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texttablescoop's Introduction

๐Ÿ“ TextTableScoop

Welcome to TextTableScoop ๐ŸŒŸ, a versatile tool designed for extracting text from files and CSV tables, particularly focusing on Office files like Excel, PowerPoint, etc. This project is part of a 'ProjectText' suite that includes ProjectTextAgent and ProjectDataBaseQnA.

๐Ÿš€ Features

  • Specializes in extracting text from various file formats, including Office files.
  • Designed to work in both Windows with COM and Linux with LibreOffice + PyUNO.
  • Current implementation supports Linux + LibreOffice + PyUNO.
  • Windows support with COM environment is planned for robust file handling.

๐Ÿ“ฅ Installation

To install TextTableScoop, use the following pip command:

pip3 install git+https://github.com/Flagro/TextTableScoop.git

๐Ÿ› ๏ธ Usage

Run texttablescoop from the bin folder with these arguments:

  1. path: Path to the file or directory to process.
  2. -t or --temp: (Optional) Path to a custom temporary folder.
  3. -p or --project: (Optional) Path to the project folder the file belongs to.
  4. --ignore: (Optional) Comma-separated list of patterns to ignore.

๐Ÿ–ฅ๏ธ Example Command

texttablescoop 'path/to/file' --temp 'path/to/temp' --project 'path/to/project' --ignore 'pattern1,pattern2'

๐Ÿค Collaboration & Issues

Open for collaboration; check the issues page for discussions.

Here's how you can contribute:

  1. Fork the Project.
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
  3. Commit your Changes (git commit -m 'Add some AmazingFeature').
  4. Push to the Branch (git push origin feature/AmazingFeature).
  5. Open a Pull Request.

texttablescoop's People

Contributors

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Stargazers

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Watchers

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texttablescoop's Issues

Different strategies for temporary files needed

Since project file usually contain sensitive information it's reasonable to not copy them into standart tmp folder of the OS but to provide interfaces for the user to specify folder to store the temporary files.

This is getting complicated as there are two possible ways to use this tool:

  1. Agent running in a docker container (for actively supporting a database of text for the project)
  2. Binary script to just dump all the data into text

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