AI Torrent Search & Downloader

📌 Overview

This project is a torrent search and download automation tool powered by:

• LLM (Large Language Model) for turning natural language queries into optimized torrent search keywords.
• 1337x torrent search scraping to fetch results.
• qBittorrent WebUI API for automatically adding torrents to your download client.
• Full-stack app with:
  • Backend (Flask + MCP server) for orchestrating LLM + torrent search + downloads.
  • Frontend (React + Vite + Material UI) for a clean UI to enter queries and monitor results.

🔑 Key Features

• Accepts natural language queries (e.g., “Download all Harry Potter movies”).
• LLM (via Perplexity API) expands queries into torrent-ready keywords and categorizes them (Movies, Games, TV, Music, etc.).
• Scrapes results from 1337x (using cloudscraper to bypass Cloudflare).
• Auto-fetches magnet links and sends them to qBittorrent for download.
• Simple web interface for users to enter queries and track torrent status.

---

🗂 Project Structure

TORRENT_SEARCH/
│── backend/
│   ├── llmclient.py       # LLM client for query expansion (Perplexity API)
│   ├── middle.py          # Flask middleware between frontend ↔ backend tools
│   ├── server.py          # MCP server for torrent search + qBittorrent download
│   └── .env               # Environment variables (API keys, etc.)
│
│── frontend/
│   ├── src/
│   │   ├── App.jsx        # Main React UI logic
│   │   ├── App.css        # Styling
│   │   └── main.jsx       # Entry point
│   ├── public/
│   ├── package.json       # Frontend dependencies
│   └── vite.config.js     # Vite configuration
│
│── requirements.txt       # Python dependencies
│── README.md              # Project documentation

---

⚙️ Installation & Setup

1. Clone Repository

git clone https://github.com/Grind1503/Torrent-Downloader.git
cd torrent-search

2. Backend Setup

Create Virtual Environment (Use Python version 3.11)

cd backend
python -m venv venv
source venv/bin/activate   # On Linux/Mac
venv\Scripts\activate      # On Windows

Install Dependencies

pip install -r requirements.txt

Configure Environment

Create a .env file in backend/ with:

PERPLEXITY_API_KEY=your_perplexity_api_key_here

Run Servers

• Start MCP server (torrent search + download):

python server.py

• Start Flask middleware (LLM + MCP bridge):

python middle.py

By default:

• MCP server runs on http://localhost:8050
• Flask middleware runs on http://localhost:8000

---

3. Frontend Setup

cd frontend
npm install
npm run dev

Frontend will start on http://localhost:5173

---

4. qBittorrent Setup

• Install qBittorrent.
• Enable WebUI in preferences (default: http://localhost:8080).
• Configure credentials in server.py:
• Make sure the application runs in the background.

QB_URL = "http://localhost:8080"
QB_USER = "admin"
QB_PASS = "adminadmin"

---

🚀 Usage

1. Open the frontend (http://localhost:5173).
2. Enter a query like:
   • "Download all Marvel movies"
   • "Latest Ubuntu ISO"
   • "Call of Duty Modern Warfare"
3. LLM expands it into torrent-ready search terms.
4. MCP server searches 1337x, fetches magnet links, and sends them to qBittorrent.
5. UI shows status of downloads (success/failure).

---

🔮 Future Improvements

• Add support for multiple torrent sources beyond 1337x.
• User authentication for private use.
• Advanced filters (seeders, size, quality).
• Automatic notifications when downloads start/finish.

---

⚠️ Disclaimer

This project is for educational purposes only. Downloading copyrighted material without permission is illegal. The authors are not responsible for any misuse of this tool.