--- title: AI Radio - Personalized Radio Station emoji: đŸŽĩ colorFrom: purple colorTo: pink sdk: gradio sdk_version: 4.44.1 app_file: app.py pinned: false license: mit short_description: Entertaining AI Radio with LLM as a Host tags: - mcp-in-action-track-consumer - mcp-in-action-track-creative --- # đŸŽĩ AI Radio - Your Personal Radio Station ## 🌟 Overview **AI Radio** is an intelligent, personalized radio station powered by cutting-edge AI technology. It creates a unique listening experience tailored to your preferences, mood, and interests. Built for the **MCP 1st Birthday Competition**, this app demonstrates autonomous agent behavior, MCP integration, and advanced RAG capabilities. ## đŸĻž Participants https://huggingface.co/nikimakarov ## Social https://x.com/nikimakarov/status/1995277463337967838 ## Demo on Youtube https://youtu.be/tAIlor2TieM ## 🛠ī¸ Technology Stack - **Gradio**: Interactive web interface - **OpenAI GPT-OSS-120B provided by Nebius** : LLM for content generation, host commentary, and reasoning - **ElevenLabs**: High-quality text-to-speech for voice generation - **Modal** Youtube-proxy service - **LlamaIndex**: RAG system for personalized recommendations and user preference management - **MCP (Model Context Protocol)**: Structured tool servers for modular functionality - **Python**: Core application logic - **RSS Feeds**: Real-time news aggregation ## 🙏 Acknowledgments - **Gradio Team** for the beautiful UI framework - **MCP Team** for the amazing protocol and competition - **OpenAI** for creating GPT-OSS-120B - **Modal** for providing servers for proxy - **Nebius** for providing models API - **ElevenLabs** for text-to-speech technology - **LlamaIndex** for RAG capabilities ## 📁 Project Structure ``` ai_radio/ ├── src/ # Source code │ ├── app.py # Main Gradio application │ ├── config.py # Configuration │ ├── radio_agent.py # AI agent logic │ ├── tts_service.py # Text-to-speech service │ ├── rag_system.py # RAG system │ ├── voice_input.py # Voice input handling │ └── mcp_servers/ # MCP server implementations ├── docs/ # Documentation files ├── audio/ # Generated audio files ├── logs/ # Log files ├── requirements.txt # Python dependencies └── run.py # Application entry point ``` ## ✨ Features ### đŸŽĩ Personalized Music - Curated tracks based on your favorite genres and current mood - Free music recommendations from multiple genres - Dynamic DJ commentary generated by AI ### 📰 Custom News Updates - Real-time news from RSS feeds - Personalized news based on your interests (technology, world, business, entertainment, science) - AI-generated news scripts delivered in a conversational style ### 🎙ī¸ Podcast Recommendations - Discover trending podcasts in various categories - Personalized recommendations based on your interests - Engaging introductions to each podcast ### 📖 AI-Generated Stories - Entertaining stories and fascinating fun facts - Tailored to your interests and mood - Perfect short-form content between segments ### 🤖 AI Radio Host - Charismatic AI host powered by Nebius GPT-OSS-120B - Personalized greetings and interactions - Smooth transitions between segments ### 💾 Smart Recommendations (RAG) - LlamaIndex-powered RAG system learns from your listening history - Improves recommendations over time - Stores your preferences and provides insights ## 🏆 MCP Competition Requirements This app fulfills all requirements for **Track 2: MCP in Action**: ✅ **Autonomous Agent Behavior**: The Radio Agent demonstrates planning (show planning), reasoning (segment selection based on preferences), and execution (content generation and delivery) ✅ **MCP Servers as Tools**: Implements three MCP servers: - **MusicMCPServer**: Music search and playlist generation - **NewsMCPServer**: News fetching and curation - **PodcastMCPServer**: Podcast discovery and recommendations ✅ **Gradio App**: Beautiful, intuitive radio station interface ✅ **Advanced Features**: - **RAG System**: Uses LlamaIndex for context-aware recommendations - **Context Engineering**: Personalizes content based on user preferences and history - **Streaming Capabilities**: Efficient audio generation and playback ## 🚀 Getting Started ### Option 1: Deploy to HuggingFace Spaces (Recommended) 🚀 **Quick Deploy:** 1. **Create a new Space** on [HuggingFace Spaces](https://huggingface.co/spaces) - SDK: **Gradio** - Hardware: **CPU** (or GPU if available) 2. **Configure Secrets** (Settings → Secrets): - `ELEVENLABS_API_KEY` - Your ElevenLabs API key - `NEBIUS_API_KEY` - Your Nebius API key (for GPT-OSS-120B) - `LLAMAINDEX_API_KEY` - Optional, for LlamaIndex embeddings - `OPENAI_API_KEY` - Optional, for OpenAI embeddings fallback 3. **Push your code** to the Space repository 4. **Access your Space** at `https://huggingface.co/spaces//` 📖 **Detailed deployment guide**: See [DEPLOY_HF.md](DEPLOY_HF.md) ### Option 2: Local Installation ### Prerequisites - Python 3.9+ - Nebius API key (for GPT-OSS-120B LLM) - ElevenLabs API key ### Installation 1. Clone the repository: ```bash git clone cd ai_radio ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Set up environment variables or edit `src/config.py`: ```bash export ELEVENLABS_API_KEY="your-key-here" export NEBIUS_API_KEY="your-key-here" export LLAMAINDEX_API_KEY="your-key-here" ``` 4. Run the app: ```bash python run.py ``` The app will be available at `http://localhost:7871` 5. Open your browser to `http://localhost:7871` ## 📖 How to Use ### 1ī¸âƒŖ Set Your Preferences - Go to the **"Your Preferences"** tab - Enter your name - Select your favorite music genres - Choose news topics you're interested in - Pick podcast categories - Set your current mood - Click **"Save Preferences"** ### 2ī¸âƒŖ Start Your Radio - Navigate to the **"Radio Player"** tab - Click **"â–ļī¸ Start Radio"** - Enjoy your personalized radio show! ### 3ī¸âƒŖ Controls - **â–ļī¸ Start Radio**: Begin your personalized show - **⏭ī¸ Next Segment**: Skip to the next segment - **⏚ī¸ Stop**: Pause the radio (you can resume later) ### 4ī¸âƒŖ Track Your Stats - Visit the **"Your Stats"** tab - See your listening history and statistics - The RAG system uses this data to improve recommendations ## 🎨 User Interface The app features a beautiful, modern radio station interface with: - **Gradient color schemes** for visual appeal - **Responsive layout** that works on all devices - **Real-time status updates** showing what's playing - **Progress tracking** through your show - **Audio player** with auto-play functionality ## 🧠 How It Works ### Agent Architecture The **RadioAgent** is an autonomous AI agent that: 1. **Plans**: Analyzes user preferences and creates a balanced show plan with music, news, podcasts, and stories 2. **Reasons**: Uses the RAG system to make intelligent decisions about content selection 3. **Executes**: Generates content using Nebius GPT-OSS-120B LLM and delivers it via ElevenLabs TTS ### MCP Server Architecture Three specialized MCP servers provide modular functionality: ```python # Music Server - search_music(genre, mood, limit) - get_personalized_playlist(user_preferences) # News Server - fetch_news(category, limit) - get_personalized_news(user_preferences) # Podcast Server - get_trending_podcasts(category, limit) - get_personalized_podcasts(user_preferences) ``` ### RAG System The RAG system powered by LlamaIndex: - Stores user preferences and listening history - Provides context-aware recommendations - Learns from user behavior over time - Supports semantic search over user data ## đŸŽ¯ Show Planning Algorithm The agent uses intelligent planning to create a balanced show: 1. **Analyze preferences**: Load user preferences from RAG system 2. **Calculate distribution**: Determine segment ratios (50% music, 20% news, 20% podcasts, 10% stories) 3. **Generate segments**: Use MCP servers to fetch content for each segment 4. **Add personality**: Generate host commentary using Nebius GPT-OSS-120B LLM 5. **Execute**: Convert text to speech and play audio ## 🔊 Audio Generation - **Text-to-Speech**: ElevenLabs API converts host commentary to natural-sounding speech - **Voice Selection**: Uses the "Rachel" voice by default (customizable) - **Streaming**: Efficient audio generation and playback - **Quality**: High-quality MP3 output ## 📊 Personalization The app learns from your behavior: - **Preference Storage**: Saves your favorite genres, interests, and moods - **History Tracking**: Records what you've listened to - **Recommendation Engine**: Uses RAG to suggest relevant content - **Adaptive Planning**: Adjusts future shows based on your history ## 🔒 Privacy - All user data is stored locally - No data is shared with third parties (except API calls to service providers) - You can clear your history at any time ## 🤝 Contributing This is a hackathon submission, but contributions and feedback are welcome! ## 📄 License MIT License - feel free to use and modify as needed. ## 📧 Contact Built with ❤ī¸ for the MCP 1st Birthday Competition Contact: @nikimakarov --- **Track**: MCP in Action - Consumer Applications **Tag**: `mcp-in-action-track-consumer` đŸŽĩ **Enjoy your personalized radio experience!** đŸŽĩ