# Document Transformer
Document Transformer allows users to define and apply transformations to documents in a flexible and robust manner, ensuring traceability of each change made to the documents.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Features
- Flexible document transformation
- Comprehensive traceability for each transformation
- Add custom supports to multiple document formats (e.g., JSON, XML, CSV)
- Easy integration with other tools and workflows
## Installation
To install Document Transformer, follow these steps:
```sh
# Install using pip
pip install document-transformer
```
## Usage
Define custom Document class
```python
from document_transformer import Document
class PDFDocument(Document):
"""Custom class to PDF Documents"""
class ImageDocument(Document):
"""Custom class to Image Documents"""
def saver(self, path):
self.data.save(path)
return self
```
Define the transformer. Specify input and output Document types
```python
from document_transformer import DocumentTransformer
import pdf2image # install: pip install pdf2image
from typing import List
from pathlib import Path
class PDF2Images(DocumentTransformer):
input: PDFDocument = None
output: List[ImageDocument] = []
def transformer(self) -> List[ImageDocument]:
"""Split the PDF document into pages"""
images = pdf2image.convert_from_path(self.input.path)
return [
ImageDocument(
metadata={'pdf_path': Path(self.input.path).name, 'page': i+1, 'size': image.size},
data=image,
)
for i, image in enumerate(images)
]
```
Run your implementation
```python
pdf_doc = PDFDocument(path="document.pdf")
images = PDF2Images(input=pdf_doc).run()
for image in images:
image.save(path=f'images/pag_{image.metadata["page"]}.jpg')
print(f"Imagen: {image.id}")
print(f"Parents: {image.parents}")
print(f"Metadata: {image.metadata}")
```
Or run like a pipeline, visualize the graph transformation
```python
from document_transformer import Pipeline
from document_transformer.utils import plot_graph
# Define Pipeline, add more transformers as you need
pipeline = Pipeline(transformers=[
PDF2Images(to="images/pag_{metadata[page]}.jpg"),
# Images2Markdown(to="images/pag_{metadata[page]}.md")),
# ...
])
# Define input and get output
pdf_doc = PDFDocument(path="document.pdf")
images = pipeline.run(input=pdf_doc)
# See transfomer plot graph
plot_graph(pipeline.get_traces())
```
## Contributing
We welcome contributions! Please read our Contributing Guide to learn how you can help.
## License
Document Transformer is licensed under the MIT License
## Contact
If you have any questions or feedback, please feel free to reach out to us at johngonzalezv@gmail.com.
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"description": "# Document Transformer\n\nDocument Transformer allows users to define and apply transformations to documents in a flexible and robust manner, ensuring traceability of each change made to the documents.\n\n## Table of Contents\n\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n- [Contact](#contact)\n\n## Features\n\n- Flexible document transformation\n- Comprehensive traceability for each transformation\n- Add custom supports to multiple document formats (e.g., JSON, XML, CSV)\n- Easy integration with other tools and workflows\n\n## Installation\n\nTo install Document Transformer, follow these steps:\n\n```sh\n# Install using pip\npip install document-transformer\n```\n\n## Usage\n\nDefine custom Document class\n\n```python\nfrom document_transformer import Document\n\nclass PDFDocument(Document):\n \"\"\"Custom class to PDF Documents\"\"\"\n\nclass ImageDocument(Document):\n \"\"\"Custom class to Image Documents\"\"\"\n def saver(self, path):\n self.data.save(path)\n return self\n```\n\nDefine the transformer. Specify input and output Document types\n\n```python\nfrom document_transformer import DocumentTransformer\nimport pdf2image # install: pip install pdf2image\nfrom typing import List\nfrom pathlib import Path\n\nclass PDF2Images(DocumentTransformer):\n input: PDFDocument = None\n output: List[ImageDocument] = []\n\n def transformer(self) -> List[ImageDocument]:\n \"\"\"Split the PDF document into pages\"\"\"\n images = pdf2image.convert_from_path(self.input.path)\n return [\n ImageDocument(\n metadata={'pdf_path': Path(self.input.path).name, 'page': i+1, 'size': image.size},\n data=image,\n )\n for i, image in enumerate(images)\n ]\n```\n\nRun your implementation\n\n```python\npdf_doc = PDFDocument(path=\"document.pdf\")\nimages = PDF2Images(input=pdf_doc).run()\n\nfor image in images:\n image.save(path=f'images/pag_{image.metadata[\"page\"]}.jpg')\n print(f\"Imagen: {image.id}\")\n print(f\"Parents: {image.parents}\")\n print(f\"Metadata: {image.metadata}\")\n```\n\nOr run like a pipeline, visualize the graph transformation\n\n```python\nfrom document_transformer import Pipeline\nfrom document_transformer.utils import plot_graph\n\n# Define Pipeline, add more transformers as you need\npipeline = Pipeline(transformers=[\n PDF2Images(to=\"images/pag_{metadata[page]}.jpg\"),\n # Images2Markdown(to=\"images/pag_{metadata[page]}.md\")),\n # ...\n])\n\n# Define input and get output\npdf_doc = PDFDocument(path=\"document.pdf\")\nimages = pipeline.run(input=pdf_doc)\n\n# See transfomer plot graph\nplot_graph(pipeline.get_traces())\n```\n\n\n\n## Contributing\nWe welcome contributions! Please read our Contributing Guide to learn how you can help.\n\n## License\nDocument Transformer is licensed under the MIT License\n\n## Contact\nIf you have any questions or feedback, please feel free to reach out to us at johngonzalezv@gmail.com.\n",
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