Deploying MobileNet with TensorFlow.js and Flask | Challenge

# Introduction

This project guides you through the process of deploying a pre-trained MobileNetV2 model using TensorFlow.js within a Flask web application. MobileNetV2 is a lightweight deep neural network used primarily for image classification. TensorFlow.js enables running machine learning models directly in the browser, allowing for interactive web applications. Flask, a Python web framework, will serve as the backend to host our application. By the end of this project, you will have a running web application that classifies images on the fly using the MobileNetV2 model.

## 👀 Preview

<video src="https://file.labex.io/namespace/718bace8-27a3-4200-a588-dde4041ceeb9/ml/project-deploying-mobilenet-with-tensorflowjs-and-flask/challenge-1/assets/predict.mp4" width="100%" autoplay loop muted></video>

## 🎯 Tasks

In this project, you will learn to:

- Export a pre-trained MobileNetV2 model from Keras to a TensorFlow.js compatible format.
- Create a simple Flask application to serve your web content and model.
- Design an HTML page to upload and display images for classification.
- Use TensorFlow.js to load the exported model in the browser.
- Preprocess images in the browser to match the input requirements of MobileNetV2.
- Run the model in the browser to classify images and display the results.

## 🏆 Achievements

By completing this project, you will learn:

- How to convert a pre-trained Keras model into a format that can be used with TensorFlow.js, enabling ML models to run in the browser.
- The basics of setting up a Flask application and how to serve HTML content and static files.
- How to integrate TensorFlow.js into a web application to perform machine learning tasks client-side.
- The process of preprocessing images in JavaScript to make them compatible with the input requirements of deep learning models.
- How to make predictions using a deep learning model in the browser and display the results dynamically on the web page.