Decart Python SDK

A Python SDK for Decart's models.

Installation

Using UV

uv add decart

Using pip

pip install decart

Documentation

For complete documentation, guides, and examples, visit: https://docs.platform.decart.ai/sdks/python

Quick Start

Image Editing (Process API)

import asyncio
import os
from decart import DecartClient, models

async def main():
    async with DecartClient(api_key=os.getenv("DECART_API_KEY")) as client:
        # Edit an image
        result = await client.process({
            "model": models.image("lucy-image-2"),
            "prompt": "Apply a painterly oil-on-canvas look while preserving the composition",
            "data": open("input.png", "rb"),
        })

        with open("output.png", "wb") as f:
            f.write(result)

asyncio.run(main())

Video Editing (Queue API)

For video editing jobs, use the queue API to submit jobs and poll for results:

async with DecartClient(api_key=os.getenv("DECART_API_KEY")) as client:
    # Submit and poll automatically
    result = await client.queue.submit_and_poll({
        "model": models.video("lucy-clip"),
        "prompt": "Restyle this footage with anime shading and vibrant neon highlights",
        "data": open("input.mp4", "rb"),
        "on_status_change": lambda job: print(f"Status: {job.status}"),
    })

    if result.status == "completed":
        with open("output.mp4", "wb") as f:
            f.write(result.data)
    else:
        print(f"Job failed: {result.error}")

Or manage the polling manually:

async with DecartClient(api_key=os.getenv("DECART_API_KEY")) as client:
    # Submit the job
    job = await client.queue.submit({
        "model": models.video("lucy-clip"),
        "prompt": "Add cinematic teal-and-orange grading and gentle film grain",
        "data": open("input.mp4", "rb"),
    })
    print(f"Job ID: {job.job_id}")

    # Poll for status
    status = await client.queue.status(job.job_id)
    print(f"Status: {status.status}")

    # Get result when completed
    if status.status == "completed":
        data = await client.queue.result(job.job_id)
        with open("output.mp4", "wb") as f:
            f.write(data)

Development

Setup with UV

# Clone the repository
git clone https://github.com/decartai/decart-python
cd decart-python

# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install all dependencies (including dev dependencies)
uv sync --all-extras

# Run tests
uv run pytest

# Run linting
uv run ruff check decart/ tests/ examples/

# Format code
uv run black decart/ tests/ examples/

# Type check
uv run mypy decart/

Common Commands

# Install dependencies
uv sync --all-extras

# Run tests with coverage
uv run pytest --cov=decart --cov-report=html

# Run examples
uv run python examples/process_video.py
uv run python examples/realtime_synthetic.py

# Update dependencies
uv lock --upgrade

Test UI

The SDK includes an interactive test UI built with Gradio for quickly testing all SDK features without writing code.

# Install Gradio
pip install gradio

# Run the test UI
python test_ui.py

Then open http://localhost:7860 in your browser.

The UI provides tabs for:

Image Editing - Image-to-image edits
Video Editing - Video-to-video edits
Video Restyle - Restyle videos using text prompts or reference images
Tokens - Create short-lived client tokens

Enter your API key at the top of the interface to start testing.

Publishing a New Version

The package is automatically published to PyPI when you create a GitHub release.

Automated Release

Use the release script to automate the entire process:

python release.py

The script will:

Display the current version
Prompt for the new version
Update pyproject.toml
Commit and push changes
Create a GitHub release with release notes

The GitHub Actions workflow will automatically build, test, and publish to PyPI.

License

MIT

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Provenance

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Statement:
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- Permalink: DecartAI/decart-python@7a8faa3bdf782b5e1c30c2d5134283d01c64bbd5
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