Metadata-Version: 2.1
Name: unstructured-inference
Version: 0.2.4
Summary: A library for performing inference using trained models.
Home-page: https://github.com/Unstructured-IO/unstructured-inference
Author: Unstructured Technologies
Author-email: devops@unstructuredai.io
License: Apache-2.0
Description: <h3 align="center">
          <img
            src="https://raw.githubusercontent.com/Unstructured-IO/unstructured/main/img/unstructured_logo.png"
            height="200"
          >
        
        </h3>
        
        <h3 align="center">
          <p>Open-Source Pre-Processing Tools for Unstructured Data</p>
        </h3>
        
        The `unstructured-inference` repo contains hosted model inference code for layout parsing models. 
        These models are invoked via API as part of the partitioning bricks in the `unstructured` package.
        
        ## Installation
        
        ### Package
        
        Run `pip install unstructured-inference`.
        
        ### Detectron2
        
        [Detectron2](https://github.com/facebookresearch/detectron2) is required for most inference tasks 
        but is not automatically installed with this package. 
        For MacOS and Linux, build from source with:
        ```shell
        pip install 'git+https://github.com/facebookresearch/detectron2.git@v0.4#egg=detectron2'
        ```
        Other install options can be found in the 
        [Detectron2 installation guide](https://detectron2.readthedocs.io/en/latest/tutorials/install.html).
        
        Windows is not officially supported by Detectron2, but some users are able to install it anyway. 
        See discussion [here](https://layout-parser.github.io/tutorials/installation#for-windows-users) for 
        tips on installing Detectron2 on Windows.
        
        ### Repository
        
        To install the repository for development, clone the repo and run `make install` to install dependencies.
        Run `make help` for a full list of install options.
        
        ## Getting Started
        
        To get started with the layout parsing model, use the following commands:
        
        ```python
        from unstructured_inference.inference.layout import DocumentLayout
        
        layout = DocumentLayout.from_file("sample-docs/loremipsum.pdf")
        
        print(layout.pages[0].elements)
        ```
        
        Once the model has detected the layout and OCR'd the document, the text extracted from the first 
        page of the sample document will be displayed.
        You can convert a given element to a `dict` by running the `.to_dict()` method.
        
        To build the Docker container, run `make docker-build`. Note that Apple hardware with an M1 chip 
        has trouble building `Detectron2` on Docker and for best results you should build it on Linux. To 
        run the API locally, use `make start-app-local`. You can stop the API with `make stop-app-local`. 
        The API will run at `http:/localhost:5000`. 
        You can then `POST` a PDF file to the API endpoint to see its layout with the command:
        ```
        curl -X 'POST' 'http://localhost:5000/layout/pdf' -F 'file=@<your_pdf_file>' | jq -C . | less -R
        ```
        
        You can also choose the types of elements you want to return from the output of PDF parsing by 
        passing a list of types to the `include_elems` parameter. For example, if you only want to return 
        `Text` elements and `Title` elements, you can curl:
        ```
        curl -X 'POST' 'http://localhost:5000/layout/pdf' \
        -F 'file=@<your_pdf_file>' \
        -F include_elems=Text \
        -F include_elems=Title \
         | jq -C | less -R
        ```
        If you are using an Apple M1 chip, use `make run-app-dev` instead of `make start-app-local` to 
        start the API with hot reloading. The API will run at `http:/localhost:8000`.
        
        View the swagger documentation at `http://localhost:5000/docs`.
        ## Security Policy
        
        See our [security policy](https://github.com/Unstructured-IO/unstructured-inference/security/policy) for
        information on how to report security vulnerabilities.
        
        ## Learn more
        
        | Section | Description |
        |-|-|
        | [Unstructured Community Github](https://github.com/Unstructured-IO/community) | Information about Unstructured.io community projects  |
        | [Unstructured Github](https://github.com/Unstructured-IO) | Unstructured.io open source repositories |
        | [Company Website](https://unstructured.io) | Unstructured.io product and company info |
        
Keywords: NLP PDF HTML CV XML parsing preprocessing
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
