Python Code Executor

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

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

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

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

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

Data Analysis with Visualization

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

Image Processing

belt app run infsh/python-executor --input '{
  "code": "from PIL import Image\nimport numpy as np\n\n# Create gradient image\narr = 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

belt app run infsh/python-executor --input '{
  "code": "from moviepy.editor import ColorClip, TextClip, CompositeVideoClip\n\nclip = 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

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

API Calls

belt app run infsh/python-executor --input '{
  "code": "import requests\nimport json\n\nresponse = 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)
belt app run infsh/python-executor --input input.json

# High memory (16GB RAM) for large datasets
belt 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