Fixes#2339
Fixes to HTML partitioning introduced with v0.11.0 removed the use of
`tabulate` for forming the HTML placed in `HTMLTable.text_as_html`. This
had several benefits, but part of `tabulate`'s behavior was to make
row-length (cell-count) uniform across the rows of the table.
Lacking this prior uniformity produced a downstream problem reported in
On closer inspection, the method used to "harvest" cell-text was
producing more text-nodes than there were cells and was sensitive to
where whitespace was used to format the HTML. It also "moved" text to
different columns in certain rows.
Refine the cell-text gathering mechanism to get exactly one text string
for each row cell, eliminating whitespace formatting nodes and producing
strict correspondence between the number of cells in the original HTML
table row and that placed in HTML.text_as_html.
HTML tables that are uniform (every row has the same number of cells)
will produce a uniform table in `.text_as_html`. Merged cells may still
produce a non-uniform table in `.text_as_html` (because the source table
is non-uniform).
Currently, we're using different kwarg names in partition() and
partition_pdf(), which has implications for the API since it goes
through partition().
### Summary
- rename `extract_element_types` -> `extract_image_block_types`
- rename `image_output_dir_path` to `extract_image_block_output_dir`
- rename `extract_to_payload` -> `extract_image_block_to_payload`
- rename `pdf_extract_images` -> `extract_images_in_pdf` in
`partition.auto`
- add unit tests to test element extraction for `pdf/image` via
`partition.auto`
### Testing
CI should pass.
Closes#2302.
### Summary
- add functionality to get a Base64 encoded string from a PIL image
- store base64 encoded image data in two metadata fields: `image_base64`
and `image_mime_type`
- update the "image element filter" logic to keep all image elements in
the output if a user specifies image extraction
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
extract_element_types=["Image", "Table"],
extract_to_payload=True,
)
```
or
```
from unstructured.partition.auto import partition
elements = partition(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
pdf_extract_element_types=["Image", "Table"],
pdf_extract_to_payload=True,
)
```
Closes#2160
Explicitly adds `hi_res_model_name` as kwarg to relevant functions and
notes that `model_name` is to be deprecated.
Testing:
```
from unstructured.partition.auto import partition
filename = "example-docs/DA-1p.pdf"
elements = partition(filename, strategy="hi_res", hi_res_model_name="yolox")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Steve Canny <stcanny@gmail.com>
Co-authored-by: Christine Straub <christinemstraub@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
The text of an oversized chunk is split on an arbitrary character
boundary (mid-word). The `chunk_by_character()` strategy introduces the
idea of allowing the user to specify a separator to use for
chunk-splitting. For `langchain` this is typically "\n\n", "\n", or " ";
blank-line, newline, or word boundaries respectively.
Even if the user is allowed to specify a separator, we must provide
fall-back for when a chunk contains no such character. This can be done
incrementally, like blank-line is preferable to newline, newline is
preferable to word, and word is preferable to arbitrary character.
Further, there is nothing particular to `chunk_by_character()` in
providing such a fall-back text-splitting strategy. It would be
preferable for all strategies to split oversized chunks on even-word
boundaries for example.
Note that while a "blank-line" ("\n\n") may be common in plain text, it
is unlikely to appear in the text of an element because it would have
been interpreted as an element boundary during partitioning.
Add _TextSplitter with basic separator preferences and fall-back and
apply it to chunk-splitting for all strategies. The `by_character`
chunking strategy may enhance this behavior by adding the option for a
user to specify a particular separator suited to their use case.
### Summary
This PR is the second part of `pdfminer` refactor to move it from
`unstructured-inference` repo to `unstructured` repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/294. This
PR adds logic to merge the extracted layout with the inferred layout.
The updated workflow for the `hi_res` strategy:
* pass the document (as data/filename) to the `inference` repo to get
`inferred_layout` (DocumentLayout)
* pass the `inferred_layout` returned from the `inference` repo and the
document (as data/filename) to the `pdfminer_processing` module, which
first opens the document (create temp file/dir as needed), and splits
the document by pages
* if is_image is `True`, return the passed
inferred_layout(DocumentLayout)
* if is_image is `False`:
* get extracted_layout (TextRegions) from the passed
document(data/filename) by pdfminer
* merge `extracted_layout` (TextRegions) with the passed
`inferred_layout` (DocumentLayout)
* return the `inferred_layout `(DocumentLayout) with updated elements
(all merged LayoutElements) as merged_layout (DocumentLayout)
* pass merged_layout and the document (as data/filename) to the `OCR`
module, which first opens the document (create temp file/dir as needed),
and splits the document by pages (convert PDF pages to image pages for
PDF file)
### Note
This PR also fixes issue #2164 by using functionality similar to the one
implemented in the `fast` strategy workflow when extracting elements by
`pdfminer`.
### TODO
* image extraction refactor to move it from `unstructured-inference`
repo to `unstructured` repo
* improving natural reading order by applying the current default
`xycut` sorting to the elements extracted by `pdfminer`
Courtesy @cdpierse.
Adds a test to PR #1529 in accordance with feedback.
Description from original PR:
In python the default behaviour of `requests.get` without a `timeout`
being set is to hang indefinitely. We have a production use case where
the desired behaviour would be to raise a timeout error rather than have
the application just hang.
This PR adds a new optional keyword parameter `request_timeout` to
`partition` which is passed to `file_and_type_from_url` in the case
where we are fetching from a URL. This is then passed to `requests.get`
---------
Co-authored-by: Charles Pierse <charlespierse@gmail.com>
Closes#1870
Defining both `languages` and `ocr_languages` raises a ValueError, but
the api defaults to `ocr_languages` being an empty string, so if users
define `languages` they are automatically hitting the ValueError.
This fix checks if `ocr_languages` is an empty string and converts it to
`None` to avoid this.
### Testing
On the main branch, the following will raise the ValueError, but it will
correctly partition on this branch
```
from unstructured.partition.auto import partition
filename = "example-docs/category-level.docx"
elements = partition(filename,languages=['spa'],ocr_languages="")
elements[0].metadata.languages
```
---------
Co-authored-by: yuming <305248291@qq.com>
Co-authored-by: Yuming Long <63475068+yuming-long@users.noreply.github.com>
Co-authored-by: Austin Walker <awalk89@gmail.com>
### Summary
A follow up ticket on
https://github.com/Unstructured-IO/unstructured/pull/1801, I forgot to
remove the lines that pass extract_tables to inference, and noted the
table regression if we only do one OCR for entire doc
**Tech details:**
* stop passing `extract_tables` parameter to inference
* added table extraction ingest test for image, which was skipped
before, and the "text_as_html" field contains the OCR output from the
table OCR refactor PR
* replaced `assert_called_once_with` with `call_args` so that the unit
tests don't need to test additional parameters
* added `error_margin` as ENV when comparing bounding boxes
of`ocr_region` with `table_element`
* added more tests for tables and noted the table regression in test for
partition pdf
### Test
* for stop passing `extract_tables` parameter to inference, run test
`test_partition_pdf_hi_res_ocr_mode_with_table_extraction` before this
branch and you will see warning like `Table OCR from get_tokens method
will be deprecated....`, which means it called the table OCR in
inference repo. This branch removed the warning.
Carrying `skip_infer_table_types` to `infer_table_structure` in
partition flow. Now PPT/X, DOC/X, etc. Table elements should not have a
`text_as_html` field.
Note: I've continued to exclude this var from partitioners that go
through html flow, I think if we've already got the html it doesn't make
sense to carry the infer variable along, since we're not 'infer-ing' the
html table in these cases.
TODO:
✅ add unit tests
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: amanda103 <amanda103@users.noreply.github.com>
Closes `unstructured-inference` issue
[#265](https://github.com/Unstructured-IO/unstructured-inference/issues/265).
Cleaned up the kwarg handling, taking opportunities to turn instances of
handling kwargs as dicts to just using them as normal in function
signatures.
#### Testing:
Should just pass CI.
The current code assumes the first line of csv and tsv files are a
header line. Most csv and tsv files don't have a header line, and even
for those that do, dropping this line may not be the desired behavior.
Here is a snippet of code that demonstrates the current behavior and the
proposed fix
```
import pandas as pd
from lxml.html.soupparser import fromstring as soupparser_fromstring
c1 = """
Stanley Cups,,
Team,Location,Stanley Cups
Blues,STL,1
Flyers,PHI,2
Maple Leafs,TOR,13
"""
f = "./test.csv"
with open(f, 'w') as ff:
ff.write(c1)
print("Suggested Improvement Keep First Line")
table = pd.read_csv(f, header=None)
html_text = table.to_html(index=False, header=False, na_rep="")
text = soupparser_fromstring(html_text).text_content()
print(text)
print("\n\nOriginal Looses First Line")
table = pd.read_csv(f)
html_text = table.to_html(index=False, header=False, na_rep="")
text = soupparser_fromstring(html_text).text_content()
print(text)
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
### Summary
Closes#1714
Changes the default value for `languages` to `None` for elements that
don't have text or the language can't be detected.
### Testing
```
from unstructured.partition.auto import partition
filename = "example-docs/handbook-1p.docx"
elements = partition(filename=filename, detect_language_per_element=True)
# PageBreak elements don't have text and will be collected here
none_langs = [element for element in elements if element.metadata.languages is None]
none_langs[0].text
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Coniferish <Coniferish@users.noreply.github.com>
Co-authored-by: cragwolfe <crag@unstructured.io>
### Summary
Closes#1534 and #1535
Detects document language using `langdetect` package.
Creates new kwargs for user to set the document language (`languages`)
or detect the language at the element level instead of the default
document level (`detect_language_per_element`)
---------
Co-authored-by: shreyanid <42684285+shreyanid@users.noreply.github.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Coniferish <Coniferish@users.noreply.github.com>
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Austin Walker <austin@unstructured.io>
This PR adds the `max_characters` (hard max) param to non-table element
chunking. Additionally updates the `num_characters` metadata to
`max_characters` to make it clearer which param we're referencing.
To test:
```
from unstructured.partition.html import partition_html
filename = "example-docs/example-10k-1p.html"
chunk_elements = partition_html(
filename,
chunking_strategy="by_title",
combine_text_under_n_chars=0,
new_after_n_chars=50,
max_characters=100,
)
for chunk in chunk_elements:
print(len(chunk.text))
# previously we were only respecting the "soft max" (default of 500) for elements other than tables
# now we should see that all the elements have text fields under 100 chars.
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
## Summary
Second part of OCR refactor to move it from inference repo to
unstructured repo, first part is done in
https://github.com/Unstructured-IO/unstructured-inference/pull/231. This
PR adds OCR process logics to entire page OCR, and support two OCR
modes, "entire_page" or "individual_blocks".
The updated workflow for `Hi_res` partition:
* pass the document as data/filename to inference repo to get
`inferred_layout` (DocumentLayout)
* pass the document as data/filename to OCR module, which first open the
document (create temp file/dir as needed), and split the document by
pages (convert PDF pages to image pages for PDF file)
* if ocr mode is `"entire_page"`
* OCR the entire image
* merge the OCR layout with inferred page layout
* if ocr mode is `"individual_blocks"`
* from inferred page layout, find element with no extracted text, crop
the entire image by the bboxes of the element
* replace empty text element with the text obtained from OCR the cropped
image
* return all merged PageLayouts and form a DocumentLayout subject for
later on process
This PR also bump `unstructured-inference==0.7.2` since the branch relay
on OCR refactor from unstructured-inference.
## Test
```
from unstructured.partition.auto import partition
entrie_page_ocr_mode_elements = partition(filename="example-docs/english-and-korean.png", ocr_mode="entire_page", ocr_languages="eng+kor", strategy="hi_res")
individual_blocks_ocr_mode_elements = partition(filename="example-docs/english-and-korean.png", ocr_mode="individual_blocks", ocr_languages="eng+kor", strategy="hi_res")
print([el.text for el in entrie_page_ocr_mode_elements])
print([el.text for el in individual_blocks_ocr_mode_elements])
```
latest output:
```
# entrie_page
['RULES AND INSTRUCTIONS 1. Template for day 1 (korean) , for day 2 (English) for day 3 both English and korean. 2. Use all your accounts. use different emails to send. Its better to have many email', 'accounts.', 'Note: Remember to write your own "OPENING MESSAGE" before you copy and paste the template. please always include [TREASURE HARUTO] for example:', '안녕하세요, 저 희 는 YGEAS 그룹 TREASUREWH HARUTOM|2] 팬 입니다. 팬 으 로서, HARUTO 씨 받 는 대 우 에 대해 의 구 심 과 불 공 평 함 을 LRU, 이 일 을 통해 저 희 의 의 혹 을 전 달 하여 귀 사 의 진지한 민 과 적극적인 답 변 을 받을 수 있 기 를 바랍니다.', '3. CC Harutonations@gmail.com so we can keep track of how many emails were', 'successfully sent', '4. Use the hashtag of Haruto on your tweet to show that vou have sent vour email]', '메 고']
# individual_blocks
['RULES AND INSTRUCTIONS 1. Template for day 1 (korean) , for day 2 (English) for day 3 both English and korean. 2. Use all your accounts. use different emails to send. Its better to have many email', 'Note: Remember to write your own "OPENING MESSAGE" before you copy and paste the template. please always include [TREASURE HARUTO] for example:', '안녕하세요, 저 희 는 YGEAS 그룹 TREASURES HARUTOM| 2] 팬 입니다. 팬 으로서, HARUTO 씨 받 는 대 우 에 대해 의 구 심 과 habe ERO, 이 머 일 을 적극 저 희 의 ASS 전 달 하여 귀 사 의 진지한 고 2 있 기 를 바랍니다.', '3. CC Harutonations@gmail.com so we can keep track of how many emails were ciiccecefisliy cant', 'VULLESSIULY Set 4. Use the hashtag of Haruto on your tweet to show that you have sent your email']
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: yuming-long <yuming-long@users.noreply.github.com>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
This change is adding to our `add_chunking_strategy` logic so that we
are able to chunk Table elements' `text` and `text_as_html` params. In
order to keep the functionality under the same `by_title` chunking
strategy we have renamed the `combine_under_n_chars` to
`max_characters`. It functions the same way for the combining elements
under Title's, as well as specifying a chunk size (in chars) for
TableChunk elements.
*renaming the variable to `max_characters` will also reflect the 'hard
max' we will implement for large elements in followup PRs
Additionally -> some lint changes snuck in when I ran `make tidy` hence
the minor changes in unrelated files :)
TODO:
✅ add unit tests
--> note: added where I could to unit tests! Some unit tests I just
clarified that the chunking strategy was now 'by_title' because we don't
have a file example that has Table elements to test the
'by_num_characters' chunking strategy
✅ update changelog
To manually test:
```
In [1]: filename="example-docs/example-10k.html"
In [2]: from unstructured.chunking.title import chunk_table_element
In [3]: from unstructured.partition.auto import partition
In [4]: elements = partition(filename)
# element at -2 happens to be a Table, and we'll get chunks of char size 4 here
In [5]: chunks = chunk_table_element(elements[-2], 4)
# examine text and text_as_html params
ln [6]: for c in chunks:
print(c.text)
print(c.metadata.text_as_html)
```
---------
Co-authored-by: Yao You <theyaoyou@gmail.com>
- bump `unstructured-inference` to `0.6.6`
- specify default model name for element detection to be
`detectron2_onnx` to keep current behavior
- NOTE: the updated inference package by default would use yolox as
element detection model; this will be evaluated and enabled in a
separated PR
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
### Summary
Uses `langdetect` to detect all languages present in the input document.
### Details
- Converts all language codes (whether user inputted or detected using
`langdetect`) to a standard ISO 639-3 code.
- Adds `languages` field to the metadata
- Will revisit how to nonstandardly represent simplified vs traditional
Chinese scripts internally (separate PR).
- Update ingest test results to add `languages` field to documents. Some
other side effects are changes in order of some elements and changes in
element categorization
### Test
You can test the detect_languages function individually by importing the
function and inputting a text sample and optionally a language:
```
text = "My lubimy mleko i chleb."
doc_langs = detect_languages(text)
print(doc_langs)
```
-> ['ces', 'pol', 'slk']
---------
Co-authored-by: Newel H <37004249+newelh@users.noreply.github.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: shreyanid <shreyanid@users.noreply.github.com>
Co-authored-by: Trevor Bossert <37596773+tabossert@users.noreply.github.com>
Co-authored-by: Ronny H <138828701+ron-unstructured@users.noreply.github.com>
### Summary
In order to convert between incompatible language codes from packages
used for OCR, this change adds a function to map between any standard
language codes and tesseract OCR specific codes. Users can input
language information to `languages` in any Tesseract-supported langcode
or any ISO 639 standard language code.
### Details
- Introduces the
[python-iso639](https://pypi.org/project/python-iso639/) package for
matching standard language codes. Recompiles all dependencies.
- If a language is not already supplied by the user as a Tesseract
specific langcode, supplies all possible script/orthography variants of
the language to the Tesseract OCR agent.
### Test
Added many unit tests for a variety of language combinations, special
cases, and variants. For general testing, call partition functions with
any lang codes in the languages parameter (Tesseract or standard).
for example,
```
from unstructured.partition.auto import partition
elements = partition(filename="example-docs/layout-parser-paper.pdf", strategy="hi_res", languages=["en", "chi"])
print("\n\n".join([str(el) for el in elements]))
```
should supply eng+chi_sim+chi_sim_vert+chi_tra+chi_tra_vert to Tesseract
### Summary
In order to support language functionality other than Tesseract OCR, we
want to represent languages provided for either partitioning accuracy or
OCR as a standard list of langcodes as strings.
### Details
Follows the pattern established with PDFs in #1334. Adds languages (a
list of strings) as a parameter to partition in auto.py. Marks
ocr_languages for deprecation.
### Test
Call partition with a variety of filetypes (especially pdfs/images),
strategies, languages, or ocr_languages.
- inclusion of ocr_languages as a parameter should display a deprecation
warning and may proceed with partitioning if no other conflicts
- the other valid call outputs should be no different from the current
outputs
### Summary
Partial solution to #1185.
Related to #1222.
Creates decorator from `chunk_by_title` cleaning brick.
Breaks a document into sections based on the presence of Title elements.
Also starts a new section under the following conditions:
- If metadata changes, indicating a change in section or page or a
switch to processing attachments. If `multipage_sections=True`, sections
can span pages. `multipage_sections` defaults to True.
- If the length of the section exceeds `new_after_n_chars` characters.
The default is 1500. The **chunking function does not split individual
elements**, so it's possible for a section to exceed that threshold if
an individual element if over `new_after_n_chars characters`, which
could occur with a long NarrativeText element.
Combines sections under these conditions
- Sections under `combine_under_n_chars` characters are combined. The
default is 500.
### Testing
from unstructured.partition.html import partition_html
url = "https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-27-2023-0"
chunks = partition_html(url=url, chunking_strategy="by_title")
for chunk in chunks:
print(chunk)
print("\n\n" + "-"*80)
input()
### Summary
Closes#1229. Updates `partition_xml` so that the element type is
inferred on each leaf node when `xml_keep_tags=False` instead of
delegating splitting and partitioning to `partition_xml`. If
`xml_keep_tags=True`, the file is treated like a text file still and
partitioning is still delegated to `partition_text`.
Also adds the option to pass `text` as an input to `partition_xml`.
### Testing
Create a `parrots.xml` file that looks like:
```xml
<xml><parrot><name>Conure</name><description>A conure is a very friendly bird.
Conures are feathery and like to dance.</description></parrot></xml>
```
Run:
```python
from unstructured.partition.xml import partition_xml
from unstructured.staging.base import convert_to_dict
elements = partition_xml(filename="parrots.xml")
convert_to_dict(elements)
```
One `main`, the output is the following. Notice how the `<name>` tag
incorrectly gets merged into `<description>` in the first element.
```python
[{'element_id': '7ae4074435df8dfcefcf24a4e6c52026',
'metadata': {'file_directory': '/home/matt/tmp',
'filename': 'parrots.xml',
'filetype': 'application/xml',
'last_modified': '2023-08-30T14:21:38'},
'text': 'Conure A conure is a very friendly bird.',
'type': 'NarrativeText'},
{'element_id': '859ecb332da6961acd2fb6a0185d1549',
'metadata': {'file_directory': '/home/matt/tmp',
'filename': 'parrots.xml',
'filetype': 'application/xml',
'last_modified': '2023-08-30T14:21:38'},
'text': 'Conures are feathery and like to dance.',
'type': 'NarrativeText'}]
```
One the feature branch, the output is the following, and the tags are
correctly separated.
```python
[{'element_id': '5512218914e4eeacf71a9cd42c373710',
'metadata': {'file_directory': '/home/matt/tmp',
'filename': 'parrots.xml',
'filetype': 'application/xml',
'last_modified': '2023-08-30T14:21:38'},
'text': 'Conure',
'type': 'Title'},
{'element_id': '113bf8d250c2b1a77c9c2caa4b812f85',
'metadata': {'file_directory': '/home/matt/tmp',
'filename': 'parrots.xml',
'filetype': 'application/xml',
'last_modified': '2023-08-30T14:21:38'},
'text': 'A conure is a very friendly bird.\n'
'\n'
'Conures are feathery and like to dance.',
'type': 'NarrativeText'}]
```
### Summary
Closes#1184. Updates `partition_html` to respect the ordering of
`<pre>` tags in HTML documents.
### Testing
The elements in the following example should be in the correct order.
```python
from unstructured.partition.html import partition_html
html_text = """
<pre>The Big Brown Bear</pre>
<div>The big brown bear is growling.</div>
<pre>The big brown bear is sleeping.</pre>
<div>The Big Blue Bear</div>
"""
elements = partition_html(text=html_text)
print("\n\n".join([str(el) for el in elements]))
```
Add test case test_partition_image_with_multipage_tiff that reads multipage TIFF file and
- confirms that the function reads all the pages in the TIFF.
- page number is added to the metadata
This PR is branched from and developed on top of 6d6be99 commit.
### Summary
Closes#1007. Adds a deprecation warning for the `file_filename` kwarg
to `partition`, `partition_via_api`, and `partition_multiple_via_api`.
Also catches a warning in `ebooklib` that we do not want to emit in
`unstructured`.
### Testing
```python
from unstructured.partition.auto import partition
filename = "example-docs/winter-sports.epub"
# Should not emit a warning
with open(filename, "rb") as f:
elements = partition(file=f, metadata_filename="test.epub")
# Should be test.epub
elements[0].metadata.filename
# Should emit a warning
with open(filename, "rb") as f:
elements = partition(file=f, file_filename="test.epub")
# Should be test.epub
elements[0].metadata.filename
# Should raise an error
with open(filename, "rb") as f:
elements = partition(file=f, metadata_filename="test.epub", file_filename="test.epub")
```
### Summary
Updates `partition` to let users know to installs the appropriate extras
if they're missing. Prior to this PR, users would get an exception
stating `partition_pdf` (or whichever function that requires extras)
does not exist.
### Testing
First `pip uninstall ebooklib`. Then run
```python
from unstructured.partition.auto import partition
partition(filename="example-docs/winter-sports.epub")
```
The error should look like
```python
ImportError: partition_epub is not available. Install the epub dependencies with pip install "unstructured[epub]"
```
* add auto_paragraph_grouper. add line break pattern.
* combine group_broken_paragraph and blank_line_grouper function
* fix make check errors
* fix make check errors
* fix make check errors
* fix make check errors
* run make tidy to fix errors
* tidy core.py and text.py
* fix blank-line breaker to extends the result and replace new line with space
* fix function name typo
* call group_broken_paragraphs for blank_line_grouper
* edit function name from one_line_grouper to new_line_grouper for consistency
* edit threshold from 0.5 to 0.1
* edit threshold from 0.5 to 0.1
* Revert "call group_broken_paragraphs for blank_line_grouper"
This reverts commit 8fb93b7aa7c4d7e0320ac1e09c77da44c9b6c7d9.
* revert to commit 8fb93b7 and change threshold from 0.5 to 0.1
* edit test_text assertion. remove all BULLETS_PATTERN.
* Update ingest test fixtures (#1052)
Co-authored-by: ahmetmeleq <ahmetmeleq@users.noreply.github.com>
* edit test case in test_xml_partition
* update assertion on test_auto
---------
Co-authored-by: Klaijan Sinteppadon <klaijan@Klaijans-MacBook-Pro.local>
Co-authored-by: Klaijan Sinteppadon <klaijan@klaijans-mbp.mynetworksettings.com>
Co-authored-by: Klaijan Sinteppadon <klaijan@Klaijans-MBP.fios-router.home>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: ahmetmeleq <ahmetmeleq@users.noreply.github.com>
* add param and test in image table extraction
* version and changelog
* need to publish this one for api repo
* add new param skip_infer_table_types
* use warning
* clean up with mapping
* add test for tsv
* fix test fail
* weird change from merge
* doc nit
* don't use mapping
* correct conflict
More deterministic element ordering when using hi_res PDF parsing strategy (from unstructured-inference bump to 0.5.4)
Make large model available (from unstructured-inference bump to 0.5.3)
Combine inferred elements with extracted elements (from unstructured-inference bump to 0.5.2)
---------
Co-authored-by: Roman Isecke <roman@unstructured.io>
Co-authored-by: Crag Wolfe <crag@unstructured.io>
- Adds reusable validation scripts (check-x.sh) to minimize repeated (or near-repeated) code and create one source of truth
- Restructures the location of download and output folders such that they are nested in the test_unstructured_ingest directory
- Adds gitignore for output folders / files to avoid them accidentally getting checked into the repository
- Construct paths as reusable variables declared at top of scripts
- Sort order of flag for ingest calls, across all tests (this makes it easier to parse at a glance)
- OVERWRITE_FIXTURES removes all old fixtures for path to guarantee no stale results are left behind
- Bonus: don't check/exit on expected number of expected outputs when OVERWRITE_FIXTURES is true
- Bonus: exclude file_directory from Slack and Discord test scripts (match convention in all others)
Avoid setting metadata in constructor signature for elements because that can lead to unexpected object reuse (and modification).
Bonus refactor for PageBreak to have text values of "".
---------
Co-authored-by: Alan Bertl <alan@unstructured.io>
Co-authored-by: Crag Wolfe <crag@unstructuredai.io>
* feature(html partition): parse pre tag
* chore: update CHANGELOG.md
* style: black format xml.py
* Added tests dor html with pre tag
* remove skip test, update parse pre tag
* fix style
* chore: spell check
* chore: update changelog & version
* chore: update ingest test fixtures
* chore: add exception handling if `element.text` is `None` in `_read_xml`
* test: add more sanity testing on the `.text` content of the element(s)
* refactor: move the conditional logic for <pre> outside of the `try/except` block
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
* Adds functionality to extract charset info from eml files
* Adds missed file-like object handling in detect_file_encoding
* Adds functionality to replace the MIME encodings for eml files with one of the
common encodings if a unicode error occurs
* Organize the eml example files in the example-docs/eml directory
