Readme.md

Multi-Projects Runner

A server that runs multiple projects (Node.js, Python, Go, Rust, etc.) with public tunnel URLs.

Features

• Multi-language support: Node.js, Python, Go, Rust, Deno, Static sites
• Auto-setup: Automatically installs dependencies for each project
• Tunnel support: Cloudflare (primary) + ngrok (fallback)
• Port management: Auto-detects available ports
• API endpoints: Start/stop projects via REST API

Quick Start

Local Development

npm install
npm run dev

Docker

docker build -t project-runner .
docker run -p 10000:10000 \
  -v $(pwd)/projects:/opt/render/project/src/projects \
  project-runner

Docker Compose

docker-compose up --build

Environment Variables

Variable	Default	Description
PORT	10000	Server port
NODE_ENV	production	Environment mode
NGROK_AUTHTOKEN	-	ngrok auth token (optional)

API Endpoints

Method	Endpoint	Description
GET	/api/projects	List projects
GET	/api/projects/:id	Get project status
POST	/api/projects/:id/start	Start project
POST	/api/projects/:id/stop	Stop project
GET	/api/status	Server status

Project Structure

Projects should be in the projects/ directory:

projects/
├── project1/          # Node.js (has package.json)
│   ├── package.json
│   ├── vite.config.js
│   └── src/
├── project2/          # Python (has main.py)
│   ├── main.py
│   └── requirements.txt
└── project3/          # Go (has go.mod)
    └── main.go

Hugging Face Projects

You can deploy Hugging Face models and apps in your projects. For Python projects, add transformers, torch, etc. to your requirements.txt.

Example project structure for a Hugging Face app:

projects/hf-project/
├── main.py          # Your Hugging Face app code
├── requirements.txt # Include transformers, torch, etc.
└── model/           # Optional: local model files

To use Hugging Face models, ensure your requirements.txt includes:

transformers
torch
huggingface_hub

Then, in your main.py, you can load and use models like:

from transformers import pipeline

# Example: text generation
generator = pipeline('text-generation', model='gpt2')
result = generator("Hello, I'm a language model")
print(result)

For Streamlit apps with Hugging Face, use the streamlit option.

1. Connect GitHub repo to Render
2. Set build command: npm install
3. Set start command: node server.js
4. Add environment variables as needed

For full setup on Render, use setup.sh or deploy via Dockerfile.