AI Support Intelligence Hub

🚀 Advanced AI-powered support analytics dashboard with premium Figma-style design and agent building capabilities

🌟 Features

🤖 AI-Powered Analytics

• Smart Ticket Analysis: Automatic priority, sentiment, and category classification
• Bulk Processing: Analyze multiple tickets simultaneously with AI insights
• Historical Insights: Trend analysis and pattern recognition from past tickets

🎨 Premium Figma-Style Design

• Glassmorphism Effects: Modern, translucent UI elements with blur effects
• Gradient Themes: Professional color schemes with smooth transitions
• Dark Mode Support: Complete theme switching with CSS-in-JS implementation
• Responsive Design: Optimized for all screen sizes

🤖 Agent Builder

• Custom Agent Creation: Build specialized AI support agents
• Tool Integration: Configure multiple LLM models and tools
• JSON Configuration: Export agent configurations for external use
• Goal-Based Building: Define specific agent purposes and functions

📊 Advanced Analytics

• Real-time Metrics: Live dashboard with key performance indicators
• Interactive Charts: Plotly-powered visualizations with drill-down capabilities
• Priority Distribution: Visual breakdown of ticket urgency levels
• Category Analysis: Department-wise ticket categorization

🔧 Professional Features

• CSV Import/Export: Seamless data integration and backup
• Sample Data Seeding: Quick start with realistic test data
• Search & Filter: Advanced filtering by priority, category, and keywords
• Escalation Alerts: Automatic highlighting of urgent tickets

🚀 Quick Start

Local Development

1. Clone the repository:

   git clone https://github.com/joicyroslin-svg/ai-support-intelligence-hub.git
   cd ai-support-intelligence-hub

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   cp .env.example .env
   # Edit .env with your API keys

4. Run the dashboard:

   streamlit run ai_support_dashboard.py

5. Access the application: Open http://localhost:8501 in your browser

Live Demo

🌐 View Live Demo

📋 Requirements

• Python 3.9+
• Streamlit 1.35+
• Plotly 5.17+
• LiteLLM 1.72+
• Pandas, NumPy, Requests

See requirements.txt for the complete list of dependencies.

🏗️ Architecture

ai-support-intelligence-hub/
├── ai_support_dashboard.py      # Main Streamlit application
├── assistant/                   # Core AI functionality
│   ├── agent.py                # Agent orchestration
│   ├── build_agent.py          # Agent builder logic
│   ├── config.py               # Configuration management
│   ├── llm.py                  # LLM client abstraction
│   ├── rag.py                  # Retrieval-Augmented Generation
│   ├── service.py              # Core service functions
│   └── ui_theme.py             # Theme management
├── data/                       # Sample data
│   └── sample_tickets.csv      # Example ticket data
├── tests/                      # Unit tests
└── requirements.txt            # Python dependencies

🎯 Usage

1. Dashboard Overview

• View real-time analytics and key metrics
• Monitor ticket volume, sentiment trends, and priority distribution
• Access escalation alerts for urgent tickets

2. Ticket Management

• Upload CSV files for bulk ticket analysis
• Filter tickets by priority, category, and keywords
• View tickets in table or chat format
• Bulk analyze filtered tickets with AI

3. Single Ticket Analysis

• Analyze individual tickets with AI
• Get priority, sentiment, and category classification
• Generate AI responses with different tones
• View similar historical tickets

4. Agent Builder

• Create custom AI support agents
• Configure tools and goals
• Export agent configurations as JSON
• Integrate with external systems

🔧 Configuration

Environment Variables

Create a .env file with the following variables:

# LLM Configuration
OPENAI_API_KEY=your_openai_key_here
GEMINI_API_KEY=your_gemini_key_here
ANTHROPIC_API_KEY=your_claude_key_here

# Application Settings
DEFAULT_MODEL=gpt-4o-mini
MAX_OUTPUT_TOKENS=1000
TEMPERATURE=0.2

Customization

• Theme Colors: Modify CSS variables in assistant/ui_theme.py
• Default Models: Update DEFAULT_MODEL in assistant/config.py
• Analysis Settings: Adjust parameters in assistant/service.py

🧪 Testing

Run the test suite:

pytest

Run specific tests:

pytest tests/test_service.py
pytest tests/test_llm.py

🚀 Deployment

Streamlit Cloud (Recommended)

1. Push to GitHub:

   git add .
   git commit -m "Ready for deployment"
   git push origin main

2. Deploy to Streamlit Cloud:

   • Go to https://streamlit.io/cloud
   • Connect your GitHub repository
   • Select ai_support_dashboard.py as the main file
   • Deploy!

3. Your app will be live at: https://your-username-ai-support-hub.streamlit.app

Docker Deployment

# Build the image
docker build -t ai-support-hub .

# Run the container
docker run -p 8501:8501 ai-support-hub

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• Streamlit - For the amazing web framework
• Plotly - For beautiful interactive charts
• LiteLLM - For unified LLM API access
• Figma - For design inspiration

📞 Support

• Issues: GitHub Issues
• Documentation: Wiki
• Email: joicyroslin@example.com

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Made with ❤️ using Streamlit and AI