project-structure.txt

customer-support-chatbot/
├── data/                    # Data folder for storing datasets
│   ├── raw/                 # Original, unprocessed datasets
│   └── processed/           # Preprocessed datasets
├── models/                  # Directory for saving fine-tuned models
├── src/                     # Source code directory
│   ├── preprocess.py        # Data preprocessing scripts
│   ├── train.py             # Fine-tuning script for the model
│   ├── api.py               # FastAPI app for real-time chatbot API
│   ├── chatbot.py           # Core chatbot logic (loading model, handling queries)
│   ├── deploy_sagemaker.py  # Script for AWS SageMaker deployment
│   └── config.py            # Configuration file for hyperparameters
├── tests/                   # Unit and integration tests
│   └── test_api.py          # Test script for API responses
├── docker/                  # Docker setup for containerized deployment
│   ├── Dockerfile           # Dockerfile for building the chatbot image
│   └── docker-compose.yml   # Optional docker-compose setup for local testing
├── mlflow/                  # MLflow setup for model tracking
│   └── mlflow_config.yml    # MLflow configuration file
├── .github/                 # GitHub Actions workflows for CI/CD
│   └── workflows/
│       └── deployment.yml   # CI/CD pipeline for testing, building, and deploying
├── requirements.txt         # Python dependencies
└── README.md                # Documentation