, pdf_bytes, out_path, filename):
         draw_bbox_without_number(i, dropped_list, page, [158, 158, 158], False)
 
     # Save the PDF
-    pdf_docs.save(f'{out_path}/{filename}_spans.pdf')
+    pdf_docs.save(f'{out_path}/{filename}')
 
 
-def draw_model_bbox(model_list: list, pdf_bytes, out_path, filename):
+def draw_model_bbox(model_list, dataset: Dataset, out_path, filename):
     dropped_bbox_list = []
     tables_body_list, tables_caption_list, tables_footnote_list = [], [], []
     imgs_body_list, imgs_caption_list, imgs_footnote_list = [], [], []
     titles_list = []
     texts_list = []
     interequations_list = []
-    pdf_docs = fitz.open('pdf', pdf_bytes)
-    magic_model = MagicModel(model_list, PymuDocDataset(pdf_bytes))
+    magic_model = MagicModel(model_list, dataset)
     for i in range(len(model_list)):
         page_dropped_list = []
         tables_body, tables_caption, tables_footnote = [], [], []
@@ -337,7 +337,8 @@ def draw_model_bbox(model_list: list, pdf_bytes, out_path, filename):
         dropped_bbox_list.append(page_dropped_list)
         imgs_footnote_list.append(imgs_footnote)
 
-    for i, page in enumerate(pdf_docs):
+    for i in range(len(dataset)):
+        page = dataset.get_page(i)
         draw_bbox_with_number(
             i, dropped_bbox_list, page, [158, 158, 158], True
         )  # color !
@@ -352,7 +353,7 @@ def draw_model_bbox(model_list: list, pdf_bytes, out_path, filename):
         draw_bbox_with_number(i, interequations_list, page, [0, 255, 0], True)
 
     # Save the PDF
-    pdf_docs.save(f'{out_path}/{filename}_model.pdf')
+    dataset.dump_to_file(f'{out_path}/{filename}')
 
 
 def draw_line_sort_bbox(pdf_info, pdf_bytes, out_path, filename):
@@ -390,7 +391,7 @@ def draw_line_sort_bbox(pdf_info, pdf_bytes, out_path, filename):
     for i, page in enumerate(pdf_docs):
         draw_bbox_with_number(i, layout_bbox_list, page, [255, 0, 0], False)
 
-    pdf_docs.save(f'{out_path}/{filename}_line_sort.pdf')
+    pdf_docs.save(f'{out_path}/{filename}')
 
 
 def draw_layout_sort_bbox(pdf_info, pdf_bytes, out_path, filename):

magic_pdf/libs/pdf_check.py

 import fitz
 import numpy as np
 from loguru import logger
-# import re
-# from io import BytesIO
-# from pdfminer.high_level import extract_text
+import re
+from io import BytesIO
+from pdfminer.high_level import extract_text
 
 
 def calculate_sample_count(total_page: int):
@@ -33,33 +33,33 @@ def extract_pages(src_pdf_bytes: bytes) -> fitz.Document:
     return sample_docs
 
 
-# def detect_invalid_chars(src_pdf_bytes: bytes) -> bool:
-#     """"
-#     检测PDF中是否包含非法字符
-#     """
-#     '''pdfminer比较慢,需要先随机抽取10页左右的sample'''
-#     sample_docs = extract_pages(src_pdf_bytes)
-#     sample_pdf_bytes = sample_docs.tobytes()
-#     sample_pdf_file_like_object = BytesIO(sample_pdf_bytes)
-#     text = extract_text(sample_pdf_file_like_object)
-#     text = text.replace("\n", "")
-#     # logger.info(text)
-#     '''乱码文本用pdfminer提取出来的文本特征是(cid:xxx)'''
-#     cid_pattern = re.compile(r'\(cid:\d+\)')
-#     matches = cid_pattern.findall(text)
-#     cid_count = len(matches)
-#     cid_len = sum(len(match) for match in matches)
-#     text_len = len(text)
-#     if text_len == 0:
-#         cid_chars_radio = 0
-#     else:
-#         cid_chars_radio = cid_count/(cid_count + text_len - cid_len)
-#     logger.info(f"cid_count: {cid_count}, text_len: {text_len}, cid_chars_radio: {cid_chars_radio}")
-#     '''当一篇文章存在5%以上的文本是乱码时,认为该文档为乱码文档'''
-#     if cid_chars_radio > 0.05:
-#         return False  # 乱码文档
-#     else:
-#         return True   # 正常文档
+def detect_invalid_chars(src_pdf_bytes: bytes) -> bool:
+    """"
+    检测PDF中是否包含非法字符
+    """
+    '''pdfminer比较慢,需要先随机抽取10页左右的sample'''
+    sample_docs = extract_pages(src_pdf_bytes)
+    sample_pdf_bytes = sample_docs.tobytes()
+    sample_pdf_file_like_object = BytesIO(sample_pdf_bytes)
+    text = extract_text(sample_pdf_file_like_object)
+    text = text.replace("\n", "")
+    # logger.info(text)
+    '''乱码文本用pdfminer提取出来的文本特征是(cid:xxx)'''
+    cid_pattern = re.compile(r'\(cid:\d+\)')
+    matches = cid_pattern.findall(text)
+    cid_count = len(matches)
+    cid_len = sum(len(match) for match in matches)
+    text_len = len(text)
+    if text_len == 0:
+        cid_chars_radio = 0
+    else:
+        cid_chars_radio = cid_count/(cid_count + text_len - cid_len)
+    logger.info(f"cid_count: {cid_count}, text_len: {text_len}, cid_chars_radio: {cid_chars_radio}")
+    '''当一篇文章存在5%以上的文本是乱码时,认为该文档为乱码文档'''
+    if cid_chars_radio > 0.05:
+        return False  # 乱码文档
+    else:
+        return True   # 正常文档
 
 
 def count_replacement_characters(text: str) -> int:

magic_pdf/model/__init__.py

+from typing import Callable
+
+from abc import ABC, abstractmethod
+
+from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
+from magic_pdf.pipe.operators import PipeResult
+
+
 __use_inside_model__ = True
 __model_mode__ = "full"
+
+
+class InferenceResultBase(ABC):
+
+    @abstractmethod
+    def __init__(self, inference_results: list, dataset: Dataset):
+        """Initialized method.
+
+        Args:
+            inference_results (list): the inference result generated by model
+            dataset (Dataset): the dataset related with model inference result
+        """
+        self._infer_res = inference_results
+        self._dataset = dataset
+
+    @abstractmethod
+    def draw_model(self, file_path: str) -> None:
+        """Draw model inference result.
+
+        Args:
+            file_path (str): the output file path
+        """
+        pass
+
+    @abstractmethod
+    def dump_model(self, writer: DataWriter, file_path: str):
+        """Dump model inference result to file.
+
+        Args:
+            writer (DataWriter): writer handle
+            file_path (str): the location of target file
+        """
+        pass
+
+    @abstractmethod
+    def get_infer_res(self):
+        """Get the inference result.
+
+        Returns:
+            list: the inference result generated by model
+        """
+        pass
+
+    @abstractmethod
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(inference_result, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        pass
+
+    @abstractmethod
+    def pipe_auto_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result.
+            step1: classify the dataset type
+            step2: based the result of step1, using `pipe_txt_mode` or `pipe_ocr_mode`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+        pass
+
+    @abstractmethod
+    def pipe_txt_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result, Extract the text using the
+        third library, such as `pymupdf`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+        pass
+
+    @abstractmethod
+    def pipe_ocr_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        pass

magic_pdf/model/doc_analyze_by_custom_model.py

+import os
 import time
 
 import fitz
 import numpy as np
 from loguru import logger
 
+# 关闭paddle的信号处理
+import paddle
+paddle.disable_signal_handler()
+
+os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
+os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
+
+try:
+    import torchtext
+
+    if torchtext.__version__ >= '0.18.0':
+        torchtext.disable_torchtext_deprecation_warning()
+except ImportError:
+    pass
+
+import magic_pdf.model as model_config
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.libs.clean_memory import clean_memory
-from magic_pdf.libs.config_reader import get_local_models_dir, get_device, get_table_recog_config, get_layout_config, \
-    get_formula_config
+from magic_pdf.libs.config_reader import (get_device, get_formula_config,
+                                          get_layout_config,
+                                          get_local_models_dir,
+                                          get_table_recog_config)
 from magic_pdf.model.model_list import MODEL
-import magic_pdf.model as model_config
+from magic_pdf.model.operators import InferenceResult
 
 
 def dict_compare(d1, d2):
@@ -25,19 +45,25 @@ def remove_duplicates_dicts(lst):
     return unique_dicts
 
 
-def load_images_from_pdf(pdf_bytes: bytes, dpi=200, start_page_id=0, end_page_id=None) -> list:
+def load_images_from_pdf(
+    pdf_bytes: bytes, dpi=200, start_page_id=0, end_page_id=None
+) -> list:
     try:
         from PIL import Image
     except ImportError:
-        logger.error("Pillow not installed, please install by pip.")
+        logger.error('Pillow not installed, please install by pip.')
         exit(1)
 
     images = []
-    with fitz.open("pdf", pdf_bytes) as doc:
+    with fitz.open('pdf', pdf_bytes) as doc:
         pdf_page_num = doc.page_count
-        end_page_id = end_page_id if end_page_id is not None and end_page_id >= 0 else pdf_page_num - 1
+        end_page_id = (
+            end_page_id
+            if end_page_id is not None and end_page_id >= 0
+            else pdf_page_num - 1
+        )
         if end_page_id > pdf_page_num - 1:
-            logger.warning("end_page_id is out of range, use images length")
+            logger.warning('end_page_id is out of range, use images length')
             end_page_id = pdf_page_num - 1
 
         for index in range(0, doc.page_count):
@@ -50,11 +76,11 @@ def load_images_from_pdf(pdf_bytes: bytes, dpi=200, start_page_id=0, end_page_id
                 if pm.width > 4500 or pm.height > 4500:
                     pm = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
 
-                img = Image.frombytes("RGB", (pm.width, pm.height), pm.samples)
+                img = Image.frombytes('RGB', (pm.width, pm.height), pm.samples)
                 img = np.array(img)
-                img_dict = {"img": img, "width": pm.width, "height": pm.height}
+                img_dict = {'img': img, 'width': pm.width, 'height': pm.height}
             else:
-                img_dict = {"img": [], "width": 0, "height": 0}
+                img_dict = {'img': [], 'width': 0, 'height': 0}
 
             images.append(img_dict)
     return images
@@ -69,117 +95,150 @@ class ModelSingleton:
             cls._instance = super().__new__(cls)
         return cls._instance
 
-    def get_model(self, ocr: bool, show_log: bool, lang=None, layout_model=None, formula_enable=None, table_enable=None):
+    def get_model(
+        self,
+        ocr: bool,
+        show_log: bool,
+        lang=None,
+        layout_model=None,
+        formula_enable=None,
+        table_enable=None,
+    ):
         key = (ocr, show_log, lang, layout_model, formula_enable, table_enable)
         if key not in self._models:
-            self._models[key] = custom_model_init(ocr=ocr, show_log=show_log, lang=lang, layout_model=layout_model,
-                                                  formula_enable=formula_enable, table_enable=table_enable)
+            self._models[key] = custom_model_init(
+                ocr=ocr,
+                show_log=show_log,
+                lang=lang,
+                layout_model=layout_model,
+                formula_enable=formula_enable,
+                table_enable=table_enable,
+            )
         return self._models[key]
 
 
-def custom_model_init(ocr: bool = False, show_log: bool = False, lang=None,
-                      layout_model=None, formula_enable=None, table_enable=None):
+def custom_model_init(
+    ocr: bool = False,
+    show_log: bool = False,
+    lang=None,
+    layout_model=None,
+    formula_enable=None,
+    table_enable=None,
+):
 
     model = None
 
-    if model_config.__model_mode__ == "lite":
-        logger.warning("The Lite mode is provided for developers to conduct testing only, and the output quality is "
-                       "not guaranteed to be reliable.")
+    if model_config.__model_mode__ == 'lite':
+        logger.warning(
+            'The Lite mode is provided for developers to conduct testing only, and the output quality is '
+            'not guaranteed to be reliable.'
+        )
         model = MODEL.Paddle
-    elif model_config.__model_mode__ == "full":
+    elif model_config.__model_mode__ == 'full':
         model = MODEL.PEK
 
     if model_config.__use_inside_model__:
         model_init_start = time.time()
         if model == MODEL.Paddle:
             from magic_pdf.model.pp_structure_v2 import CustomPaddleModel
+
             custom_model = CustomPaddleModel(ocr=ocr, show_log=show_log, lang=lang)
         elif model == MODEL.PEK:
             from magic_pdf.model.pdf_extract_kit import CustomPEKModel
+
             # 从配置文件读取model-dir和device
             local_models_dir = get_local_models_dir()
             device = get_device()
 
             layout_config = get_layout_config()
             if layout_model is not None:
-                layout_config["model"] = layout_model
+                layout_config['model'] = layout_model
 
             formula_config = get_formula_config()
             if formula_enable is not None:
-                formula_config["enable"] = formula_enable
+                formula_config['enable'] = formula_enable
 
             table_config = get_table_recog_config()
             if table_enable is not None:
-                table_config["enable"] = table_enable
+                table_config['enable'] = table_enable
 
             model_input = {
-                            "ocr": ocr,
-                            "show_log": show_log,
-                            "models_dir": local_models_dir,
-                            "device": device,
-                            "table_config": table_config,
-                            "layout_config": layout_config,
-                            "formula_config": formula_config,
-                            "lang": lang,
+                'ocr': ocr,
+                'show_log': show_log,
+                'models_dir': local_models_dir,
+                'device': device,
+                'table_config': table_config,
+                'layout_config': layout_config,
+                'formula_config': formula_config,
+                'lang': lang,
             }
 
             custom_model = CustomPEKModel(**model_input)
         else:
-            logger.error("Not allow model_name!")
+            logger.error('Not allow model_name!')
             exit(1)
         model_init_cost = time.time() - model_init_start
-        logger.info(f"model init cost: {model_init_cost}")
+        logger.info(f'model init cost: {model_init_cost}')
     else:
-        logger.error("use_inside_model is False, not allow to use inside model")
+        logger.error('use_inside_model is False, not allow to use inside model')
         exit(1)
 
     return custom_model
 
 
-def doc_analyze(pdf_bytes: bytes, ocr: bool = False, show_log: bool = False,
-                start_page_id=0, end_page_id=None, lang=None,
-                layout_model=None, formula_enable=None, table_enable=None):
+def doc_analyze(
+    dataset: Dataset,
+    ocr: bool = False,
+    show_log: bool = False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    layout_model=None,
+    formula_enable=None,
+    table_enable=None,
+) -> InferenceResult:
 
-    if lang == "":
+    if lang == '':
         lang = None
 
     model_manager = ModelSingleton()
-    custom_model = model_manager.get_model(ocr, show_log, lang, layout_model, formula_enable, table_enable)
-
-    with fitz.open("pdf", pdf_bytes) as doc:
-        pdf_page_num = doc.page_count
-        end_page_id = end_page_id if end_page_id is not None and end_page_id >= 0 else pdf_page_num - 1
-        if end_page_id > pdf_page_num - 1:
-            logger.warning("end_page_id is out of range, use images length")
-            end_page_id = pdf_page_num - 1
-
-    images = load_images_from_pdf(pdf_bytes, start_page_id=start_page_id, end_page_id=end_page_id)
+    custom_model = model_manager.get_model(
+        ocr, show_log, lang, layout_model, formula_enable, table_enable
+    )
 
     model_json = []
     doc_analyze_start = time.time()
 
-    for index, img_dict in enumerate(images):
-        img = img_dict["img"]
-        page_width = img_dict["width"]
-        page_height = img_dict["height"]
+    if end_page_id is None:
+        end_page_id = len(dataset)
+
+    for index in range(len(dataset)):
+        page_data = dataset.get_page(index)
+        img_dict = page_data.get_image()
+        img = img_dict['img']
+        page_width = img_dict['width']
+        page_height = img_dict['height']
         if start_page_id <= index <= end_page_id:
             page_start = time.time()
             result = custom_model(img)
             logger.info(f'-----page_id : {index}, page total time: {round(time.time() - page_start, 2)}-----')
         else:
             result = []
-        page_info = {"page_no": index, "height": page_height, "width": page_width}
-        page_dict = {"layout_dets": result, "page_info": page_info}
+
+        page_info = {'page_no': index, 'height': page_height, 'width': page_width}
+        page_dict = {'layout_dets': result, 'page_info': page_info}
         model_json.append(page_dict)
 
     gc_start = time.time()
     clean_memory()
     gc_time = round(time.time() - gc_start, 2)
-    logger.info(f"gc time: {gc_time}")
+    logger.info(f'gc time: {gc_time}')
 
     doc_analyze_time = round(time.time() - doc_analyze_start, 2)
-    doc_analyze_speed = round( (end_page_id + 1 - start_page_id) / doc_analyze_time, 2)
-    logger.info(f"doc analyze time: {round(time.time() - doc_analyze_start, 2)},"
-                f" speed: {doc_analyze_speed} pages/second")
+    doc_analyze_speed = round((end_page_id + 1 - start_page_id) / doc_analyze_time, 2)
+    logger.info(
+        f'doc analyze time: {round(time.time() - doc_analyze_start, 2)},'
+        f' speed: {doc_analyze_speed} pages/second'
+    )
 
-    return model_json
+    return InferenceResult(model_json, dataset)

magic_pdf/model/operators.py

+import copy
+import json
+import os
+from typing import Callable
+
+from magic_pdf.config.constants import PARSE_TYPE_OCR, PARSE_TYPE_TXT
+from magic_pdf.config.enums import SupportedPdfParseMethod
+from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
+from magic_pdf.filter import classify
+from magic_pdf.libs.draw_bbox import draw_model_bbox
+from magic_pdf.libs.version import __version__
+from magic_pdf.model import InferenceResultBase
+from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
+from magic_pdf.pipe.operators import PipeResult
+
+
+class InferenceResult(InferenceResultBase):
+    def __init__(self, inference_results: list, dataset: Dataset):
+        """Initialized method.
+
+        Args:
+            inference_results (list): the inference result generated by model
+            dataset (Dataset): the dataset related with model inference result
+        """
+        self._infer_res = inference_results
+        self._dataset = dataset
+
+    def draw_model(self, file_path: str) -> None:
+        """Draw model inference result.
+
+        Args:
+            file_path (str): the output file path
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        draw_model_bbox(
+            copy.deepcopy(self._infer_res), self._dataset, dir_name, base_name
+        )
+
+    def dump_model(self, writer: DataWriter, file_path: str):
+        """Dump model inference result to file.
+
+        Args:
+            writer (DataWriter): writer handle
+            file_path (str): the location of target file
+        """
+        writer.write_string(
+            file_path, json.dumps(self._infer_res, ensure_ascii=False, indent=4)
+        )
+
+    def get_infer_res(self):
+        """Get the inference result.
+
+        Returns:
+            list: the inference result generated by model
+        """
+        return self._infer_res
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(inference_result, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(copy.deepcopy(self._infer_res), *args, **kwargs)
+
+    def pipe_auto_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result.
+            step1: classify the dataset type
+            step2: based the result of step1, using `pipe_txt_mode` or `pipe_ocr_mode`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+
+        pdf_proc_method = classify(self._dataset.data_bits())
+
+        if pdf_proc_method == SupportedPdfParseMethod.TXT:
+            return self.pipe_txt_mode(
+                imageWriter, start_page_id, end_page_id, debug_mode, lang
+            )
+        else:
+            return self.pipe_ocr_mode(
+                imageWriter, start_page_id, end_page_id, debug_mode, lang
+            )
+
+    def pipe_txt_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result, Extract the text using the
+        third library, such as `pymupdf`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+
+        def proc(*args, **kwargs) -> PipeResult:
+            res = pdf_parse_union(*args, **kwargs)
+            res['_parse_type'] = PARSE_TYPE_TXT
+            res['_version_name'] = __version__
+            if 'lang' in kwargs and kwargs['lang'] is not None:
+                res['lang'] = kwargs['lang']
+            return PipeResult(res, self._dataset)
+
+        res = self.apply(
+            proc,
+            self._dataset,
+            imageWriter,
+            SupportedPdfParseMethod.TXT,
+            start_page_id=start_page_id,
+            end_page_id=end_page_id,
+            debug_mode=debug_mode,
+            lang=lang,
+        )
+        return res
+
+    def pipe_ocr_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result, Extract the text using `OCR`
+        technical.
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+
+        def proc(*args, **kwargs) -> PipeResult:
+            res = pdf_parse_union(*args, **kwargs)
+            res['_parse_type'] = PARSE_TYPE_OCR
+            res['_version_name'] = __version__
+            if 'lang' in kwargs and kwargs['lang'] is not None:
+                res['lang'] = kwargs['lang']
+            return PipeResult(res, self._dataset)
+
+        res = self.apply(
+            proc,
+            self._dataset,
+            imageWriter,
+            SupportedPdfParseMethod.OCR,
+            start_page_id=start_page_id,
+            end_page_id=end_page_id,
+            debug_mode=debug_mode,
+            lang=lang,
+        )
+        return res

magic_pdf/model/pdf_extract_kit.py

@@ -179,7 +179,25 @@ class CustomPEKModel:
             layout_res = self.layout_model(image, ignore_catids=[])
         elif self.layout_model_name == MODEL_NAME.DocLayout_YOLO:
             # doclayout_yolo
-            layout_res = self.layout_model.predict(image)
+            img_pil = Image.fromarray(image)
+            width, height = img_pil.size
+            # logger.info(f'width: {width}, height: {height}')
+            input_res = {"poly":[0,0,width,0,width,height,0,height]}
+            new_image, useful_list = crop_img(input_res, img_pil, crop_paste_x=width//2, crop_paste_y=0)
+            paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
+            layout_res = self.layout_model.predict(new_image)
+            for res in layout_res:
+                p1, p2, p3, p4, p5, p6, p7, p8 = res['poly']
+                p1 = p1 - paste_x + xmin
+                p2 = p2 - paste_y + ymin
+                p3 = p3 - paste_x + xmin
+                p4 = p4 - paste_y + ymin
+                p5 = p5 - paste_x + xmin
+                p6 = p6 - paste_y + ymin
+                p7 = p7 - paste_x + xmin
+                p8 = p8 - paste_y + ymin
+                res['poly'] = [p1, p2, p3, p4, p5, p6, p7, p8]
+
         layout_cost = round(time.time() - layout_start, 2)
         logger.info(f'layout detection time: {layout_cost}')
 
@@ -215,6 +233,7 @@ class CustomPEKModel:
 
             # OCR recognition
             new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
+
             if self.apply_ocr:
                 ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res)[0]
             else:

magic_pdf/model/sub_modules/model_init.py

@@ -92,14 +92,24 @@ class AtomModelSingleton:
         return cls._instance
 
     def get_atom_model(self, atom_model_name: str, **kwargs):
+
         lang = kwargs.get('lang', None)
         layout_model_name = kwargs.get('layout_model_name', None)
-        key = (atom_model_name, layout_model_name, lang)
+        table_model_name = kwargs.get('table_model_name', None)
+
+        if atom_model_name in [AtomicModel.OCR]:
+            key = (atom_model_name, lang)
+        elif atom_model_name in [AtomicModel.Layout]:
+            key = (atom_model_name, layout_model_name)
+        elif atom_model_name in [AtomicModel.Table]:
+            key = (atom_model_name, table_model_name)
+        else:
+            key = atom_model_name
+
         if key not in self._models:
             self._models[key] = atom_model_init(model_name=atom_model_name, **kwargs)
         return self._models[key]
 
-
 def atom_model_init(model_name: str, **kwargs):
     atom_model = None
     if model_name == AtomicModel.Layout:
@@ -129,7 +139,7 @@ def atom_model_init(model_name: str, **kwargs):
         atom_model = ocr_model_init(
             kwargs.get('ocr_show_log'),
             kwargs.get('det_db_box_thresh'),
-            kwargs.get('lang')
+            kwargs.get('lang'),
         )
     elif model_name == AtomicModel.Table:
         atom_model = table_model_init(

magic_pdf/model/sub_modules/model_utils.py

@@ -42,10 +42,16 @@ def get_res_list_from_layout_res(layout_res):
 
 
 def clean_vram(device, vram_threshold=8):
-    if torch.cuda.is_available() and device != 'cpu':
-        total_memory = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3)  # 将字节转换为 GB
-        if total_memory <= vram_threshold:
+    total_memory = get_vram(device)
+    if total_memory and total_memory <= vram_threshold:
         gc_start = time.time()
         clean_memory()
         gc_time = round(time.time() - gc_start, 2)
         logger.info(f"gc time: {gc_time}")
+
+
+def get_vram(device):
+    if torch.cuda.is_available() and device != 'cpu':
+        total_memory = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3)  # 将字节转换为 GB
+        return total_memory
+    return None
\ No newline at end of file

magic_pdf/pdf_parse_by_ocr.py

 from magic_pdf.config.enums import SupportedPdfParseMethod
-from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
 
 
-def parse_pdf_by_ocr(pdf_bytes,
+def parse_pdf_by_ocr(dataset: Dataset,
                      model_list,
                      imageWriter,
                      start_page_id=0,
@@ -11,9 +11,8 @@ def parse_pdf_by_ocr(pdf_bytes,
                      debug_mode=False,
                      lang=None,
                      ):
-    dataset = PymuDocDataset(pdf_bytes)
-    return pdf_parse_union(dataset,
-                           model_list,
+    return pdf_parse_union(model_list,
+                           dataset,
                            imageWriter,
                            SupportedPdfParseMethod.OCR,
                            start_page_id=start_page_id,

magic_pdf/pdf_parse_by_txt.py

 from magic_pdf.config.enums import SupportedPdfParseMethod
-from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
 
 
 def parse_pdf_by_txt(
-    pdf_bytes,
+    dataset: Dataset,
     model_list,
     imageWriter,
     start_page_id=0,
@@ -12,9 +12,8 @@ def parse_pdf_by_txt(
     debug_mode=False,
     lang=None,
 ):
-    dataset = PymuDocDataset(pdf_bytes)
-    return pdf_parse_union(dataset,
-                           model_list,
+    return pdf_parse_union(model_list,
+                           dataset,
                            imageWriter,
                            SupportedPdfParseMethod.TXT,
                            start_page_id=start_page_id,