🤖 AI Code Plagiarism Detector

A small web app that analyzes source code and estimates how likely it is to be AI-generated. The app visualizes an "AI percentage" in a circular chart and streams human-readable analysis results as they are generated.

This repo contains the frontend (Vite + React) and utilities for interacting with a backend analysis service.

Key features

• AI-percentage visual (circular chart)
• Streaming analysis output (progressive text like ChatGPT)
• Simple code editor input and history sidebar

Quick demo

1. Open the frontend in your browser (after starting the dev server).
2. Paste or type code into the editor.
3. The AI-percentage and streaming analysis update in real time.

Contents

• index.html — app entry HTML
• package.json — frontend project manifest
• src/ — main frontend source
  • App.jsx, main.jsx — entry & boot
  • index.css — styles
  • components/ — UI components (Editor, Header, Sidebar, Report, etc.)
  • utils/analysis.js — client-side helpers for analysis streaming
  • utils/pdfGenerator.js — export report helpers

Local setup (frontend)

This project uses npm and Vite for the frontend. From the repo root:

# install dependencies
npm install

# run the dev server (Vite)
npm run dev

Open the URL that Vite prints (usually http://localhost:5173). The UI will load and you can paste code into the editor.

Backend note

This repo assumes a separate backend analysis service (Node/Express) that exposes an API endpoint the frontend calls to run code analysis and stream results. The original README mentioned script.js pointing to a Render backend URL — update the API base URL in the frontend if your backend runs at a different host.

If you have the backend source and want to run it locally, typical commands are:

# from the backend directory (if separate)
npm install
node index.js

Then update the frontend API URL (where the frontend sends code for analysis) to point to http://localhost:<PORT>.

Configuration pointers

• API endpoint: ensure the frontend points to your analysis backend. Look for the file that contains the backend URL (example: src/utils/analysis.js or a script.js referenced in index.html) and update it.
• Environment: if the backend requires API keys or ENV values, provide them locally per the backend README (not included here).

Running a production build

To build the frontend for production:

npm run build

You can then host the generated dist/ with any static host (Vercel, Netlify, GitHub Pages, etc.). If you deploy the frontend, update the deployed frontend's backend URL to the production backend endpoint.

Project structure (short)

.
├─ index.html
├─ package.json
├─ src/
│  ├─ App.jsx
│  ├─ main.jsx
│  ├─ index.css
│  ├─ components/
│  │  ├─ CodeEditor.jsx
│  │  ├─ AnalysisReport.jsx
│  │  ├─ HistorySidebar.jsx
│  │  └─ ...
│  └─ utils/
│     ├─ analysis.js
│     └─ pdfGenerator.js

Contributing

• Fork the repository and create a feature branch.
• Open a pull request with a clear description of the change.

Small, safe improvements I recommend:

• Add instructions or environment templates for the backend if you include it in the repo.
• Add tests for utils/analysis.js (unit tests for parsing/formatting streaming messages).

Troubleshooting

• If the analysis stream doesn't start, verify the frontend's API URL and that the backend is running and accessible.
• Open the browser console / network tab to inspect requests and SSE/Fetch streaming responses.

Developed BY : Piyush Ranakoti 🧠

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