confectionary 0.1.3

A tool to quickly create sweet PDF files from text files.

PDF Confectionary :cupcake:

Do you work in the NLP domain and find that your end users/clients are not thrilled by receiving a ton of .txt files with your amazing results? Look no further, you are at the right place!

PDF Confectionary is a tool for quickly creating templated PDFs from text files using FPDF2. Go create some sweet PDFs.

About

The primary focus of this repo is to provide a simple, easy to use, and extensible PDF creation tool. The initial use case for this project was to convert transcribed speech to a PDF for reading & review, such as in the vid2cleantxt project. Relevant features in PDF Confectionary include:

• Automatic paragraph separation
• TOC generation & links to TOC entries on each page (click on footer)
• Keyword extraction for each txt file

PDF Confectionary was originally designed to be used as a command line tool, but it can also be used as an installable Python module.

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Installation

Requirements

• Requirements are listed in the requirements.txt file.
• the primary requirements are: FPDF2, textsplit, and clean-text.

Package Installation

The package can be installed using pip:

pip install confectionary

To install as a python package without pip, run:

1. git clone <https://github.com/pszemraj/confectionary.git>
2. pip install -r requirements.txt
3. pip install .

Usage

There are two ways to use PDF Confectionary:

1. command line, via python confectionary/text2pdf.py

2. as a python module, via the dir_to_pdf function.

This will create one pdf from all txt files in the input directory and save it to the output directory. Add the -r switch (or recurse=True in function) to load files recursively.

Command Line Usage

• call python confectionary/text2pdf.py -i /path/to/input/dir -o /path/to/output/dir to create a pdf from all txt files in the input directory and save it to the output directory:

python confectionary/text2pdf.py -i "./example/text-files" -o "./example/outputs"

• console output is below (in the next section), the result file is in the output directory example\outputs.
• additional options can be found in the text2pdf.py file or by passing the -h switch.

Basic Usage within Python

• python
• import confectionary
• confectionary.text2pdf.dir_to_pdf("./example/text-files", "./example/outputs", key_phrase="test")

Resulting output is in example\outputs:

3 files found matching extension .txt

# entries is 3, < title thresh 39
will use one page for TOC

Building Chapters in PDF file: 100%|█| 3/3 [00
100%|█████████| 3/3 [00:00<00:00, 3010.27it/s]

PDF file saved to C:\Users\peter\code-dev-22\misc-repos\text2pdf\example\outputs\pdf_from_txt_Feb-15-2022\text-files_txt2pdf_Feb-15-2022_standard.pdf

• see the dir_to_pdf docstring for more details.

Note

• The word2vec model is quite big, 3.7GB. It will be downloaded if it doesn't exist, and the model will be saved to the ./models directory.
• The word2vec mode is not required for the text2pdf tool, and can be disabled by setting the do_paragraph_splitting parameter to False or in command line mode, by adding the --no-split switch.

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TODO list

• convert the text2pdf.py script to a module/function
• publish to PyPI IN-PROGRESS
• improve TOC calculation beyond a simple title threshold
• add alternate, smaller, word2vec models for splitting paragraphs
• Add a basic notebook demo

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License

Apache License 2.0

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Contributing

• Given the open-ended nature of documentation creation, there are a lot of features that are not yet implemented. Please feel free to contribute!
• Developers can contribute to this project by submitting pull requests in this repo - see details in the contributing guide.

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