Fall Detector

The project is a web application designed to detect falls from uploaded images using a pre-trained deep-learning model. It is built with Streamlit for the web interface and TensorFlow for the machine-learning model

Objectives

• To create an easy-to-use web application that can detect falls from images.

Key Features:

• User Interface: Developed using Streamlit, it provides a simple and interactive interface for users.
• Image Upload: Allows users to upload images in .jpg or .png format.
• Model Prediction: Utilizes a TensorFlow model to predict whether the uploaded image shows a normal situation or a fall.
• Result Display: Shows the uploaded image and the prediction result on the same page.

Developer

• James Bañez