🌌 Orbit: AI-Powered Conversation Intelligence Platform

Real-time facial recognition, conversation tracking, and intelligent networking for the modern world.

Orbit is a comprehensive AI platform that bridges physical conversations with digital intelligence. It uses computer vision, natural language processing, and web search to automatically identify people in real-time, track conversation topics, and build rich professional profiles.

🎯 What Orbit Does

Real-Time Intelligence

👁️ Live Facial Recognition: Identify people in your camera feed instantly
🎙️ Conversation Recording: Automatic audio capture and transcription
🤖 Topic Extraction: AI-powered analysis of what you discussed
💾 Smart Caching: Automatic profile building and conversation history

Conversation-Driven Networking

📝 Automatic Logging: Every person who appears in frame gets logged
🔗 Topic Linking: Conversation topics automatically added to participant profiles
📊 Rich Profiles: Professional info, education, achievements, and social media
🌐 Web Search Integration: Automatic research on unknown individuals

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │    Backend      │    │   AI Services   │
│                 │    │                 │    │                 │
│ • React UI      │◄──►│ • Face Recognition │◄──►│ • Cerebras LLM  │
│ • Real-time Feed│    │ • Audio Recording │    │ • Face Search   │
│ • Profile Views │    │ • Web Search      │    │ • Web Scraping  │
│ • Conversation  │    │ • Cache System    │    │ • Topic Analysis│
│   Management    │    │ • Integration     │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Core Components

🖥️ Frontend (Next.js)

Live Video Feed: Real-time webcam with face detection overlays
Person Profiles: Rich detailed views with professional info and conversation history
Conversation Management: Track topics and link to participants
Network Visualization: Interactive graph of connections and relationships

⚙️ Backend (Python)

Facial Recognition: InsightFace + DeepFace for accurate identification
Audio Pipeline: Groq Whisper for transcription + LLM topic extraction
Web Intelligence: Face search + content scraping + AI analysis
Cache System: Persistent storage of profiles and conversation data

🧠 AI Layer

LLM Integration: Cerebras GPT-OSS-120B for fast, accurate analysis
Topic Extraction: Automatic identification of 3-5 conversation topics
Profile Building: Structured data extraction from web sources
Smart Matching: Confidence scoring and verification

🚀 Quick Start

Prerequisites

Python 3.8+ and Node.js 18+
Webcam and microphone
API keys (see setup below)

1. Clone and Setup

git clone <repository-url>
cd Orbit

# Backend setup
cd backend
pip install -r requirements.txt
cp env.example .env
# Edit .env with your API keys

# Frontend setup  
cd ../frontend
npm install

2. Configure API Keys

Edit backend/.env:

# Required for facial recognition
FACECHECK_API_TOKEN=your_token

# Required for web search  
SERPAPI_KEY=your_key

# Required for AI analysis
CEREBRAS_KEY=your_key

# Optional for audio transcription
GROQ_API_KEY=your_key

3. Start the System

# Terminal 1: Backend
cd backend
python server.py

# Terminal 2: Frontend  
cd frontend
npm run dev

4. Access the Application

Frontend: http://localhost:3000
Backend API: http://localhost:8000

🎬 How It Works

The Conversation Flow

🎥 Start Camera Session

Camera starts → Frame presence tracking begins
Audio recording starts → Real-time transcription

👤 Person Detection

Face detected → Recognition attempt → Profile lookup
Unknown person → Web search → Profile creation
Known person → Load existing profile → Update presence

🗣️ Conversation Capture

Audio captured → Groq Whisper transcription
Text analyzed → LLM topic extraction (3-5 topics)

🔗 Automatic Integration

Session ends → Find all participants in frame
Extract topics → Update each participant's cache
Link conversation data → Build conversation history

Example Workflow

📹 Camera Session: "Discussion about AI and startups"

👥 Participants Detected:
   • John Smith (recognized from cache)
   • Jane Doe (unknown → web search → profile created)

🎙️ Audio Transcribed:
   "We talked about the future of AI startups and funding trends..."

🤖 Topics Extracted:
   • AI Startups
   • Funding Trends  
   • Future Technology

💾 Cache Updated:
   • John Smith's profile ← conversation topics added
   • Jane Doe's profile ← conversation topics added

📁 Project Structure

Orbit/
├── backend/                    # Python backend services
│   ├── facial_recognition/     # Face detection & recognition
│   ├── recording/             # Audio capture & transcription  
│   ├── search/                # Web search & content scraping
│   ├── llm/                   # AI analysis & topic extraction
│   ├── cache/                 # Person profiles & images
│   ├── logs/                  # Session & integration logs
│   ├── conversation_integration.py # Core integration logic
│   └── server.py              # FastAPI backend server
│
├── frontend/                  # Next.js React frontend
│   ├── src/components/        # UI components
│   │   ├── main/             # Camera feed & detection
│   │   ├── profile/          # Person profiles & details
│   │   └── layout/           # Navigation & layout
│   ├── src/data/             # Data adapters & utilities
│   └── src/utils/            # Helper functions
│
└── README.md                  # This file

🎛️ Key Features

🔍 Advanced Recognition

Multi-Modal Matching: Face vectors + photo comparison + DeepFace verification
Confidence Scoring: Multiple similarity metrics for accurate identification
Unknown Person Handling: Automatic web search and profile creation
Cache Learning: System improves recognition over time

🎙️ Intelligent Audio

Real-Time Transcription: Groq Whisper Large Turbo for accuracy
Topic Extraction: LLM analysis of conversation content
Multi-Language Support: Automatic language detection
Conversation Linking: Topics automatically linked to all participants

🌐 Web Intelligence

Face Search: FaceCheck.id integration for photo matching
Content Scraping: Comprehensive web data extraction
Profile Building: Structured data with professional info, education, achievements
Source Verification: Quality scoring and credibility indicators

💾 Smart Caching

Persistent Profiles: Rich JSON profiles with conversation history
Incremental Updates: Profiles grow with each conversation
Frame Presence Tracking: Detailed logs of who appeared when
Integration Logs: Complete audit trail of all processing

🛠️ Configuration

Recognition Settings

# In facial_recognition/webcam_recognition.py
recognition_threshold = 0.7        # Face matching confidence
track_timeout = 1.5               # Seconds before "left" event
unknown_person_timeout = 5.0      # Delay before unknown person search

Audio Settings

# In recording/transcriber.py
model = "whisper-large-v3-turbo"  # Transcription model
auto_summarize = True             # Enable topic extraction
temperature = 0.3                 # LLM creativity setting

Search Settings

# In search/modules/
min_score = 85                    # Face search confidence threshold
max_face_results = 3              # Number of face matches to process
max_serp_per_url = 2             # Web search results per URL

📊 Data Flow

Cache File Structure

{
  "person_analysis": {
    "personal_info": {
      "full_name": "John Smith",
      "location": "San Francisco, CA",
      "interests": ["AI", "Technology"]
    },
    "professional_info": {
      "current_position": "Senior Engineer",
      "company": "TechCorp",
      "previous_positions": [...]
    },
    "conversation_history": [
      {
        "date": "2025-09-14T03:55:47",
        "topics": ["AI Startups", "Funding", "Technology"],
        "duration": 120,
        "presence_time": 95
      }
    ]
  }
}

Session Logs

{
  "session_metadata": {
    "session_id": "20250914_035534",
    "total_unique_participants": 2,
    "duration_seconds": 120
  },
  "participants_summary": {
    "1": {
      "name": "John Smith",
      "recognition_status": "recognized",
      "total_presence_time": 95.2
    }
  }
}

🔧 Development

Adding New Features

Backend: Add endpoints in server.py, implement logic in modules
Frontend: Create components in src/components/, add routes in app/
Integration: Update conversation_integration.py for new data flows

Testing

# Backend tests
cd backend
python -m pytest

# Frontend tests  
cd frontend
npm test

# Integration testing
python conversation_integration.py

API Documentation

Backend API docs: http://localhost:8000/docs (FastAPI auto-generated)
Key endpoints: /webcam/start, /webcam/stop, /cache/list, /voice/summarize

🚨 Troubleshooting

Issue	Solution
Camera not detected	Check permissions, try different camera index
Face recognition failing	Verify lighting, face angle, update recognition threshold
Audio not recording	Check microphone permissions, verify audio device
LLM errors	Verify API keys, check rate limits
Frontend not loading	Ensure backend is running, check CORS settings

🎯 Use Cases

Professional Networking

Conferences & Events: Automatically track who you meet and what you discuss
Business Meetings: Build conversation history with clients and partners
Networking Events: Never forget a name or conversation topic again

Personal Memory

Social Gatherings: Remember conversations with friends and family
Learning Sessions: Track discussion topics in study groups or workshops
Community Building: Build rich profiles of community members

Research & Analysis

Interview Studies: Systematic tracking of research conversations
Market Research: Capture insights from customer conversations
Content Creation: Track topics and sources for content development

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

Fork the repository
Create a feature branch
Make your changes with tests
Submit a pull request

Key areas for contribution:

New recognition algorithms
Additional LLM providers
Frontend UI improvements
Mobile app development
Integration with CRM systems

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

InsightFace: Face recognition models
Groq: Fast audio transcription
Cerebras: Ultra-fast LLM inference
FaceCheck.id: Face search capabilities
Next.js & React: Frontend framework
FastAPI: Backend API framework

🌌 Orbit: Where conversations become intelligence.

Built with ❤️ for the future of human connection.

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