Project Description:

The AI-Based Code Autocompletion Editor is an intelligent web-based code editor that helps developers write code faster and with fewer errors by offering real-time AI-powered suggestions. It predicts the next line, statement, or function call based on what the user is typing, similar to features in modern IDEs like Visual Studio Code, GitHub Copilot, or TabNine, but implemented as a custom web app.

The system uses machine learning models trained on code datasets or leverages pre-trained AI models (like OpenAI Codex or CodeBERT) via APIs to offer suggestions, detect syntax errors, and even autocomplete functions based on context.

Key Features:

Real-Time Code Autocompletion

• Predicts variables, functions, classes, and methods as the user types.

• Context-aware suggestions based on previously written code.

AI-Based Suggestions

• Uses AI/ML model or third-party AI API to analyze code context.

• Suggests code snippets or even full blocks (e.g., for-loops, if-statements).

Multi-language Support

• Supports popular languages like Python, JavaScript, Java, PHP, and more.

• Syntax highlighting tailored per language.

Syntax Error Detection

• Highlights syntax errors as you type (using static analysis + AI correction hints).

• Offers one-click fix suggestions.

Code Snippet Insertion

• Frequently used functions or templates can be suggested from a library or ML model.

Documentation Hints

• When hovering over a function or variable, documentation or AI-generated explanations are shown.

Web Application Tech Stack:

Frontend:

• HTML, CSS, Bootstrap – UI and responsive layout

• JavaScript – Core logic for typing events and editor behavior

• CodeMirror / Monaco Editor – Browser-based editor with syntax highlighting

• AJAX / WebSockets – Communicate with AI engine in real-time

Backend (Choose One):

• Node.js / Java (Spring Boot) / PHP – Handles code parsing, logging, and AI API integration

• API calls to AI/ML models (e.g., OpenAI Codex API or HuggingFace CodeBERT models)

• Provides suggestion results to frontend editor

Database:

• MongoDB or MySQL – Save:

  • Code history

  • User profiles

  • Frequently used snippets

AI/ML Integration Options:

1. OpenAI Codex or GPT-4 API (Recommended)

   • Input: User’s partially typed code.

   • Output: Suggestion for next line or block of code.

2. Fine-Tuned Code Language Models

   • Use CodeBERT, CodeT5, or Transformer models trained on public code repositories.

   • Hosted via custom ML backend (optional if not using paid APIs).