Welcome to my project implementation of Real-time Image Classification Web App!
            This implementation is powered by a robust Machine Learning algorithm at the back-end while the front-end is developed with
            Flask and BootStrap.

            A wide range of techniques in Image Data Analysis, Feature Extraction, Machine Learning, and Web Development have been
            covered during this implementation.

            In addition, a wide range of Data Science, Image Processing, Computer Vision, and Machine Learning libraries and tools have
            been utilized, such as Scikit-Learn, Scikit-Image, Pandas, Matplotlib, Numpy, OpenCV, Scipy, Anaconda, and Visual Studio
            Code IDE, etc.

            The programming language used for the back-end development is Python 3+, while HTML, CSS, DJango are primarily used for the
            front-end web interface development, and no database is required.

            Essentially, the steps involved in this implementation are as follows:
            - Data/Image Arrangement and Preparation
            - Data/Image Pre-processing
            - Feature Extraction - with HOG Image Descriptor
            - Machine Learning Model Training
            - Machine Leaning Model Pipelining - Model Finalization
            - Front End Development - Building Flask Web App interface
            - Application Deployment - App Deployed in Python Anywhere

            WHAT'S EXPECTED?
            There are only 20 classes or categories of image used to train the Machine Learning model that powers this application.
            They are the following: [1] Human head, [2] Eagle head, [3] Wolf head, [4] Elephant head, [5] Bear head, [6] Cat head,
            [7] Chicken head, [8] Cow head, [9] Deer head, [10. Dog head, [11] Duck head, [12] Lion head, [13] Monkey head, [14] Mouse head,
            [15] Panda head, [16] Nature, [17] Pigeon head, [18] Rabbit head, [19] Tiger head, [20] Sheep head.

            However, a total of 2,676 images which are divided into 20 categories were used to train my Machine Learning model. For better
            performance, I expect to train with larger number of images in many other different classes/categories.
            This is a proposal for future work!

            NOTE 1: In order to get a good sense of the outcome of this app, I encourage you to test
            this app only with images that belong to any of the above-listed 20 categories. Since my model was not
            trained with any other category of image, proving this app with any image which is not included the list
            of classes/categories above would return a bad result.

            NOTE 2: I want to mention that for each of the 20 classes/categories of images listed above,
            only the picture of facial or head region were used to train my Machine Learning model and not pictures of
            the entire object. What this means, therefore, is that providing a full image of an Elephant or Eagle
            instead of Elephant head or Eagle head will have the app return wrong classification result.

            More information regarding the dataset used to train my Machine Learning model can be found on my GitHub page.
            For further infromation regarding this project, please contact the author:  Ekpo Otu