Project Description:

The Fruit Classification CNN Model is a deep learning web application designed to identify and classify different types of fruits from uploaded images. Users can upload an image of a fruit, and the app instantly predicts which fruit it is along with a confidence score.

This project demonstrates how computer vision and convolutional neural networks (CNNs) can be used to solve real-world image classification problems. The model was trained on a dataset of fruit images, learning to recognize visual patterns such as color, shape, and texture that distinguish each fruit category.

The app is built with Streamlit, providing an interactive and user-friendly interface that allows users to upload images and view results in real time. The backend model is implemented using TensorFlow/Keras, ensuring accurate and efficient image recognition.

This project serves as a practical example of deep learning deployment, combining model training, image preprocessing, and real time prediction within an accessible web application.