workflow: fix package version extraction using pacman -Qp
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---
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name: python-executor
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description: "Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib"
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allowed-tools: Bash(belt *)
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---
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# Python Code Executor
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Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.
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## Quick Start
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> Requires inference.sh CLI (`belt`). [Install instructions](https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md)
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```bash
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belt login
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# Run Python code
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belt app run infsh/python-executor --input '{
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"code": "import pandas as pd\nprint(pd.__version__)"
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}'
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```
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## App Details
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| Property | Value |
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|----------|-------|
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| App ID | `infsh/python-executor` |
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| Environment | Python 3.10, CPU-only |
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| RAM | 8GB (default) / 16GB (high_memory) |
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| Timeout | 1-300 seconds (default: 30) |
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## Input Schema
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```json
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{
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"code": "print('Hello World!')",
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"timeout": 30,
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"capture_output": true,
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"working_dir": null
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}
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```
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## Pre-installed Libraries
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### Web Scraping & HTTP
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- `requests`, `httpx`, `aiohttp` - HTTP clients
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- `beautifulsoup4`, `lxml` - HTML/XML parsing
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- `selenium`, `playwright` - Browser automation
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- `scrapy` - Web scraping framework
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### Data Processing
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- `numpy`, `pandas`, `scipy` - Numerical computing
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- `matplotlib`, `seaborn`, `plotly` - Visualization
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### Image Processing
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- `pillow`, `opencv-python-headless` - Image manipulation
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- `scikit-image`, `imageio` - Image algorithms
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### Video & Audio
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- `moviepy` - Video editing
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- `av` (PyAV), `ffmpeg-python` - Video processing
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- `pydub` - Audio manipulation
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### 3D Processing
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- `trimesh`, `open3d` - 3D mesh processing
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- `numpy-stl`, `meshio`, `pyvista` - 3D file formats
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### Documents & Graphics
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- `svgwrite`, `cairosvg` - SVG creation
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- `reportlab`, `pypdf2` - PDF generation
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## Examples
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### Web Scraping
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```bash
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belt app run infsh/python-executor --input '{
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"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)"
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}'
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```
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### Data Analysis with Visualization
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```bash
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belt app run infsh/python-executor --input '{
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"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!\")"
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}'
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```
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### Image Processing
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```bash
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belt app run infsh/python-executor --input '{
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"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!\")"
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}'
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```
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### Video Creation
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```bash
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belt app run infsh/python-executor --input '{
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"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!\")",
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"timeout": 120
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}'
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```
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### 3D Model Processing
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```bash
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belt app run infsh/python-executor --input '{
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"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\")"
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}'
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```
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### API Calls
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```bash
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belt app run infsh/python-executor --input '{
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"code": "import requests\nimport json\n\nresponse = requests.get(\"https://api.github.com/users/octocat\")\ndata = response.json()\nprint(json.dumps(data, indent=2))"
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}'
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```
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## File Output
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Files saved to `outputs/` are automatically returned:
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```python
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# These files will be in the response
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plt.savefig('outputs/chart.png')
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df.to_csv('outputs/data.csv')
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video.write_videofile('outputs/video.mp4')
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mesh.export('outputs/model.stl')
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```
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## Variants
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```bash
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# Default (8GB RAM)
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belt app run infsh/python-executor --input input.json
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# High memory (16GB RAM) for large datasets
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belt app run infsh/python-executor@high_memory --input input.json
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```
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## Use Cases
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- **Web scraping** - Extract data from websites
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- **Data analysis** - Process and visualize datasets
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- **Image manipulation** - Resize, crop, composite images
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- **Video creation** - Generate videos with text overlays
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- **3D processing** - Load, transform, export 3D models
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- **API integration** - Call external APIs
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- **PDF generation** - Create reports and documents
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- **Automation** - Run any Python script
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## Important Notes
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- **CPU-only** - No GPU/ML libraries (use dedicated AI apps for that)
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- **Safe execution** - Runs in isolated subprocess
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- **Non-interactive** - Use `plt.savefig()` not `plt.show()`
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- **File detection** - Output files are auto-detected and returned
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## Related Skills
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```bash
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# AI image generation (for ML-based images)
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npx skills add inference-sh/skills@ai-image-generation
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# AI video generation (for ML-based videos)
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npx skills add inference-sh/skills@ai-video-generation
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# LLM models (for text generation)
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npx skills add inference-sh/skills@llm-models
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```
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## Documentation
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- [Running Apps](https://inference.sh/docs/apps/running) - How to run apps via CLI
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- [App Code](https://inference.sh/docs/extend/app-code) - Understanding app execution
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- [Sandboxed Code Execution](https://inference.sh/blog/tools/sandboxed-execution) - Safe code execution for agents
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