Natural Language Shell Interface

AI-Assisted Command Translation & Execution System

A system-level application that enables users to interact with a Unix/Linux terminal using natural language. The system translates user instructions into executable shell commands using AI, executes them via a custom C-based shell, and streams results in real time through a web interface.

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📌 Overview

Natural Language Shell Interface combines Operating Systems fundamentals, shell programming, and AI-assisted command processing to simplify terminal usage while preserving execution reliability and system-level control.

This project demonstrates:

• Custom shell implementation in C
• Process execution and command parsing
• Client–server architecture with real-time communication
• Practical application of AI in systems software

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✨ Features

🖥 Natural Language Command Execution

• Converts plain English instructions into valid Unix/Linux commands
• Supports file operations, navigation, search, and system commands
• Handles 30+ command patterns

⚙️ Custom Mini-Bash Shell (C)

• Implements command parsing, execution, and error handling
• Automatic fallback to native system shell for unsupported commands
• Ensures reliable execution on Linux/macOS

🔁 Real-Time Execution Pipeline

• REST APIs for command handling
• WebSockets for live output streaming
• End-to-end latency under 100 ms

📂 System-Wide File Search

• Searches across directories
• Automatic path resolution
• Application-aware file opening (e.g., VS Code)

🎙 Optional Voice Input

• Browser-based speech recognition
• Hands-free command execution
• Asynchronous execution handling

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🏗 Architecture

The system follows a modular client–server architecture with a dual execution layer to ensure reliability and OS-level control.

Frontend (React)
│
├── Natural Language Input
├── Terminal UI
├── WebSocket Client
│
└── REST / WebSocket APIs
        ↓
Backend (Python / Flask)
│
├── AI Command Translation (Google Gemini)
├── Command Dispatcher & Validation
├── System-Wide File Search Engine
├── Execution Controller
│
└── Dual Execution Layer
        ├── Custom Mini-Bash (C)
        └── Native System Shell (Linux / macOS)

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⚙️ Backend Setup

Prerequisites

• Python 3.8+
• pip
• Linux or macOS

Setup

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Create .env file:

GEMINI_API_KEY=your_api_key_here
FLASK_ENV=development
FLASK_DEBUG=True
PORT=5002
CORS_ORIGINS=http://localhost:3000

Run backend:

python app.py

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🎨 Frontend Setup

cd frontend
npm install
npm start

Open:

http://localhost:3000

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📄 License

MIT License

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👤 Author

Aditya Pandey
Computer Science Undergraduate
Focus: Operating Systems, Systems Programming, Scalable Software Engineering