Python Code Executor

Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.

!Python Code Executor

Quick Start

curl -fsSL https://cli.inference.sh | sh && infsh login

# Run Python code
infsh app run infsh/python-executor --input '{
  "code": "import pandas as pdnprint(pd.__version__)"
}'

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

App Details

Property	Value
App ID	infsh/python-executor
Environment	Python 3.10, CPU-only
RAM	8GB (default) / 16GB (high_memory)
Timeout	1-300 seconds (default: 30)

Input Schema

{
  "code": "print('Hello World!')",
  "timeout": 30,
  "capture_output": true,
  "working_dir": null
}

Pre-installed Libraries

Web Scraping & HTTP

• requests, httpx, aiohttp - HTTP clients
• beautifulsoup4, lxml - HTML/XML parsing
• selenium, playwright - Browser automation
• scrapy - Web scraping framework

Data Processing

• numpy, pandas, scipy - Numerical computing
• matplotlib, seaborn, plotly - Visualization

Image Processing

• pillow, opencv-python-headless - Image manipulation
• scikit-image, imageio - Image algorithms

Video & Audio

• moviepy - Video editing
• av (PyAV), ffmpeg-python - Video processing
• pydub - Audio manipulation

3D Processing

• trimesh, open3d - 3D mesh processing
• numpy-stl, meshio, pyvista - 3D file formats

Documents & Graphics

• svgwrite, cairosvg - SVG creation
• reportlab, pypdf2 - PDF generation

Examples

Web Scraping

infsh app run infsh/python-executor --input '{
  "code": "import requestsnfrom bs4 import BeautifulSoupnnresponse = requests.get("https://example.com")nsoup = BeautifulSoup(response.content, "html.parser")nprint(soup.find("title").text)"
}'

Data Analysis with Visualization

infsh app run infsh/python-executor --input '{
  "code": "import pandas as pdnimport matplotlib.pyplot as pltnndata = {"name": ["Alice", "Bob"], "sales": [100, 150]}ndf = pd.DataFrame(data)nnplt.bar(df["name"], df["sales"])nplt.savefig("outputs/chart.png")nprint("Chart saved!")"
}'

Image Processing

infsh app run infsh/python-executor --input '{
  "code": "from PIL import Imagenimport numpy as npnn# Create gradient imagenarr = np.linspace(0, 255, 256*256, dtype=np.uint8).reshape(256, 256)nimg = Image.fromarray(arr, mode="L")nimg.save("outputs/gradient.png")nprint("Image created!")"
}'

Video Creation

infsh app run infsh/python-executor --input '{
  "code": "from moviepy.editor import ColorClip, TextClip, CompositeVideoClipnnclip = ColorClip(size=(640, 480), color=(0, 100, 200), duration=3)ntxt = TextClip("Hello!", fontsize=70, color="white").set_position("center").set_duration(3)nvideo = CompositeVideoClip([clip, txt])nvideo.write_videofile("outputs/hello.mp4", fps=24)nprint("Video created!")",
  "timeout": 120
}'

3D Model Processing

infsh app run infsh/python-executor --input '{
  "code": "import trimeshnnsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)nsphere.export("outputs/sphere.stl")nprint(f"Created sphere with {len(sphere.vertices)} vertices")"
}'

API Calls

infsh app run infsh/python-executor --input '{
  "code": "import requestsnimport jsonnnresponse = requests.get("https://api.github.com/users/octocat")ndata = response.json()nprint(json.dumps(data, indent=2))"
}'

File Output

Files saved to outputs/ are automatically returned:

# These files will be in the response
plt.savefig('outputs/chart.png')
df.to_csv('outputs/data.csv')
video.write_videofile('outputs/video.mp4')
mesh.export('outputs/model.stl')

Variants

# Default (8GB RAM)
infsh app run infsh/python-executor --input input.json

# High memory (16GB RAM) for large datasets
infsh app run infsh/python-executor@high_memory --input input.json

Use Cases

• Web scraping - Extract data from websites
• Data analysis - Process and visualize datasets
• Image manipulation - Resize, crop, composite images
• Video creation - Generate videos with text overlays
• 3D processing - Load, transform, export 3D models
• API integration - Call external APIs
• PDF generation - Create reports and documents
• Automation - Run any Python script

Important Notes

• CPU-only - No GPU/ML libraries (use dedicated AI apps for that)
• Safe execution - Runs in isolated subprocess
• Non-interactive - Use plt.savefig() not plt.show()
• File detection - Output files are auto-detected and returned

Related Skills

# AI image generation (for ML-based images)
npx skills add inference-sh/skills@ai-image-generation

# AI video generation (for ML-based videos)
npx skills add inference-sh/skills@ai-video-generation

# LLM models (for text generation)
npx skills add inference-sh/skills@llm-models

Documentation

• Running Apps - How to run apps via CLI
• App Code - Understanding app execution
• Sandboxed Code Execution - Safe code execution for agents