Skip to content

README.md

Latest commit

 

History

History
227 lines (169 loc) · 7.08 KB

README.md

File metadata and controls

227 lines (169 loc) · 7.08 KB
Raw

AI Support Intelligence Hub

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

Streamlit App Python Plotly Streamlit Cloud

🌟 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:

  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

Made with ❤️ using Streamlit and AI

GitHub stars GitHub forks