🎫 AI-Powered Ticket Classification System

An intelligent support ticket routing system that uses machine learning to automatically classify and assign support tickets to appropriate agent groups.

🌟 Features

• Automated Processing: Integrates with ticketing systems via REST API
• Web Interface: Interactive Gradio-based demo for testing predictions
• Flexible Architecture: Easily adaptable to different ticketing platforms
• Production Ready: Includes error handling, logging, and configuration management

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/Juno445/ticket-classification-model.git
   cd ticket-classification-model

2. Install dependencies

   pip install -r requirements.txt

3. Prepare your data

   • Create inputdata.csv with columns: subject, agent_group, priority
   • Create workspace_agent_groups.csv with columns: group_name, group_id, workspace_id

4. Set up environment variables

   cp .env.example .env
   # Edit .env with your configuration

📊 Usage

1. Data Preparation

Split your dataset into training and testing sets:

python data_splitter.py

This creates train.csv and test.csv from your input data with an 80/20 split.

2. Model Training

Train the multi-task neural network:

python train_model.py

This will:

• Process and vectorize ticket subjects using TF-IDF
• Train a neural network with two output heads (agent group + priority)
• Save the trained model and preprocessing components
• Use early stopping to prevent overfitting

Training Output:

Using device: cuda
Loaded 10000 records from inputdata.csv
Training samples: 8000, Validation samples: 2000
Model initialized with 3,276,842 parameters
Starting training...
Epoch 1/50, Training Loss: 2.1543, Validation Loss: 1.8932
...
New best model saved to ticket_multitask_model.pth
Training completed!

3. Interactive Testing

Launch the web interface to test predictions:

python gradio_demo.py

Open your browser to http://localhost:7860 and test the model with sample ticket subjects.

4. Production Deployment

Run the automated ticket processing system:

python main.py

This will:

• Connect to your ticketing system API
• Fetch unassigned tickets
• Predict appropriate assignments using the trained model
• Automatically update ticket assignments
• Add explanatory notes to processed tickets

🏗️ Architecture

Model Architecture

Input: Ticket Subject (Text)
    ↓
TF-IDF Vectorization (25,000 features)
    ↓
Neural Network:
├── Dense Layer (25,000 → 128) + ReLU
├── Dense Layer (128 → 64) + ReLU
├── Agent Head (64 → num_agent_groups)
└── Priority Head (64 → num_priorities)
    ↓
Outputs: Agent Group

System Components

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Data Splitter │    │  Model Training  │    │  Gradio Demo    │
│                 │    │                  │    │                 │
│ • Train/Test    │    │ • Multi-task NN  │    │ • Web Interface │
│   Split         │    │ • TF-IDF         │    │ • Live Testing  │
│ • 80/20 Ratio   │    │ • Early Stop     │    │ • Confidence    │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌──────────────────┐
                    │  Main System     │
                    │                  │
                    │ • API Integration│
                    │ • Auto-Assignment│
                    │ • Batch Process  │
                    └──────────────────┘

📁 Project Structure

ticket-classification-model/
├── data_splitter.py          # Split dataset into train/test
├── train_model.py            # Train the multi-task model
├── main.py                   # Production ticket processing
├── gradio_demo.py            # Interactive web interface
├── requirements.txt          # Python dependencies
├── .env.example             # Environment variables template
├── .gitignore               # Git ignore rules
└── README.md                # This file

# Generated files (not in repo)
├── inputdata.csv            # Your training data
├── train.csv               # Training split
├── test.csv                # Testing split
├── workspace_agent_groups.csv  # Group/workspace mappings
├── vectorizer.pkl          # Trained TF-IDF vectorizer
├── agent_encoder.pkl       # Agent group label encoder
├── priority_encoder.pkl    # Priority label encoder
├── ticket_multitask_model.pth  # Trained model weights
└── .env                    # Your configuration (keep secret!)

⚙️ Configuration

Environment Variables

Create a .env file with your settings:

# API Configuration
FRESHSERVICE_API_KEY=your_api_key_here
FRESHSERVICE_DOMAIN=your_domain_here

# Contact Information
CONTACT_EMAIL=[email protected]
CONTACT_NAME=IT Support Team

# Processing Settings
SOURCE_TAG=auto-classify
PROCESSED_TAG=auto-classified
DEFAULT_WORKSPACE_ID=1

# Model Settings
MAX_FEATURES=25000
BATCH_SIZE=10
LEARNING_RATE=0.0005
EPOCHS=50

Data Format

Training Data (inputdata.csv):

subject,agent_group,priority
"Computer won't start",Hardware Support,High
"Password reset needed",IT Helpdesk,Medium
"New software request",Software Team,Low

Group Mappings (workspace_agent_groups.csv):

group_name,group_id,workspace_id
Hardware Support,101,1
IT Helpdesk,102,1
Software Team,103,2

🔧 API Integration

The system supports integration with ticketing platforms through REST APIs. Currently includes:

• Freshservice: Full integration with ticket fetching and updating
• Generic REST API: Easily adaptable to other platforms

Adding New Integrations

1. Implement API client in main.py
2. Update configuration for your platform
3. Modify ticket processing logic as needed

📈 Model Performance

Training Metrics

• Multi-task Loss: Combined cross-entropy for both tasks
• Early Stopping: Prevents overfitting with patience mechanism
• Validation Split: 20% of data reserved for validation

Evaluation

Use the Gradio interface to:

• Test edge cases and unusual ticket subjects
• Evaluate prediction confidence scores
• Compare against human assignments

🛠️ Development

Adding New Features

1. New Prediction Tasks: Extend the model architecture

Testing

# Test individual components
python -c "from train_model import load_model_components; print('✓ Model loads correctly')"

# Validate data format
python -c "import pandas as pd; df = pd.read_csv('inputdata.csv'); print(f'✓ Data shape: {df.shape}')"

🚨 Security Considerations

• API Keys: Never commit .env files or hardcode credentials
• Data Privacy: Ensure training data doesn't contain sensitive information
• Access Control: Limit API permissions to minimum required scope
• Logging: Avoid logging sensitive ticket content

🤝 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

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

📋 Requirements

Core Dependencies

torch>=2.0.0
scikit-learn>=1.3.0
pandas>=2.0.0
numpy>=1.24.0
requests>=2.31.0
python-dotenv>=1.0.0
gradio>=4.0.0

Optional Dependencies

# For development
pytest>=7.0.0
black>=23.0.0
flake8>=6.0.0

# For enhanced features
transformers>=4.30.0  # For BERT-based models
plotly>=5.0.0        # For training visualizations

📊 Example Results

Sample Predictions

Ticket Subject	Predicted Group	Predicted Priority	Confidence
"Server is down - urgent!"	Infrastructure	High	94%
"Password reset request"	IT Helpdesk	Medium	87%
"New laptop setup needed"	Hardware Support	Low	91%

Performance Metrics

• Agent Group Accuracy: ~85-90% on validation set
• Priority Accuracy: ~80-85% on validation set
• Processing Speed: ~100 tickets/minute
• API Response Time: <2 seconds per ticket

🐛 Troubleshooting

Common Issues

ModuleNotFoundError: Install missing dependencies

pip install -r requirements.txt

CUDA out of memory: Reduce batch size in configuration

CONFIG['batch_size'] = 5  # Reduce from default 10

API authentication errors: Check your .env file

# Verify API key is correct
curl -u "your_api_key:X" https://yourdomain.freshservice.com/api/v2/tickets

Model file not found: Train the model first

python train_model.py

📜 License

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

🙏 Acknowledgments

• Built with PyTorch for deep learning
• Gradio for the interactive interface
• scikit-learn for preprocessing utilities

📞 Support

• 📧 Email: [email protected]
• 💬 Issues: GitHub Issues
• 📖 Documentation: Wiki

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