Features

🛡️ Multi-Layered Content Moderation: Automatically detects AI-generated, violent, and explicit content in images and videos.

⚡ Fast Local Inference: Runs a lightweight computer vision model entirely on-device using ONNX Runtime. It operates with zero network latency and completely avoids pulling in massive deep-learning dependencies like PyTorch.

🏢 Seamless Production Testing: Designed to perfectly mirror the cloud API's data structures. This allows enterprise teams to easily build and test their integration logic locally before routing production traffic to the cloud API.

🧑‍💻 Instant Open-Source Baseline: Provides open-source developers and hobbyists with a free, immediately usable baseline classifier. Get a foundational media safety layer up and running in minutes, not days.

Installation

Recommended Way

We highly recommend using this package as an optional extension of the main Guard client. This provides a single, unified API for both cloud and local detection.

pip install guard-client[local]

Standalone Way

pip install guard-local-detector

Quick Start

Using the Unified Client (Recommended)

If you installed via guard-client[local], you do not need to import this package directly. Ask the client for the local engine and it routes every call here — no API key, and no network access.

from guard_client import GuardClient

with GuardClient(engine="local") as client:
    result = client.analyze("/local/paths/to/video.mp4")

    for item in result.results:
        print(f"{item.label}: {item.score}")  # AI-Generated: 71

The results carry the same labels, task ids, and 0-100 scores a cloud run returns, so the same code works against either engine. A cloud-configured client can also send a single call locally with client.analyze(source, engine="local").

Using the Standalone Engine

The engine works on bytes and a MIME type, never a path — reading the source is the caller's job.

import guard_local

engine = guard_local.LocalDetectorEngine()

with open("/local/paths/to/image.png", "rb") as handle:
    results = engine.analyze(handle.read(), "image/png")
    
for item in results:
    print(f"{item['label']}: {item['score']:.2f}")

# AI-Generated: 0.90
# Violence: 0.02
# Explicit: 0.01

Development

This project uses uv for lightning-fast Python package and environment management.

Prerequisites

• uv (already installed on your system)

Setup

1. Clone the repository:

   git clone https://github.com/elhio/guard-local-python.git
   cd guard-local-python
   

2. Sync the environment:

   uv sync
   

   This command automatically creates a .venv virtual environment, reads the uv.lock file, and installs all core and development dependencies exactly as they were locked.

3. Run tests:

   uv run pytest
   

4. Formatting, linting, and type checking:

   uv run ruff format
   uv run ruff check
   uv run mypy
   

5. Build for production:

   uv build
   

Contributing

We welcome contributions! Please note that all contributors must sign our automated CLA. Read more in our Contributing Guide.

License

This repository and its corresponding PyPI package are licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) - see the LICENSE file for details.