A real-time dashboard that displays customer happiness levels by region and uses an NVIDIA Nemotron multi-step agent to diagnose issues and auto-generate resolution recommendations.

• 🗺️ Interactive Map Visualization: Heatmap showing customer happiness scores by US region
• 📊 Real-time Sentiment Analysis: Aggregated sentiment data from multiple sources
• 🤖 NVIDIA Nemotron Agent: Multi-step workflow for diagnosing issues and generating recommendations
• 📱 Responsive Dashboard: Dark, minimal UI with real-time updates
• 🔄 Auto-refresh: Data updates every 30 seconds
• 📈 Trend Analysis: Track positive and negative sentiment spikes

• Frontend: Next.js 14 + React + TypeScript
• Styling: TailwindCSS
• Map: Leaflet.js + React-Leaflet
• Backend: Next.js API Routes
• Database: Supabase (PostgreSQL)
• LLM Agent: NVIDIA Nemotron (simulated with rule-based logic)
• Sentiment Analysis: HuggingFace API (with fallback)

• Node.js 18+ and npm
• Supabase account and project
• (Optional) HuggingFace API key for sentiment analysis
• (Optional) NVIDIA API key for Nemotron integration

1. Clone and install dependencies:

   npm install

2. Set up environment variables:

   cp .env.local.example .env.local

   Edit .env.local and add your credentials:

   NEXT_PUBLIC_SUPABASE_URL=your_supabase_project_url
   NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
   SUPABASE_SERVICE_ROLE_KEY=your_supabase_service_role_key
   
   # Optional
   HUGGINGFACE_API_KEY=your_huggingface_api_key
   NVIDIA_API_KEY=your_nvidia_api_key
   NVIDIA_BASE_URL=https://integrate.api.nvidia.com/v1

3. Set up Supabase database:

   • Create a new Supabase project
   • Run the migration script from supabase/migrations/001_initial_schema.sql in your Supabase SQL editor
   • Or use Supabase CLI: supabase db push

4. Seed the database (optional):

   npm run seed

5. Run the development server:

   npm run dev

6. Open your browser: Navigate to http://localhost:3000

SentimentRadar/
├── app/                    # Next.js app directory
│   ├── api/               # API routes
│   │   ├── diagnose/      # Nemotron agent endpoint
│   │   ├── snapshots/     # Sentiment data endpoint
│   │   └── publish-slack/ # Slack integration (simulated)
│   ├── globals.css        # Global styles
│   ├── layout.tsx         # Root layout
│   └── page.tsx           # Main dashboard page
├── components/            # React components
│   ├── Dashboard.tsx      # Main dashboard component
│   ├── HappinessMap.tsx   # Map visualization
│   ├── Sidebar.tsx        # Sentiment trends sidebar
│   └── DiagnosisPanel.tsx # Diagnosis results modal
├── lib/                   # Utility libraries
│   ├── supabase.ts        # Supabase client setup
│   ├── sentiment.ts       # Sentiment analysis service
│   └── nemotron-agent.ts  # Nemotron agent workflow
├── scripts/               # Utility scripts
│   └── seed.ts            # Database seeding script
└── supabase/              # Database migrations
    └── migrations/
        └── 001_initial_schema.sql

• id (uuid, primary key)
• region (text)
• happiness_score (float, 0-100)
• sentiment_summary (text)
• outage_level (integer, 0-100)
• created_at (timestamp)

• id (uuid, primary key)
• region (text)
• cause (text)
• internal_message (text)
• customer_message (text)
• created_at (timestamp)

1. Click on any region marker on the map
2. Click "Run Diagnosis" in the popup
3. The Nemotron agent will:
   • Retrieve latest sentiment data
   • Analyze trends
   • Identify root causes
   • Generate internal and customer-facing messages
4. Review the results in the diagnosis panel
5. Optionally publish to Slack (simulated)

• The sidebar shows:
  • Overall happiness score
  • Top 5 negative sentiment spikes
  • Top 5 positive moments
• Data refreshes automatically every 30 seconds
• Click the refresh button to manually update

The agent implements a 5-step workflow:

1. Retrieve Data: Fetches latest sentiment and outage data from Supabase
2. Analyze Trend: Determines if happiness is rising, falling, or stable
3. Identify Cause: If happiness drops below threshold, identifies probable causes
4. Generate Internal Message: Creates detailed resolution message for internal teams
5. Generate Customer Message: Creates customer-friendly status update

Edit lib/nemotron-agent.ts and uncomment the callNemotronAPI function implementation. Replace the simulated responses with actual API calls:

const response = await fetch(`${process.env.NVIDIA_BASE_URL}/chat/completions`, {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.NVIDIA_API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'nemotron-4-340b-instruct',
    messages: [
      { role: 'system', content: systemPrompt },
      { role: 'user', content: prompt }
    ],
  }),
})

To integrate real Twitter/X, Reddit, or outage data:

1. Create new API routes in app/api/
2. Implement data fetching logic
3. Use lib/sentiment.ts to analyze sentiment
4. Insert results into sentiment_snapshots table

# Run development server
npm run dev

# Build for production
npm run build

# Start production server
npm start

# Run linter
npm run lint

# Seed database
npm run seed

MIT

Contributions are welcome! Please feel free to submit a Pull Request.