Delete next_docs/en/user_guide/quick_start/to_markdown.rst

next_docs/en/user_guide/quick_start/to_markdown.rst

-
-
-Convert To Markdown
-========================
-
-
-Local File Example
-^^^^^^^^^^^^^^^^^^
-
-.. code:: python
-
-    import os
-
-    from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
-    from magic_pdf.data.dataset import PymuDocDataset
-    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
-    from magic_pdf.config.enums import SupportedPdfParseMethod
-
-    # args
-    pdf_file_name = "abc.pdf"  # replace with the real pdf path
-    name_without_suff = pdf_file_name.split(".")[0]
-
-    # prepare env
-    local_image_dir, local_md_dir = "output/images", "output"
-    image_dir = str(os.path.basename(local_image_dir))
-
-    os.makedirs(local_image_dir, exist_ok=True)
-
-    image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
-        local_md_dir
-    )
-    image_dir = str(os.path.basename(local_image_dir))
-
-    # read bytes
-    reader1 = FileBasedDataReader("")
-    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
-
-    # proc
-    ## Create Dataset Instance
-    ds = PymuDocDataset(pdf_bytes)
-
-    ## inference
-    if ds.classify() == SupportedPdfParseMethod.OCR:
-        infer_result = ds.apply(doc_analyze, ocr=True)
-
-        ## pipeline
-        pipe_result = infer_result.pipe_ocr_mode(image_writer)
-
-    else:
-        infer_result = ds.apply(doc_analyze, ocr=False)
-
-        ## pipeline
-        pipe_result = infer_result.pipe_txt_mode(image_writer)
-
-    ### draw model result on each page
-    infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
-
-    ### draw layout result on each page
-    pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
-
-    ### draw spans result on each page
-    pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
-
-    ### dump markdown
-    pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
-
-    ### dump content list
-    pipe_result.dump_content_list(md_writer, f"{name_without_suff}_content_list.json", image_dir)
-
-
-S3 File Example
-^^^^^^^^^^^^^^^^
-
-.. code:: python
-
-    import os
-
-    from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
-    from magic_pdf.data.dataset import PymuDocDataset
-    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
-
-    bucket_name = "{Your S3 Bucket Name}"  # replace with real bucket name
-    ak = "{Your S3 access key}"  # replace with real s3 access key
-    sk = "{Your S3 secret key}"  # replace with real s3 secret key
-    endpoint_url = "{Your S3 endpoint_url}"  # replace with real s3 endpoint_url
-
-
-    reader = S3DataReader('unittest/tmp/', bucket_name, ak, sk, endpoint_url)  # replace `unittest/tmp` with the real s3 prefix
-    writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
-    image_writer = S3DataWriter('unittest/tmp/images', bucket_name, ak, sk, endpoint_url)
-
-    # args
-    pdf_file_name = (
-        "s3://llm-pdf-text-1/unittest/tmp/bug5-11.pdf"  # replace with the real s3 path
-    )
-
-    # prepare env
-    local_dir = "output"
-    name_without_suff = os.path.basename(pdf_file_name).split(".")[0]
-
-    # read bytes
-    pdf_bytes = reader.read(pdf_file_name)  # read the pdf content
-
-    # proc
-    ## Create Dataset Instance
-    ds = PymuDocDataset(pdf_bytes)
-
-    ## inference
-    if ds.classify() == SupportedPdfParseMethod.OCR:
-        infer_result = ds.apply(doc_analyze, ocr=True)
-
-        ## pipeline
-        pipe_result = infer_result.pipe_ocr_mode(image_writer)
-    else:
-        infer_result = ds.apply(doc_analyze, ocr=False)
-
-        ## pipeline
-        pipe_result = infer_result.pipe_txt_mode(image_writer)
-
-    ### draw model result on each page
-    infer_result.draw_model(os.path.join(local_dir, f'{name_without_suff}_model.pdf'))  # dump to local
-
-    ### draw layout result on each page
-    pipe_result.draw_layout(os.path.join(local_dir, f'{name_without_suff}_layout.pdf'))  # dump to local
-
-    ### draw spans result on each page
-    pipe_result.draw_span(os.path.join(local_dir, f'{name_without_suff}_spans.pdf'))   # dump to local
-
-    ### dump markdown
-    pipe_result.dump_md(writer, f'{name_without_suff}.md', "unittest/tmp/images")    # dump to remote s3
-
-    ### dump content list
-    pipe_result.dump_content_list(md_writer, f"{name_without_suff}_content_list.json", image_dir)
-
-
-Check :doc:`../data/data_reader_writer` for more [reader | writer] examples and check :doc:`../../api/pipe_operators` or :doc:`../../api/model_operators` for api details