rfctr(pptx): minify HTML and table.text is cct (#3734)

**Summary**
Eliminate historical "idiosyncracies" of `table.metadata.text_as_html`
HTML introduced by `partition_pptx()`. Produce minified `.text_as_html`
consistent with that formed by chunking.

**Additional Context**
- PPTX `.metadata.text_as_html` is minified (no extra whitespace or
thead, tbody, tfoot elements).
- `table.text` is clean-concatenated-text (CCT) of table.
- Last use of `tabulate` library is removed and that dependency is
removed from `base.in`.

CHANGELOG.md

@@ -1,4 +1,4 @@
-## 0.16.1-dev3
+## 0.16.1-dev4
 
 ### Enhancements
 
@@ -8,10 +8,11 @@
 
 * **Remove unsupported chipper model**
 * **Rewrite of `partition.email` module and tests.** Use modern Python stdlib `email` module interface to parse email messages and attachments. This change shortens and simplifies the code, and makes it more robust and maintainable. Several historical problems were remedied in the process.
-* **Minify text_as_html from DOCX.** Previously `.metadata.text_as_html` for DOCX tables was "bloated" with whitespace and noise elements introduced by `tabulate` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count without preserving all text.
+* **Minify text_as_html from DOCX.** Previously `.metadata.text_as_html` for DOCX tables was "bloated" with whitespace and noise elements introduced by `tabulate` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count while preserving all text.
 * **Fall back to filename extension-based file-type detection for unidentified OLE files.** Resolves a problem where a DOC file that could not be detected as such by `filetype` was incorrectly identified as a MSG file.
-* **Minify text_as_html from XLSX.** Previously `.metadata.text_as_html` for DOCX tables was "bloated" with whitespace and noise elements introduced by `pandas` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count without preserving all text.
+* **Minify text_as_html from XLSX.** Previously `.metadata.text_as_html` for DOCX tables was "bloated" with whitespace and noise elements introduced by `pandas` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count while preserving all text.
-* **Minify text_as_html from CSV.** Previously `.metadata.text_as_html` for CSV tables was "bloated" with whitespace and noise elements introduced by `pandas` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count without preserving all text.
+* **Minify text_as_html from CSV.** Previously `.metadata.text_as_html` for CSV tables was "bloated" with whitespace and noise elements introduced by `pandas` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count while preserving all text.
+* **Minify text_as_html from PPTX.** Previously `.metadata.text_as_html` for PPTX tables was "bloated" with whitespace and noise elements introduced by `tabulate` that produced over-chunking and lower "semantic density" of elements. Reduce HTML to minimum character count while preserving all text and structure.
 
 ## 0.16.0
 

requirements/base.in

@@ -4,7 +4,6 @@ filetype
 python-magic
 lxml
 nltk
-tabulate
 requests
 beautifulsoup4
 emoji

test_unstructured/partition/common/test_common.py

@@ -388,17 +388,6 @@ def test_convert_office_docs_respects_wait_timeout():
     assert np.sum([(path / "simple.docx").is_file() for path in paths_to_save]) < 3
 
 
-class MockDocxEmptyTable:
-    def __init__(self):
-        self.rows = []
-
-
-def test_convert_ms_office_table_to_text_works_with_empty_tables():
-    table = MockDocxEmptyTable()
-    assert common.convert_ms_office_table_to_text(table, as_html=True) == ""
-    assert common.convert_ms_office_table_to_text(table, as_html=False) == ""
-
-
 @pytest.mark.parametrize(
     ("text", "expected"),
     [

test_unstructured/partition/test_pptx.py

@@ -247,15 +247,11 @@ def test_partition_pptx_grabs_tables():
     assert elements[1].text.startswith("Column 1")
     assert elements[1].text.strip().endswith("Aqua")
     assert elements[1].metadata.text_as_html == (
-        "<table>\n"
+        "<table>"
-        "<thead>\n"
+        "<tr><td>Column 1</td><td>Column 2</td><td>Column 3</td></tr>"
-        "<tr><th>Column 1  </th><th>Column 2  </th><th>Column 3  </th></tr>\n"
+        "<tr><td>Red</td><td>Green</td><td>Blue</td></tr>"
-        "</thead>\n"
+        "<tr><td>Purple</td><td>Orange</td><td>Yellow</td></tr>"
-        "<tbody>\n"
+        "<tr><td>Tangerine</td><td>Pink</td><td>Aqua</td></tr>"
-        "<tr><td>Red       </td><td>Green     </td><td>Blue      </td></tr>\n"
-        "<tr><td>Purple    </td><td>Orange    </td><td>Yellow    </td></tr>\n"
-        "<tr><td>Tangerine </td><td>Pink      </td><td>Aqua      </td></tr>\n"
-        "</tbody>\n"
         "</table>"
     )
     assert elements[1].metadata.filename == "fake-power-point-table.pptx"
@@ -516,7 +512,7 @@ def test_partition_pptx_hierarchy_sample_document():
         (2, "6ec455f5f19782facf184886876c9a66", "5614b00c3f6bff23ebba1360e10f6428"),
         (0, "8319096532fe2e55f66c491ea8313150", "2f57a8d4182e6fd5bd5842b0a2d9841b"),
         (None, None, "4120066d251ba675ade42e8a167ca61f"),
-        (None, None, "2ed3bd10daace79ac129cbf8faf22bfc"),
+        (None, None, "efb9d74b4f8be6308c9a9006da994e12"),
         (0, None, "fd08cacbaddafee5cbacc02528536ee5"),
     ]
 

unstructured/__version__.py

@@ -1 +1 @@
-__version__ = "0.16.1-dev3"  # pragma: no cover
+__version__ = "0.16.1-dev4"  # pragma: no cover

unstructured/partition/common/common.py

@@ -9,7 +9,6 @@ from typing import IO, TYPE_CHECKING, Any, Optional, TypeVar, cast
 
 import emoji
 import psutil
-from tabulate import tabulate
 
 from unstructured.documents.coordinates import CoordinateSystem, PixelSpace
 from unstructured.documents.elements import (
@@ -29,9 +28,6 @@ from unstructured.nlp.patterns import ENUMERATED_BULLETS_RE, UNICODE_BULLETS_RE
 from unstructured.partition.utils.constants import SORT_MODE_DONT, SORT_MODE_XY_CUT
 from unstructured.utils import dependency_exists, first
 
-if dependency_exists("pptx") and dependency_exists("pptx.table"):
-    from pptx.table import Table as PptxTable
-
 if dependency_exists("numpy") and dependency_exists("cv2"):
     from unstructured.partition.utils.sorting import sort_page_elements
 
@@ -396,27 +392,6 @@ def convert_to_bytes(file: bytes | IO[bytes]) -> bytes:
     raise ValueError("Invalid file-like object type")
 
 
-def convert_ms_office_table_to_text(table: PptxTable, as_html: bool = True) -> str:
-    """Convert a PPTX table object to an HTML table string using the tabulate library.
-
-    Args:
-        table (Table): A pptx.table.Table object.
-        as_html (bool): Whether to return the table as an HTML string (True) or a
-            plain text string (False)
-
-    Returns:
-        str: An table string representation of the input table.
-    """
-    rows = list(table.rows)
-
-    if not rows:
-        return ""
-
-    headers = [cell.text for cell in rows[0].cells]
-    data = [[cell.text for cell in row.cells] for row in rows[1:]]
-    return tabulate(data, headers=headers, tablefmt="html" if as_html else "plain")
-
-
 def contains_emoji(s: str) -> bool:
     """
     Check if the input string contains any emoji characters.

unstructured/partition/pptx.py

@@ -22,6 +22,7 @@ from pptx.slide import Slide
 from pptx.text.text import _Paragraph  # pyright: ignore [reportPrivateUsage]
 
 from unstructured.chunking import add_chunking_strategy
+from unstructured.common.html_table import HtmlTable, htmlify_matrix_of_cell_texts
 from unstructured.documents.elements import (
     Element,
     ElementMetadata,
@@ -34,7 +35,6 @@ from unstructured.documents.elements import (
     Title,
 )
 from unstructured.file_utils.model import FileType
-from unstructured.partition.common.common import convert_ms_office_table_to_text
 from unstructured.partition.common.metadata import apply_metadata, get_last_modified_date
 from unstructured.partition.text_type import (
     is_email_address,
@@ -213,38 +213,6 @@ class _PptxPartitioner:
         PicturePartitionerCls = self._opts.picture_partitioner
         yield from PicturePartitionerCls.iter_elements(picture, self._opts)
 
-    def _iter_title_shape_element(self, shape: Shape) -> Iterator[Element]:
-        """Generate Title element for each paragraph in title `shape`.
-
-        Text is most likely a title, but in the rare case that the title shape was used
-        for the slide body text, also check for bulleted paragraphs."""
-        if self._shape_is_off_slide(shape):
-            return
-
-        depth = 0
-        for paragraph in shape.text_frame.paragraphs:
-            text = paragraph.text
-            if text.strip() == "":
-                continue
-
-            if self._is_bulleted_paragraph(paragraph):
-                bullet_depth = paragraph.level or 0
-                yield ListItem(
-                    text=text,
-                    metadata=self._opts.text_metadata(category_depth=bullet_depth),
-                    detection_origin=DETECTION_ORIGIN,
-                )
-            elif is_email_address(text):
-                yield EmailAddress(text=text, detection_origin=DETECTION_ORIGIN)
-            else:
-                # increment the category depth by the paragraph increment in the shape
-                yield Title(
-                    text=text,
-                    metadata=self._opts.text_metadata(category_depth=depth),
-                    detection_origin=DETECTION_ORIGIN,
-                )
-                depth += 1  # Cannot enumerate because we want to skip empty paragraphs
-
     def _iter_shape_elements(self, shape: Shape) -> Iterator[Element]:
         """Generate Text or subtype element for each paragraph in `shape`."""
         if self._shape_is_off_slide(shape):
@@ -280,17 +248,54 @@ class _PptxPartitioner:
 
         An empty table does not produce an element.
         """
-        text_table = convert_ms_office_table_to_text(graphfrm.table, as_html=False).strip()
+        if not (rows := list(graphfrm.table.rows)):
-        if not text_table:
             return
-        html_table = None
+
-        if self._opts.infer_table_structure:
+        html_text = htmlify_matrix_of_cell_texts(
-            html_table = convert_ms_office_table_to_text(graphfrm.table, as_html=True)
+            [[cell.text for cell in row.cells] for row in rows]
-        yield Table(
-            text=text_table,
-            metadata=self._opts.table_metadata(html_table),
-            detection_origin=DETECTION_ORIGIN,
         )
+        html_table = HtmlTable.from_html_text(html_text)
+
+        if not html_table.text:
+            return
+
+        metadata = self._opts.table_metadata(
+            html_table.html if self._opts.infer_table_structure else None
+        )
+
+        yield Table(text=html_table.text, metadata=metadata, detection_origin=DETECTION_ORIGIN)
+
+    def _iter_title_shape_element(self, shape: Shape) -> Iterator[Element]:
+        """Generate Title element for each paragraph in title `shape`.
+
+        Text is most likely a title, but in the rare case that the title shape was used
+        for the slide body text, also check for bulleted paragraphs."""
+        if self._shape_is_off_slide(shape):
+            return
+
+        depth = 0
+        for paragraph in shape.text_frame.paragraphs:
+            text = paragraph.text
+            if text.strip() == "":
+                continue
+
+            if self._is_bulleted_paragraph(paragraph):
+                bullet_depth = paragraph.level or 0
+                yield ListItem(
+                    text=text,
+                    metadata=self._opts.text_metadata(category_depth=bullet_depth),
+                    detection_origin=DETECTION_ORIGIN,
+                )
+            elif is_email_address(text):
+                yield EmailAddress(text=text, detection_origin=DETECTION_ORIGIN)
+            else:
+                # increment the category depth by the paragraph increment in the shape
+                yield Title(
+                    text=text,
+                    metadata=self._opts.text_metadata(category_depth=depth),
+                    detection_origin=DETECTION_ORIGIN,
+                )
+                depth += 1  # Cannot enumerate because we want to skip empty paragraphs
 
     def _order_shapes(self, slide: Slide) -> tuple[Shape | None, Sequence[BaseShape]]:
         """Orders the shapes on `slide` from top to bottom and left to right.