AI Code Reviewer

A powerful, automated code review tool powered by Python and Large Language Models (Llama 3 via Groq). It analyzes source code files for Clean Code violations, SOLID principles adherence, security vulnerabilities, and performance bottlenecks, generating detailed Markdown reports.

Features

• Automated Analysis: Scans local directories for source code files (.py, .js, .java, etc.).
• AI-Powered: Utilizes the Llama 3 model (via Groq API) for deep code reasoning.
• Detailed Reports: Generates professional Markdown reports with actionable feedback and refactoring suggestions.
• Clean Architecture: Built with modularity, scalability, and SOLID principles in mind.
• Web Interface: A professional, deployable web app (Flask) with drag & drop uploads, paste-code support and in-browser rendered reviews with syntax highlighting.
• CLI Interface: Easy-to-use Command Line Interface.
• Desktop GUI (optional): A native desktop client (CustomTkinter) for offline use.

Architecture

The project follows a modular Source Layout structure:

src/
├── analyzer/    # Handles file system operations (SRP)
├── ai_engine/   # Decoupled LLM client (currently Groq/Llama3)
├── reports/     # Generates output formats (Markdown)
├── utils/       # Configuration & Logging (Singleton/Static patterns)
├── gui.py       # Desktop GUI (CustomTkinter) — optional, reuses the core
└── main.py      # CLI Orchestrator
webapp/
├── server.py    # Flask web server (JSON API + report downloads)
├── templates/   # Single-page front-end (index.html)
└── static/      # CSS, JS and vendored libraries

Every interface (Web, CLI and Desktop) is a thin presentation layer: they all reuse LocalFileReader, GroqClient and ReportGenerator unchanged, so the business logic lives in exactly one place.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/ai-code-reviewer.git
   cd ai-code-reviewer

2. Set up a virtual environment (recommended):

   python -m venv venv
   # Windows:
   venv\Scripts\activate
   # Mac/Linux:
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configuration:

   • Rename .env.example to .env.
   • Add your Groq API Key (Get one for free at console.groq.com):

     GROQ_API_KEY=gsk_your_key_here...

Usage

Web Interface (recommended)

Start the development server and open it in your browser:

python -m webapp.server
# then open http://127.0.0.1:5000

From the page you can:

• Drag & drop or browse source files, or switch to Paste code to review a snippet on the fly.
• Set your Groq API key from the top-right ⚙ button (stored only in your browser) if it isn't configured server-side via .env.
• Click Analyze Code; each file gets its own tab with the review rendered as Markdown with syntax highlighting.
• Download the full Markdown report, which is also saved server-side to output/.

Production deployment

The server is a standard WSGI app exposed as webapp.server:app. To deploy behind a production server (Linux):

gunicorn "webapp.server:app" --bind 0.0.0.0:8000 --timeout 300

Set the GROQ_API_KEY environment variable (or a .env file) on the host. The --timeout is raised because LLM calls can take several seconds per file.

Command Line Interface

Run the tool by pointing it to a directory containing code:

python -m src.main ./path/to/your/code

Example:

python -m src.main ./test_code

The tool will generate a report in the output/ directory:

output/code_review_report_YYYYMMDD_HHMMSS.md

Desktop GUI (optional, offline)

A native desktop client is also available for local use:

python app.py    # or: python -m src.gui

Technologies

• Language: Python 3.10+
• AI Model: Llama 3 (70B Versatile)
• API: Groq Cloud
• Web: Flask, vanilla JS, marked.js + highlight.js
• Desktop: CustomTkinter
• Libraries: groq, python-dotenv

License

This project is open-source