Cat Breed Detection

This AI-powered web app uses Flask and TensorFlow to identify a cat's breed from an uploaded image. Supporting eight distinct breeds, it instantly provides predictions alongside confidence scores, intelligently prompting users for a clearer photo if the model's certainty falls below a built-in threshold.

The Cat Breed Detector is an interactive, AI-driven web application built with Python, Flask, and TensorFlow that seamlessly identifies a cat’s breed from a user-uploaded image. Designed with a clean, intuitive interface, the app allows users to simply upload a photo, which is automatically processed and analyzed by a custom-trained deep learning model capable of recognizing eight distinct breeds, including Bengal, Persian, and Sphynx. Upon analysis, the application provides the user with the most likely breed alongside a percentage-based confidence score to ensure transparency. To maintain accuracy and a high-quality user experience, the system incorporates a built-in confidence threshold; if an image is too blurry, unreadable, or not of a cat, the app gracefully rejects the low-confidence prediction and prompts the user to upload a clearer picture. By bridging the gap between complex machine learning and accessible web design, this project transforms a raw TensorFlow model into a practical, real-world tool.

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