Model Deployment Flask

## Overview
The tutorial on "Deploying a Machine Learning Model with a Flask API" is an insightful guide aimed at developers looking to deploy their image classification models effectively. By leveraging Flask, it showcases how to create a user-friendly API that can classify Google Street View House Numbers. This approach not only enhances your model's accessibility but also allows for a seamless integration into web applications.

By following the straightforward instructions provided, you can set up a local environment to run the model and test its functionalities with minimal setup. It's an excellent resource for anyone interested in bridging the gap between machine learning and web development.

## Features
- **Easy Setup**: The tutorial provides clear instructions for cloning the repository and running the API, making it easy for beginners to follow along.
- **Flask Integration**: By using Flask, it demonstrates how to create a lightweight web application that interacts with your machine learning model.
- **Local Deployment**: Users can run the entire setup on their local machine to test and experiment with the application before deploying it to the web.
- **Image Classification**: The focus on classifying Google Street View House Numbers allows for a practical application of image recognition technology.
- **Python Dependencies**: The tutorial includes a simple command to install all necessary Python packages, streamlining the setup process.
- **Interactive Web Interface**: The final product provides a web page that allows users to upload and classify images easily, enhancing user interaction.