AI Terminal

A modern, AI-powered terminal interface built with React and Python Flask. This terminal allows you to execute commands using natural language through Google's Gemini AI.

Features

• 🤖 AI-Powered Commands: Convert natural language to shell commands
• 🧠 Contextual Understanding: AI remembers context from previous commands
• 🖥️ Multi-Tab Support: Multiple terminal sessions with persistent state
• ⌨️ Auto-Complete: Tab completion for commands and file paths
• 🎨 Modern UI: Beautiful terminal interface with custom themes
• 🔍 Smart File Operations: Find, count, and manipulate files using natural language
• 📊 System Monitoring: Easy CPU, memory, and process monitoring
• 🌐 Cross-Platform: Works on Windows, macOS, and Linux
• 🔒 Security: Command validation and whitelist protection
• 📱 Responsive: Works on desktop and mobile devices
• 🔄 Session Tracking: Maintains context across commands
• 🔴 Status Indicator: Real-time backend connection status with AI availability
• 🔄 Real-time Output: Streaming command output via WebSockets
• 🛑 Command Cancellation: Cancel long-running commands with Ctrl+C

Prerequisites

• Node.js (v18 or higher)
• Python 3.9 or higher
• Google AI API key (for Gemini 1.5 Flash)

Quick Start

Automated Setup (Recommended)

1. Clone the repository:

git clone https://github.com/krishnasharma4415/AI-Terminal.git
cd AI-Terminal

2. Run the setup script:

# On macOS/Linux:
./setup.sh

# On Windows:
# Right-click setup.sh and open with Git Bash

3. Start the backend server:

# Activate the virtual environment
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Start the server
python app.py  # Runs on http://localhost:5000

4. Start the frontend development server:

cd ai-terminal
npm run dev  # Runs on http://localhost:5173

Manual Setup

1. Clone the repository:

git clone https://github.com/krishnasharma4415/AI-Terminal.git
cd AI-Terminal

2. Set up the backend:

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file with your Google AI API key
cp env.example .env
# Edit .env with your editor and add your API key

3. Start the backend server:

python app.py  # Runs on http://localhost:5000

4. Set up the frontend:

cd ai-terminal

# Install dependencies
npm install

# Create development environment file
echo "VITE_API_URL=http://localhost:5000" > .env.development

# Start development server
npm run dev  # Runs on http://localhost:5173

Using the Terminal

Basic Usage

1. Open the terminal interface in your web browser (http://localhost:5173)
2. Create a new tab or use the default tab
3. Enter commands using natural language or traditional shell syntax
4. Press Enter to execute commands

Example Commands

Natural Language	Equivalent Shell Command
"Show all files"	ls -la
"What's my CPU usage"	cpu or system-specific command
"Find all Python files"	find . -name "*.py"
"How much disk space is left"	df -h
"Show running processes"	ps aux
"Count files in this folder"	`ls -1

Contextual Features

The AI remembers context, allowing for follow-up commands:

1. find . -name "*.py" (lists Python files)
2. count them (counts the files from previous command)

Keyboard Shortcuts

• Up/Down Arrow: Navigate command history
• Tab: Autocomplete commands and file paths
• Ctrl+C: Cancel current command
• Ctrl+L: Clear terminal
• Ctrl+R: Search command history
• Ctrl+Shift+P: Open command palette

Building for Production

To build the frontend for production:

cd ai-terminal
npm run build

This creates a dist directory with optimized files ready for deployment.

For backend production deployment, see DEPLOYMENT.md.

Installation

1. Clone the repository

git clone <your-repo-url>
cd AI-Terminal

2. Set up the Python backend

# Install Python dependencies
pip install -r requirements.txt

# Set up environment variables
cp env.example .env
# Edit .env and add your Google AI API key

3. Set up the React frontend

cd ai-terminal
npm install

Configuration

Environment Variables

Create a .env file in the root directory with the following variables:

# Required: Google AI API Key
GOOGLE_API_KEY=your_google_api_key_here

# Optional: Flask configuration
FLASK_ENV=development
FLASK_DEBUG=True

Getting a Google AI API Key

1. Visit Google AI Studio
2. Create a new API key
3. Add it to your .env file

Running the Application

1. Start the Python backend

python app.py

The backend will run on http://127.0.0.1:5000

2. Start the React frontend

cd ai-terminal
npm run dev

The frontend will run on http://localhost:5173

Usage

Basic Commands

• ls - List directory contents
• cd <path> - Change directory
• pwd - Print working directory
• mkdir <name> - Create directory
• rm <file> - Remove file
• help - Show help information

AI Commands

Type natural language commands like:

• "Show me all Python files"
• "Find files larger than 100MB"
• "List running processes"
• "Check disk usage"

Keyboard Shortcuts

• Ctrl+Shift+P - Open command palette
• ↑/↓ - Navigate command history
• Tab - Auto-complete commands/paths
• Enter - Execute command

Security Features

• Command Whitelist: Only allows safe, predefined commands
• Input Validation: Prevents malicious input
• Path Traversal Protection: Blocks dangerous path operations
• Command Length Limits: Prevents buffer overflow attacks

Development

Project Structure

AI-Terminal/
├── app.py                 # Python Flask backend
├── requirements.txt       # Python dependencies
├── env.example           # Environment variables template
├── README.md             # This file
└── ai-terminal/          # React frontend
    ├── src/
    │   ├── PyTerminal.jsx # Main terminal component
    │   ├── main.jsx      # React entry point
    │   └── index.css     # Styles
    ├── package.json      # Node dependencies
    └── vite.config.js    # Vite configuration

Available Scripts

Frontend (ai-terminal/)

• npm run dev - Start development server
• npm run build - Build for production
• npm run preview - Preview production build
• npm run lint - Run ESLint

Troubleshooting

Common Issues

1. "AI features are not configured"

   • Make sure you've set the GOOGLE_API_KEY in your .env file
   • Verify the API key is valid

2. "Connection Error: Is the Python server running?"

   • Ensure the Python backend is running on port 5000
   • Check that no firewall is blocking the connection

3. Command not found

   • The command might not be in the allowed whitelist
   • Try using a different phrasing for AI commands

Deployment

This project is configured for easy deployment:

• Backend: Ready for deployment on Render
• Frontend: Ready for deployment on Vercel

For detailed deployment instructions, see DEPLOYMENT.md.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.

Acknowledgments

• Google Gemini AI for natural language processing
• React and Vite for the frontend framework
• Flask for the Python backend
• Tailwind CSS for styling