AquaID: An AI-Powered Web Application for Underwater Animal Species Identification Using the Gemini Vision-Language Model

AquaID is a full-stack web application that identifies underwater and aquatic animals from a user-uploaded photograph or short video clip and returns a structured species profile — common name, scientific name, full taxonomic classification, habitat, diet, typical size, lifespan, conservation status, danger-to-humans notes, fun facts, an honestly reported confidence score, and visually similar "look-alike" species where relevant. Unlike conventional underwater species-recognition systems that train or fine-tune a dedicated convolutional or transformer-based classifier on a curated, labelled underwater image dataset, AquaID performs no model training and uses no training dataset of its own: identification is delegated entirely to Google's Gemini 2.5 Pro multimodal vision-language model through a carefully engineered, schema-constrained prompt, accessed over the Gemini API. The application additionally supports optional free-text context from the user (e.g. location, approximate size, behavior observed), video understanding via Gemini's File API, a conversational follow-up assistant grounded in the specific uploaded media and its identification result, automatic detection and graceful rejection of non-aquatic-animal uploads, and a conservation-reporting module that lists real, verifiable government and international wildlife bodies (e.g. IUCN, CITES, national wildlife authorities) relevant to the identified species together with a live map search for the nearest office. User authentication is handled with hashed credentials and session-protected routes. This paper describes the system's architecture, the prompt-engineering methodology used in place of model training, and the design choices made to keep the system both training-data-free and resistant to confident misidentification.