Python Code Executor Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries. Quick Start Requires inference.sh CLI ( infsh ). Get installation instructions: npx skills add inference-sh/skills@agent-tools infsh login

Run Python code

infsh 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 infsh 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 infsh 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 infsh 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 infsh 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 infsh 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 infsh 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)

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