README.md

metadata

title: AI Radio - Personalized Radio Station
emoji: 🎵
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

🎵 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.

🦾 Participants

https://huggingface.co/nikimakarov

Social

https://x.com/nikimakarov/status/1995277463337967838

Demo on Youtube

https://youtu.be/tAIlor2TieM

🛠️ 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

🎵 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

🎙️ 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

   • 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/<username>/<space-name>

📖 Detailed deployment guide: See 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:

git clone <your-repo-url>
cd ai_radio

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables or edit src/config.py:

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:

python run.py

The app will be available at http://localhost:7871

5. Open your browser to http://localhost:7871

📖 How to Use

1️⃣ 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"

2️⃣ Start Your Radio

• Navigate to the "Radio Player" tab
• Click "▶️ Start Radio"
• Enjoy your personalized radio show!

3️⃣ Controls

• ▶️ Start Radio: Begin your personalized show
• ⏭️ Next Segment: Skip to the next segment
• ⏹️ Stop: Pause the radio (you can resume later)

4️⃣ 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:

# 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

🎯 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 ❤️ for the MCP 1st Birthday Competition Contact: @nikimakarov

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Track: MCP in Action - Consumer Applications
Tag: mcp-in-action-track-consumer

🎵 Enjoy your personalized radio experience! 🎵