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https://github.com/tdevuit/ie105-malicious-code-scanner
A Next.js 14 application that leverages machine learning models to detect malicious code in uploaded files, featuring a FastAPI backend.
https://github.com/tdevuit/ie105-malicious-code-scanner
fastapi malicious nextjs python typescript
Last synced: about 8 hours ago
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A Next.js 14 application that leverages machine learning models to detect malicious code in uploaded files, featuring a FastAPI backend.
- Host: GitHub
- URL: https://github.com/tdevuit/ie105-malicious-code-scanner
- Owner: TDevUIT
- License: mit
- Created: 2024-10-29T11:39:53.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2024-10-29T11:55:18.000Z (3 months ago)
- Last Synced: 2024-11-15T20:26:18.572Z (2 months ago)
- Topics: fastapi, malicious, nextjs, python, typescript
- Language: TypeScript
- Homepage: https://ie-105-malicious-code-scanner.vercel.app
- Size: 60.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
A Next.js 14 application that leverages machine learning models to detect malicious code in uploaded files, featuring a FastAPI backend.
## Introduction
The **Malicious Code Scanner** is a robust web app developed using Next.js and FastAPI, designed to enable users to upload files in various formats and scan for potential malicious code. It features a user-friendly, dashboard-like UI for selecting models, monitoring upload progress, and displaying scan results. This tool is ideal for developers and security teams needing quick, reliable file safety assessments using custom AI/ML models.
## How It Works
The Python/FastAPI backend handles the AI/ML models, seamlessly integrated with the Next.js frontend through `/api/` endpoints. File uploads trigger model-based scans, with results presented in a structured dashboard format.
The integration is facilitated through [`next.config.js` rewrites](https://github.com/vanthaita/malicious-code-scanner/blob/main/next.config.js), routing requests to `/api/py/:path*` directly to FastAPI.
Locally, the FastAPI server runs at `127.0.0.1:8000`, while in production, it operates as [serverless Python functions](https://vercel.com/docs/concepts/functions/serverless-functions/runtimes/python) on Vercel.
## Demo
Explore the live demo here: [Malicious Code Scanner](https://ie-105-malicious-code-scanner.vercel.app/)
## Deploy Your Own
Clone & deploy it to Vercel in one click:
[](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fvanthaita%2Fmalicious-code-scanner%2Ftree%2Fmain)
## Developing Locally
Set up the repository with:
```bash
npx create-next-app malicious-code-scanner --example "https://github.com/vanthaita/IE105-Malicious-Code-Scanner.git"
```
## Getting Started
### 1. Set up a virtual environment
```bash
python3 -m venv venv
source venv/bin/activate
```
### 2. Install dependencies
For the frontend, run:
```bash
npm install
# or
yarn
# or
pnpm install
```
For the backend, install Python packages listed in `requirements.txt`.
### 3. Run the development server
Start the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
```
Access the app at [http://localhost:3000](http://localhost:3000), with the FastAPI backend running at [http://127.0.0.1:8000](http://127.0.0.1:8000). Update the port in `package.json` and `next.config.js` as needed.
## Key Features
- **File Uploads**: Supports PDF and other common file formats.
- **Model Selection**: Allows users to choose from multiple models (in `.h5` format) for scanning.
- **Progress & Results Display**: Visualize scan progress and results in a dashboard format with graphical displays.
## Learn More
For further resources, check out:
- [Next.js Documentation](https://nextjs.org/docs)
- [FastAPI Documentation](https://fastapi.tiangolo.com/)
We welcome your feedback and contributions via [our GitHub repository](https://github.com/vanthaita/malicious-code-scanner)!
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