This library enables Large Language Models (LLMs) to interact with thousands of Gradio applications hosted on Hugging Face Spaces, transforming them into callable tools for LLM agents. It allows agents to perform complex tasks by chaining together various Gradio-powered functionalities, such as image generation, transcription, and text-to-speech.

How It Works

The core of the library is the GradioTool class, which abstracts the process of converting a Gradio app into an LLM-usable tool. Developers define a tool by implementing a standard interface that includes a name, a precise description for the LLM, and the URL or Space ID of the Gradio application. The library uses the gradio_client to interact with the remote Gradio app, abstracting the API calls and data processing.

Quick Start & Requirements

• Install via pip: pip install gradio-tools
• Requires Python 3.7+
• Example usage with LangChain is provided in the README.
• Official documentation and examples are available in the repository.

Highlighted Details

• Integrates with LangChain and MiniChain for LLM agent frameworks.
• Includes pre-built tools for Stable Diffusion, image captioning, text-to-video, audio transcription (Whisper), and text-to-speech (Bark).
• Supports creating custom tools by implementing the GradioTool interface.
• Enables LLMs to perform multi-step tasks by chaining Gradio tools.

Maintenance & Community

• The project welcomes contributions for new tools.
• Links to example usage and the underlying Gradio client library are provided.

Licensing & Compatibility

• The library appears to be under an unspecified license, but its dependencies (like Gradio) are typically Apache 2.0. Compatibility for commercial use should be verified.

Limitations & Caveats

The library relies on the availability and stability of remote Hugging Face Spaces. The effectiveness of LLM agents using these tools is highly dependent on the quality of the tool descriptions provided to the LLM.