llm_check

This script uses an Ollama model to analyze and determine the relevance of text files from a specified folder to three key topics:

• Coach Carter (2005 movie)
• The Oscars Slap (Will Smith and Chris Rock, 2022)
• The Trump–Zelenskyy White House Meeting (2022)

The script reads .txt files, analyzes their content, and provides a relevance percentage for each of the three topics. It uses a small, performant Ollama model (like mistral or llama2:7b-chat) to evaluate the content.

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Table of Contents

• Installation
• Usage
• How It Works
• Arguments
• Example Output
• Contributing
• License

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Installation

Before you can use the script, you will need to install the necessary dependencies and set up Ollama.

Prerequisites

1. Ollama - An AI model serving platform. You can download and install it here.
2. Python 3.8+ - Make sure Python is installed and available in your PATH.

Install Dependencies

Clone this repository and install the required Python packages:

git clone https://github.com/FakeProfileDetection/llm_check.git
cd llm_check
ollama pull mistral
pip install -r requirements.txt

Usage

The script requires a folder containing .txt files. It will analyze each file and output the relevance percentage for each topic.

Command-line Usage

python llm_check.py <folder_path> [--model <model_name>]

• <folder_path>: Path to the folder containing .txt files you want to analyze.
• [--model <model_name>]: Optional argument specifying the Ollama model to use. Default is mistral

Example Output

Here’s an example of what the output might look like:

{
  "Coach Carter": 12,
  "Oscars Slap": 88,
  "Trump-Ukraine Meeting": 5
}

This means that the file has:

• 12% relevance to Coach Carter.
• 88% relevance to The Oscars slap.
• 5% relevance to the Trump–Zelenskyy White House meeting.