CodeGuard AI

Overview

CodeGuard AI is a full-stack static code analysis platform designed to help developers identify security vulnerabilities and code quality issues in Python applications. It combines a modern React frontend with a FastAPI backend to perform static analysis, generate security scores, classify vulnerabilities by severity, and produce professional audit reports.

The platform provides an intuitive browser-based development experience through an integrated Monaco Editor while delivering detailed analysis results and downloadable PDF reports.

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Features

• Monaco-powered code editor
• Write, paste, upload, or drag-and-drop Python files
• Static code analysis using 26 security and quality rules
• AST-based analysis for advanced code inspection
• Security score and letter grade generation
• Severity-wise issue classification
• Detailed findings dashboard with recommendations
• Professional downloadable PDF audit reports
• Scan history
• Responsive user interface
• Dark IDE-inspired design
• FastAPI-powered backend
• React + Vite frontend

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Static Analysis Capabilities

CodeGuard AI performs analysis for:

• Hardcoded credentials
• Unsafe deserialization
• Mutable default arguments
• Bare exception handling
• Duplicate function definitions
• Unreachable code
• Unused imports
• Unused variables
• Infinite loops
• Weak coding practices
• General code quality issues

The analysis combines pattern-based rule checking with Python Abstract Syntax Tree (AST) inspection for improved accuracy.

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Technology Stack

Frontend

• React
• Vite
• React Router
• Tailwind CSS
• Monaco Editor
• Framer Motion
• jsPDF
• jspdf-autotable
• Lucide React

Backend

• FastAPI
• Python
• Uvicorn
• Python AST Module

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Project Structure

CodeGuard-AI
│
├── frontend
│   ├── public
│   ├── src
│   │   ├── components
│   │   ├── pages
│   │   ├── utils
│   │   └── ...
│   ├── package.json
│   └── ...
│
├── backend
│   ├── main.py
│   ├── analyzer.py
│   ├── rules.py
│   ├── ast_rules.py
│   ├── requirements.txt
│   └── ...
│
└── README.md

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Installation

Clone the Repository

git clone https://github.com/Ritish363/codeguard-ai.git

cd codeguard-ai

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Backend Setup

cd backend

python -m venv .venv

# Windows
.venv\Scripts\activate

# macOS/Linux
source .venv/bin/activate

pip install -r requirements.txt

uvicorn main:app --reload

Backend will run at:

http://127.0.0.1:8000

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Frontend Setup

cd frontend

npm install

npm run dev

Frontend will run at:

http://localhost:5173

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Environment Variables

Create a .env file inside the frontend directory.

VITE_API_URL=http://127.0.0.1:8000

For production deployment, replace the value with the deployed backend URL.

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Usage

1. Launch both frontend and backend.
2. Enter or upload a Python source file.
3. Click Analyze Code.
4. Review the generated security score, grade, and detected issues.
5. Generate and download the audit report.

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Screenshots

Screenshots will be added after deployment.

• Home Page
• Code Workspace
• Analysis Dashboard
• Audit Report

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Deployment

Frontend

Vercel

Backend

Render

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Future Enhancements

• Support for additional programming languages
• AI-powered code fix suggestions
• GitHub repository scanning
• User authentication
• Persistent cloud-based scan history
• Database integration
• Docker deployment
• CI/CD pipeline

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Author

Ritish Oswal

B.Tech – Artificial Intelligence & Data Science

GitHub: https://github.com/Ritish363

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License

This project is intended for educational, portfolio, and demonstration purposes.

Live Demo

Frontend: https://codeguard-ai-umber.vercel.app

Backend API: https://codeguard-ai-backend-iy6j.onrender.com

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Note

The backend is hosted on Render Free Tier. The first request after inactivity may take around 30–60 seconds.