CodePilot AI

CodePilot AI is a full-stack AI-powered code analysis platform that helps users upload, explore, understand, review, and document software projects through a simple web interface.

The application combines a React frontend, a FastAPI backend, and Gemini-powered AI features to analyse complete project folders and individual source-code files.

🔗 Live Demo: codepilot-frontend-i46e.onrender.com

Hosted on Render's free tier, so the first load after a period of inactivity can take up to a minute while the server wakes up.

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Features

• Upload multiple source-code files or an entire project folder
• Filter supported files and ignore unsupported file types
• View the total number of analysed files and lines of code
• Browse individual files and view their source code
• Generate an AI-powered summary of the uploaded project
• Detect programming languages, frameworks, libraries, and tools
• Generate beginner-friendly code explanations
• Identify possible bugs and code-quality issues
• Generate code optimisation suggestions
• Automatically generate README documentation
• Download the generated documentation as a Markdown file

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Screenshots

Home Page

Project Upload

Project Analysis

Source-Code Viewer

AI Project Summary

Technology Detection

AI Code Explanation

AI Bug Detection

AI Code Optimisation

AI Documentation Generation

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Tech Stack

Frontend

• React.js
• JavaScript
• Vite
• CSS3
• React Router

Backend

• FastAPI
• Python
• Uvicorn
• Pydantic
• REST APIs

AI Integration

• Google Gemini API
• Prompt-based code and project analysis

Development Tools

• Git
• GitHub
• Visual Studio Code
• Postman / FastAPI Swagger UI

Deployment

• Render

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Supported File Types

CodePilot AI currently supports:

.py
.java
.js
.jsx
.ts
.tsx
.html
.css
.json
.md
.txt

Folders such as node_modules, .git, virtual environments, build folders, and Python cache folders can be excluded from project analysis.

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Project Workflow

User uploads files or a project folder
              ↓
React sends the files to the FastAPI backend
              ↓
The backend stores and analyses supported files
              ↓
The user views project statistics and source code
              ↓
Selected project data or code is sent to Gemini
              ↓
AI-generated analysis is displayed in the frontend

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Project Structure

CodePilot-AI/
├── backend/
│   ├── app.py
│   ├── services/
│   ├── uploads/
│   ├── requirements.txt
│   └── .env
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   └── services/
│   ├── package.json
│   └── vite.config.js
├── screenshots/
├── .gitignore
└── README.md

The .env file is ignored by Git and must never be uploaded to GitHub.

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Getting Started

Prerequisites

Install the following before running the project:

• Python 3.10 or later
• Node.js and npm
• Git
• A Google Gemini API key

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Backend Setup

1. Clone the repository

git clone https://github.com/Induja2210/CodePilot-AI.git
cd CodePilot-AI

2. Open the backend folder

cd backend

3. Create a virtual environment

python3 -m venv env

4. Activate the virtual environment

macOS or Linux

source env/bin/activate

Windows

env\Scripts\activate

5. Install backend dependencies

pip install -r requirements.txt

6. Create the environment file

Create a file named .env inside the backend folder:

GEMINI_API_KEY=your_gemini_api_key

7. Start the FastAPI server

uvicorn app:app --reload

The backend will run at:

http://127.0.0.1:8000

FastAPI Swagger documentation:

http://127.0.0.1:8000/docs

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Frontend Setup

Open a new terminal from the project root.

1. Open the frontend folder

cd frontend

2. Install frontend dependencies

npm install

3. Start the development server

npm run dev

The frontend will usually run at:

http://localhost:5173

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API Overview

The backend provides REST endpoints for operations such as:

• Uploading files and project folders
• Analysing uploaded files
• Viewing source-code content
• Generating project summaries
• Detecting technologies
• Explaining code
• Detecting possible bugs
• Suggesting code optimisations
• Generating project documentation

The available routes can be tested through FastAPI Swagger UI at /docs.

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Security

• API keys are stored in backend/.env
• .env files are excluded through .gitignore
• Virtual environments and Python cache files are not committed
• Unsupported file types are skipped during upload
• Large source files can be limited before being sent to the AI model

Never place API keys directly inside source-code files.

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Current Status

CodePilot AI is deployed and live on Render. It currently provides an end-to-end working MVP for project upload, codebase analysis, source-code viewing, AI project summarisation, technology detection, code explanation, bug detection, code optimisation, and automated documentation generation.

Future improvements may include:

• Automated unit-test generation
• Documentation export in additional formats
• Code-complexity analysis
• Pull-request review
• Persistent project history
• User authentication
• Database integration
• Improved handling of large repositories
• Agentic multi-step code review workflows

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Author

Induja B
B.Tech Computer Science and Engineering — Artificial Intelligence and Machine Learning

• GitHub: Induja2210

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Acknowledgements

• FastAPI
• React
• Vite
• Google Gemini

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License

This project is currently intended for educational and portfolio purposes.