Chatbot Conversation Classifier

This project implements an LLM-based classifier that analyzes chatbot conversations to determine sentiment and context. It provides a FastAPI-based REST API for easy integration.

Features

• Sentiment analysis of chatbot conversations
• Context classification
• REST API endpoints for classification
• Docker support for easy deployment
• FastAPI for high-performance API serving

Project Structure

.
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── models.py
│   ├── classifier.py
│   └── utils.py
├── tests/
│   └── test_classifier.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── README.md

Installation

1. Clone the repository:

git clone <repository-url>
cd chatbot-conversation-classifier

2. Install dependencies:

pip install -r requirements.txt

3. Run with Docker:

docker-compose up --build

API Usage

The API provides the following endpoints:

POST /classify

Classifies a chatbot conversation based on sentiment and context.

Request body:

{
    "conversation": [
        {"role": "user", "content": "Hello, how are you?"},
        {"role": "assistant", "content": "I'm doing well, thank you!"}
    ]
}

Response:

{
    "sentiment": "positive",
    "context": "greeting",
    "confidence_score": 0.95
}

Development

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install development dependencies:

pip install -r requirements.txt

3. Run tests:

pytest tests/

License

MIT License