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TableMutant

TableMutant lets you generate new columns in datasets using LLMs. It provides both a CLI and a Toga-based GUI.

Installation

Requires Python 3.12+.

Using uv (recommended):

uv sync

Or using pip:

pip install -e .

Running

CLI:

tablemutant --model TheBloke/Llama-2-7B-GGUF/llama-2-7b.Q4_K_M.gguf \
            --table data.csv \
            --instructions "Summarize the content in columns 0 and 1"

GUI:

tablemutant --gui

Briefcase packaging

This project is configured for Briefcase packaging via [tool.briefcase] in the pyproject. Reference: https://briefcase.readthedocs.io/en/stable/

Install Briefcase

Using uv:

uv add --dev briefcase

Or pip:

pip install briefcase

Dev run

Run your app with a managed, editable development bundle:

briefcase dev

Create, build, package

Create application scaffolding for your platform:

briefcase create

Build an app bundle:

briefcase build

Generate a distributable installer:

briefcase package

Notes:

  • Some platforms require SDKs/tools (e.g., Xcode for macOS/iOS, Android SDK for Android). See Briefcase docs.
  • First runs may download toolchains; allow time and follow prompts.

App icons (Toga / Briefcase)

App icon locations are configured in [tool.briefcase.app.tablemutant] and per-platform sections in toml.table().

Icon folders:

  • resources/icons/tablemutant/ -> generic fallback (icon.png)
  • resources/icons/macos/ -> macOS icon.icns
  • resources/icons/windows/ -> Windows icon.ico
  • resources/icons/linux/ -> Linux icon.png
  • resources/icons/ios/ -> placeholder
  • resources/icons/android/ -> placeholder

Quick-start with placeholders:

  1. Place a square high-res PNG at repo root as logo.png
  2. Populate placeholders:
python resources/icons_setup.py
  1. Replace generated placeholders with proper per-platform assets when ready.

For detailed Toga app icon guidance, see:

Entry point

Briefcase runs the application via:

  • entry_point = "tablemutant.main:main"

Main entry is defined in python.main().

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TableMutant lets you generate new columns in datasets using LLMs.

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