codesum 0.1.1

Interactive code summarizer using AI and TUI

Code Summarizer for LLM Interaction

Project Description 📝

This tool generates concise code summaries optimized for Large Language Models (LLMs). It uses OpenAI's GPT-4 to create project overviews, making it easier for LLMs to understand and work with your codebase. Features include interactive file selection, summary caching, and automatic README generation.

Video Tutorial

Key Features 🔑

• Curses-based interactive file selection
• Intelligent summary caching
• Respects .gitignore rules
• Generates detailed and compressed summaries
• Creates AI-generated README.md
• Command-line argument support

Installation 🛠️

You can set up this project using either the automated setup script or manual installation.

Option 1: Automated Setup (Recommended)

1. Clone the repository
2. Navigate to the project directory
3. Run the setup script:

   python setup.py
   

   This script will:
   • Create a virtual environment
   • Install dependencies
   • Set up the .env file
   • Create an alias for easy usage

Option 2: Manual Setup

1. Clone the repository
2. Navigate to the project directory
3. Set up the Python environment:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   

4. Create a .env file with your OpenAI API key:

   OPENAI_API_KEY='your-api-key-here'
   LLM_MODEL='gpt-4'
   

5. Optionally, run codesum.sh for automated setup

Usage 🚀

Python script:

python app.py [--infer]

Bash script:

chmod +x codesum.sh
./codesum.sh [arguments]

Using the alias (if set up):

codesum [arguments]

The --infer flag enables OpenAI API calls for summaries and README generation.

Output 📂

When run, the script produces the following:

1. Local Code Summary:

   • File: .summary_files/code_summary.md
   • Contents: A comprehensive summary of all selected files, including their full content and a tree structure of the project.
   • The contents of this file will automatically be sent to the system clipboard.

2. Compressed Code Summary (optional):

   • File: .summary_files/compressed_code_summary.md
   • Contents: A condensed version of the code summary, including AI-generated summaries for each file and the project structure.

3. Updated README.md (optional):

   • File: README.md in the project root
   • Contents: An AI-generated README file based on the compressed code summary, including sections like Project Description, Installation, Usage, and more.

4. Metadata Files:

   • Location: .summary_files/[file_path]_metadata.json for each summarized file
   • Contents: JSON files containing the hash of the original file and its generated summary, used for caching purposes.

5. Previous Selection File:

   • File: .summary_files/previous_selection.json
   • Contents: A JSON file storing the list of previously selected files for future reference.

These outputs provide a comprehensive overview of your project, facilitate efficient interaction with LLMs, and streamline future summarization processes.

Dependencies 📚

• openai
• itsprompt
• pathspec
• python-dotenv
• keyboard
• curses (built-in)

Acknowledgements 🙌

Thanks to all contributors and library creators.

License 📜

Apache 2.0