From f670b4a8338e3807d3105f9c210bffbdebaf9d35 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 2 Oct 2024 10:06:31 -0700 Subject: [PATCH 1/2] feat: add paddleocr --- docetl/parsing_tools.py | 66 ++ docs/examples/custom-parsing.md | 117 ++- poetry.lock | 1146 ++++++++++++++++++++++++- pyproject.toml | 5 +- tests/data/PublicWaterMassMailing.pdf | Bin 0 -> 2681358 bytes tests/test_parsing_tools.py | 10 + 6 files changed, 1298 insertions(+), 46 deletions(-) create mode 100644 tests/data/PublicWaterMassMailing.pdf diff --git a/docetl/parsing_tools.py b/docetl/parsing_tools.py index 24b3e2fa..ecdd3f7d 100644 --- a/docetl/parsing_tools.py +++ b/docetl/parsing_tools.py @@ -312,6 +312,71 @@ def azure_di_read( ] +def paddleocr_pdf_to_string( + input_path: str, + doc_per_page: bool = False, + ocr_enabled: bool = True, + lang: str = "en", +) -> List[str]: + """ + Extract text and image information from a PDF file using PaddleOCR for image-based PDFs. + + **Note: this is very slow!!** + + Args: + input_path (str): Path to the input PDF file. + doc_per_page (bool): If True, return a list of strings, one per page. + If False, return a single string. + ocr_enabled (bool): Whether to enable OCR for image-based PDFs. + lang (str): Language of the PDF file. + + Returns: + List[str]: Extracted content as a list of formatted strings. + """ + from paddleocr import PaddleOCR + import fitz + import numpy as np + + ocr = PaddleOCR(use_angle_cls=True, lang=lang) + + pdf_content = [] + + with fitz.open(input_path) as pdf: + for page_num in range(len(pdf)): + page = pdf[page_num] + text = page.get_text() + images = [] + + # Extract image information + for img_index, img in enumerate(page.get_images(full=True)): + rect = page.get_image_bbox(img) + images.append(f"Image {img_index + 1}: bbox {rect}") + + page_content = f"Page {page_num + 1}:\n" + page_content += f"Text:\n{text}\n" + page_content += "Images:\n" + "\n".join(images) + "\n" + + if not text and ocr_enabled: + mat = fitz.Matrix(2, 2) + pix = page.get_pixmap(matrix=mat) + img = np.frombuffer(pix.samples, dtype=np.uint8).reshape( + pix.height, pix.width, 3 + ) + + ocr_result = ocr.ocr(img, cls=True) + page_content += "OCR Results:\n" + for line in ocr_result[0]: + bbox, (text, _) = line + page_content += f"{bbox}, {text}\n" + + pdf_content.append(page_content) + + if not doc_per_page: + return ["\n\n".join(pdf_content)] + + return pdf_content + + # Define a dictionary mapping function names to their corresponding functions PARSING_TOOLS = { "whisper_speech_to_text": whisper_speech_to_text, @@ -320,4 +385,5 @@ def azure_di_read( "docx_to_string": docx_to_string, "pptx_to_string": pptx_to_string, "azure_di_read": azure_di_read, + "paddleocr_pdf_to_string": paddleocr_pdf_to_string, } diff --git a/docs/examples/custom-parsing.md b/docs/examples/custom-parsing.md index 431653b4..8028dbe4 100644 --- a/docs/examples/custom-parsing.md +++ b/docs/examples/custom-parsing.md @@ -33,16 +33,35 @@ To use custom parsing, you need to define parsing tools in your DocETL configura ```yaml parsing_tools: - - name: ocr_parser + - name: top_products_report function_code: | - import pytesseract - from pdf2image import convert_from_path - def ocr_parser(filename: str) -> List[str]: - images = convert_from_path(filename) - text = "" - for image in images: - text += pytesseract.image_to_string(image) - return [text] + def top_products_report(filename: str) -> List[str]: + import pandas as pd + + # Read the Excel file + df = pd.read_excel(filename) + + # Calculate total sales + total_sales = df['Sales'].sum() + + # Find top 500 products by sales + top_products = df.groupby('Product')['Sales'].sum().nlargest(500) + + # Calculate month-over-month growth + df['Date'] = pd.to_datetime(df['Date']) + monthly_sales = df.groupby(df['Date'].dt.to_period('M'))['Sales'].sum() + mom_growth = monthly_sales.pct_change().fillna(0) + + # Prepare the analysis report + report = [ + f"Total Sales: ${total_sales:,.2f}", + "\nTop 500 Products by Sales:", + top_products.to_string(), + "\nMonth-over-Month Growth:", + mom_growth.to_string() + ] + + return ["\n".join(report)] datasets: sales_reports: @@ -51,10 +70,8 @@ datasets: path: "sales_data/sales_paths.json" parsing: - input_key: excel_path - function: xlsx_to_string - output_key: sales_data - function_kwargs: - orientation: "col" + function: top_products_report + output_key: sales_analysis receipts: type: file @@ -62,14 +79,17 @@ datasets: path: "receipts/receipt_paths.json" parsing: - input_key: pdf_path - function: ocr_parser + function: paddleocr_pdf_to_string output_key: receipt_text + function_kwargs: + ocr_enabled: true + lang: "en" ``` In this configuration: -- We define a custom `ocr_parser` for PDF files. -- We use the built-in `xlsx_to_string` parser for Excel files. +- We define a custom `top_products_report` function for Excel files. +- We use the built-in `paddleocr_pdf_to_string` parser for PDF files. - We apply these parsing tools to the external files referenced in the respective datasets. ### 2. Pipeline Integration @@ -145,48 +165,55 @@ When you run your DocETL pipeline, the parsing tools you've specified in your co Let's look at how this works for our earlier examples: -### Excel Files (using xlsx_to_string) +### Excel Files (using top_products_report) For an Excel file like "sales_data/january_sales.xlsx": -1. The `xlsx_to_string` function reads the Excel file. -2. It converts the data to a string representation. -3. The output might look like this: +- The top_products_report function reads the Excel file. +- It processes the sales data and generates a report of top-selling products. +- The output might look like this: -``` -Date: -2023-01-01 -2023-01-02 -... - -Product: -Widget A -Widget B -... - -Amount: -100 -150 -... +```markdown +Top Products Report - January 2023 + +1. Widget A - 1500 units sold +2. Gadget B - 1200 units sold +3. Gizmo C - 950 units sold +4. Doohickey D - 800 units sold +5. Thingamajig E - 650 units sold + ... + +Total Revenue: $245,000 +Best Selling Category: Electronics ``` -### PDF Files (using ocr_parser) +### PDF Files (using paddleocr_pdf_to_string) For a PDF file like "receipts/receipt001.pdf": -1. The `ocr_parser` function converts each page of the PDF to an image. -2. It applies OCR to each image. -3. The function combines the text from all pages. -4. The output might look like this: +- The paddleocr_pdf_to_string function reads the PDF file. +- It uses PaddleOCR to perform optical character recognition on each page. +- The function combines the extracted text from all pages into a single string. + The output might look like this: -``` +```markdown RECEIPT Store: Example Store Date: 2023-05-15 Items: + 1. Product A - $10.99 2. Product B - $15.50 -Total: $26.49 +3. Product C - $7.25 +4. Product D - $22.00 + Subtotal: $55.74 + Tax (8%): $4.46 + Total: $60.20 + +Payment Method: Credit Card +Card Number: \***\* \*\*** \*\*\*\* 1234 + +Thank you for your purchase! ``` This parsed and formatted data is then passed to the respective operations in your pipeline for further processing. @@ -242,13 +269,17 @@ DocETL provides several built-in parsing tools to handle common file formats and options: show_root_heading: true heading_level: 3 - ::: docetl.parsing_tools.azure_di_read options: heading_level: 3 show_root_heading: true +::: docetl.parsing_tools.paddleocr_pdf_to_string + options: + heading_level: 3 + show_root_heading: true + ### Using Function Arguments with Parsing Tools When using parsing tools in your DocETL configuration, you can pass additional arguments to the parsing functions using the function_kwargs field. This allows you to customize the behavior of the parsing tools without modifying their implementation. diff --git a/poetry.lock b/poetry.lock index 15248ad9..2eee201e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -187,6 +187,17 @@ dev = ["build", "twine"] doc = ["Sphinx"] test = ["coverage", "pytest", "pytest-cov"] +[[package]] +name = "astor" +version = "0.8.1" +description = "Read/rewrite/write Python ASTs" +optional = true +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" +files = [ + {file = "astor-0.8.1-py2.py3-none-any.whl", hash = "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5"}, + {file = "astor-0.8.1.tar.gz", hash = "sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e"}, +] + [[package]] name = "async-timeout" version = "4.0.3" @@ -433,6 +444,191 @@ files = [ {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] +[[package]] +name = "contourpy" +version = 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os.path.exists(file_path): os.remove(file_path) os.unlink(file_path) + + +def test_paddleocr_pdf_to_string(): + pdf_path = "tests/data/PublicWaterMassMailing.pdf" + result = parsing_tools.paddleocr_pdf_to_string(pdf_path, lang="en") + + assert isinstance(result, list) + assert len(result) == 1 + + assert "have received the new bottles, please discard" in result[0] From 4fc27cbd0adbf35016a1c5269b3f8d34e0056996 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 2 Oct 2024 10:11:35 -0700 Subject: [PATCH 2/2] feat: add paddleocr Co-authored-by: staru09 --- poetry.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/poetry.lock b/poetry.lock index 2eee201e..2f7088cc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2967,7 +2967,7 @@ extra = ["pygments (>=2.12)"] name = "pymupdf" version = "1.24.10" description = "A high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents." -optional = false +optional = true python-versions = ">=3.8" files = [ {file = "PyMuPDF-1.24.10-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:a320d44a96f0cc69364c4d7384205fdae57a511edb07e68627108546cfd3ca7d"}, @@ -3015,7 +3015,7 @@ PyMuPDFb = "1.24.10" name = "pymupdfb" version = "1.24.10" description = "MuPDF shared libraries for PyMuPDF." -optional = false +optional = true python-versions = ">=3.8" files = [ {file = "PyMuPDFb-1.24.10-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:cd6b24630d90dce9ab3e59d06c5e616686f8d7ec626be1311721fcb062aa0078"}, @@ -4399,9 +4399,9 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", type = ["pytest-mypy"] [extras] -parsing = ["azure-ai-documentintelligence", "openpyxl", "paddleocr", "paddlepaddle", "pydub", "python-docx", "python-pptx"] +parsing = ["azure-ai-documentintelligence", "openpyxl", "paddleocr", "paddlepaddle", "pydub", "pymupdf", "python-docx", "python-pptx"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "9abd6b035c52228c74d6dac68b87788d0da6e66f4194b904b09048f336c5cdd2" +content-hash = "e4bfeb00f95deb98e5c313266c57e361d7090d2591bfa3d6aa004988243dab19"

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