🎫 AI Ticket Classification Dashboard

A comprehensive Databricks project that demonstrates the power of AI functions (ai_classify, ai_extract, ai_gen) for intelligent ticket classification and business insights generation. Features a stunning Streamlit dashboard with interactive visualizations and real-time data analysis.

✨ Features

🤖 AI-Powered Analysis

• AI Classification: Automatically classify ticket priorities using ai_classify
• AI Extraction: Extract structured data (action items, requirements, urgency) using ai_extract
• AI Generation: Generate comprehensive analysis and recommendations using ai_gen

📊 Interactive Dashboard

• Beautiful UI: Modern Streamlit interface with gradient headers and animations
• Interactive Charts: Plotly visualizations (pie, bar, scatter, gauge, donut charts)
• Real-time Metrics: Animated metric cards with business KPIs
• System Health: Gauge charts and health indicators
• Resource Allocation: Scatter plots and priority analysis

🏗️ Databricks Integration

• Unity Catalog: Seamless data storage and retrieval
• Job Workflows: Automated data processing pipeline
• SQL Warehouse: Real-time data connectivity
• Asset Bundles: Production-ready deployment configuration

🚀 Quick Start

Prerequisites

• Databricks workspace with AI functions enabled
• Python 3.8+ with pip
• Databricks CLI configured

Local Development

1. Clone and setup:

   git clone <repository-url>
   cd databricks_classify_tickets
   pip install -r requirements.txt

2. Configure Databricks:

   databricks configure --profile DEFAULT

3. Run the Streamlit dashboard:

   python run_local.py

   Open your browser to: http://localhost:8501

Databricks Deployment

1. Deploy the job:

   databricks bundle deploy --target dev

2. Run the workflow:

   databricks bundle run

3. Deploy the Streamlit app:

   databricks bundle deploy --target dev

📁 Project Structure

databricks_classify_tickets/
├── src/databricks_classify_tickets/
│   ├── 01_sample_data_generation.ipynb    # Generate realistic ticket data
│   ├── 02_ai_showcase.ipynb               # AI functions demonstration
│   ├── 03_business_insights.ipynb         # Business analysis and KPIs
│   └── config.py                          # Unity Catalog configuration
├── resources/
│   └── databricks_classify_tickets.job.yml # Job workflow definition
├── app.py                                 # Streamlit dashboard
├── app.yaml                              # Streamlit app configuration
├── run_local.py                          # Local development script
├── requirements.txt                      # Python dependencies
└── databricks.yml                        # Asset bundle configuration

🔧 Configuration

Environment Variables

• DATABRICKS_WAREHOUSE_ID: SQL Warehouse ID (default: 148ccb90800933a1)
• DATABRICKS_CONFIG_PROFILE: Databricks profile (default: DEFAULT)
• STREAMLIT_GATHER_USAGE_STATS: Disable usage tracking (default: false)

Unity Catalog Tables

• ai_showcase_results: Main AI analysis results
• dashboard_priority_distribution: Priority analysis data
• dashboard_system_health: System health metrics
• dashboard_resource_allocation: Resource allocation recommendations

📊 Dashboard Features

🤖 AI Analysis Results

• Interactive priority distribution charts
• Real-time ticket metrics
• Action items extraction
• Urgency level analysis

📈 Business Insights

• Priority distribution analysis
• System health monitoring
• Resource allocation recommendations
• Executive summary generation

🎨 Visual Enhancements

• Gradient headers and animated cards
• Interactive Plotly visualizations
• Color-coded alerts and status indicators
• Smooth loading animations
• Professional styling

🛠️ Development

Running Tests

# Run the complete workflow
databricks bundle run

# Test individual notebooks
# Open in Databricks workspace and run cells

Customizing the Dashboard

• Modify app.py for UI changes
• Update config.py for table configurations
• Add new visualizations in display functions

📚 Documentation

• Streamlit Dashboard Guide
• Deployment Guide
• Databricks Asset Bundles
• Streamlit Documentation

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Databricks AI Functions team
• Streamlit community
• Plotly for interactive visualizations