Textboost

A bionic reading tool that leverages the power of machine learning to enhance the reading experience and organization, particularly for individuals with an inability to focus.

How TextBoost Harnesses Machine Learning

• Folder Management: The model can create subsections such as modified/food, modified/space, modified/politics, and more and store the respected pdf within their folder.
• Text Summarization: Textboost provides the capability to summarize the text within a given PDF. Optionally, the user can turn on this feature and the summarization will appear at the last page of the PDF.

❗PLEASE NOTE THE FOLLOWING❗
Please note that this tool supports simple text extraction from PDFs. Complex elements like tables and code blocks will not be rendered properly.
The updated PDF will be automatically placed in appropriate folders based on the text context. You can find your modified file in a folder similar to ./modified/space/your_file.py.
Don't forget to check your current directory for the outputted file.

Usage

1. Through Github

• Install Python.
• Git clone this repository by running the command https://github.com/boushrabettir/textboost.git
• Move to the textboost directory by running cd ./textboost
• Pip install requirements by running pip install -r requirements.txt
• Convert your PDF to a Markdown using this tool.
• Place your modified Markdown file in the pre-modified folder.
• Run the script by typing python ./main.py

2. Through Pypi

• Install Python.
• Convert your PDF to a Markdown using this tool.
• Place your modified Markdown file in the pre-modified folder.
• Run the script by typing textboost

Key Features

• Bold formatting to emphasize specific letters within words.
• Organized file locations for modified files generated by a model
• Optional text summarization generated by the model

Demo

instruction.mp4

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Made with 🐱💛 by @boushrabettir