Predicts crocodile conservation status using machine learning algorithms via a web application and terminal-based LLM interactions.
This ML-powered web application determines the conservation status of crocodiles, offering real-time predictions based on observed characteristics such as length, weight, scientific name, and habitat. It features a robust FastAPI backend, an interactive Streamlit frontend, and a trained machine learning model. Furthermore, it integrates with the Modular Control Protocol (MCP), enabling dynamic API capability discovery and real-time predictions through terminal-based LLM interactions.