README.md

metadata

title: Advanced Code Interpreter Sandbox
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: docker
sdk_version: 24.1.2
app_file: app.py
pinned: false
license: mit
hardware: t4-small

🚀 Advanced Code Interpreter Sandbox

A powerful, feature-rich code execution environment on HuggingFace Spaces that rivals e2b.dev! This sandbox provides a comprehensive Python development environment with advanced capabilities.

✨ Features

🔐 Secure Code Execution

• Safe, sandboxed Python code execution
• Timeout protection (10s default)
• Memory limit controls
• Syntax error handling
• Exception capture and display

📁 File System Support

• Upload files directly to the workspace
• Download results and outputs
• Multi-file management
• Real-time file operations (read, delete, list)
• Temporary file storage with session isolation

📦 Package Management

• On-the-fly package installation via pip
• Support for popular data science libraries
• Package version management
• Quick install of common packages (numpy, pandas, matplotlib, plotly, etc.)
• Pre-installed essential packages

📊 Data Visualization

• Built-in support for:
  • Matplotlib - Static plots and charts
  • Plotly - Interactive visualizations
  • Seaborn - Statistical data visualization
  • Bokeh - Interactive plots
  • Altair - Declarative visualization
  • Pillow - Image processing

💾 Session Persistence

• Maintains state throughout session
• File persistence
• Package installation history
• Session information tracking
• Uptime monitoring

🔄 Real-time Output

• Streamed stdout/stderr capture
• Live code execution feedback
• Error highlighting
• Output mode selection (stdout, stderr, or both)

📝 Multi-file Support

• Create and manage multiple files
• File editor interface
• Switch between files easily
• Automatic file detection
• File metadata tracking

🎨 Superior UX

• Clean, modern Gradio interface
• Dark theme support
• Intuitive tabbed interface
• Responsive design
• Syntax highlighting in code editor
• Progress indicators

🚀 Getting Started

1. Run Your Code

# Write Python code in the editor
print("Hello, World!")

# Import any installed package
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(f"Array: {arr}")
print(f"Mean: {np.mean(arr)}")

2. Install Packages

• Go to "Package Manager" tab
• Enter package names (comma-separated)
• Click "Install"
• Example: numpy, pandas, matplotlib, plotly

3. Upload Files

• Go to "File Manager" tab
• Upload files using the file picker
• Files are stored in your workspace
• Access files directly in your code

4. Data Visualization Example

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create plot
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', label='sin(x)')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Sine Wave')
plt.legend()
plt.grid(True)
plt.show()

📚 Pre-installed Libraries

The following packages are pre-installed and ready to use:

• NumPy
• Pandas
• Matplotlib
• Plotly
• Seaborn
• SciPy
• Scikit-learn
• Pillow
• Requests
• BeautifulSoup4
• NetworkX
• SymPy

🔧 Available Packages

You can install additional packages using the Package Manager. Popular options:

• tensorflow - Machine learning
• torch - Deep learning
• transformers - Hugging Face transformers
• streamlit - Web app framework
• dash - Interactive dashboards
• openai - OpenAI API client
• langchain - LLM application framework
• wordcloud - Text visualization
• geopandas - Geospatial analysis
• sqlalchemy - Database ORM

🎯 Use Cases

Data Analysis

import pandas as pd
import matplotlib.pyplot as plt

# Load and analyze data
df = pd.read_csv('your_data.csv')
print(df.head())
df.describe().plot()
plt.show()

Machine Learning

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Train a model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)

Web Scraping

import requests
from bs4 import BeautifulSoup

# Scrape a webpage
url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
print(soup.title)

API Integration

import requests
import json

# Call an API
response = requests.get('https://api.github.com/users/octocat')
data = response.json()
print(json.dumps(data, indent=2))

⚠️ Security Notes

• Code runs in a secure sandbox environment
• No network access to external resources (except for package installation and explicit API calls)
• Files are isolated per session
• Execution timeouts prevent infinite loops
• Maximum memory usage is limited

🆚 Compared to e2b.dev

Feature	Code Interpreter Sandbox	e2b.dev
Package Installation	✅ Yes	✅ Yes
File Upload/Download	✅ Yes	✅ Yes
Data Visualization	✅ Yes	✅ Yes
Session Persistence	✅ Yes	✅ Yes
Real-time Output	✅ Yes	✅ Yes
Multi-file Support	✅ Yes	✅ Yes
Cost	🆓 Free on Spaces	💰 Paid
Custom Environment	🐳 Docker-based	🐳 Docker-based
Pre-installed Libraries	📦 20+ packages	📦 Limited
GitHub Integration	🟡 Manual	✅ Automatic

🎨 Interface Overview

Code Executor Tab

• Write and execute Python code
• Choose output mode (stdout, stderr, or both)
• Real-time execution feedback
• Syntax highlighting

File Manager Tab

• Upload files to workspace
• View and manage all files
• Read file contents
• Delete unwanted files

Package Manager Tab

• Install packages on-the-fly
• View installed packages
• Batch installation support

Session Info Tab

• View session details
• Monitor uptime
• Track installed packages
• Workspace information

🚀 Deployment

This application is designed for HuggingFace Spaces and includes:

• Optimized requirements.txt
• Pre-configured dependencies
• Docker support
• GPU acceleration ready

📝 Tips & Tricks

1. Use variables across executions: Variables persist within a session
2. Install packages first: Install required packages before using them
3. Save important outputs: Use file operations to save results
4. Check session info: Monitor your session status
5. Explore the interface: Each tab provides different functionality

🤝 Contributing

Contributions are welcome! Areas for improvement:

• Additional language support
• Enhanced visualization options
• More pre-installed packages
• Improved error handling
• Better performance optimization

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Built with Gradio
• Powered by HuggingFace Spaces
• Inspired by e2b.dev

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Ready to code? Just start typing in the Code Executor tab! 🚀