Sports Personality Classifier - Python Project with Flask UI

This project classifies images of five prominent sports personalities:

• Maria Sharapova
• Serena Williams
• Virat Kohli
• Roger Federer
• Lionel Messi

Project Structure:

• UI: Contains the website code for user interaction. (HTML, CSS, Javascript)
• server: Python Flask server handles communication between UI and model.
• model: Python notebook containing the machine learning model for classification.
• google_image_scrapping: Code to scrape images of the personalities from Google.
• images_dataset: Folder storing the downloaded image dataset.

Technologies Used:

• Python: Core programming language.
• Numpy & OpenCV: Libraries for image processing and manipulation.
• Matplotlib & Seaborn: Libraries for data visualization.
• Scikit-learn (Sklearn): Machine learning library for model building.
• Jupyter Notebook: Interactive environment for code development and model training.
• Visual Studio Code/PyCharm: Integrated Development Environments (IDEs) for coding.
• Flask: Python web framework for building the server.

Project Functionality:

• Scrape images from Google using the google_image_scrapping script.
• Preprocess and clean the downloaded images in the model notebook.
• Train a machine learning model using Sklearn to identify the personalities.
• Develop a Flask server (server) to handle user requests and communicate with the model.
• Design a user interface (UI) using HTML, CSS, and Javascript to allow users to upload images and receive classification results.

Getting Started:

1. Clone the project from your GitHub repository.
2. Install required libraries (numpy, opencv-python, matplotlib, seaborn, scikit-learn, flask).
3. Run the google_image_scrapping script to download the initial dataset (optional: manually download images).
4. Open the model notebook in Jupyter Notebook and follow the instructions for training the model.
5. Run the Flask server (python server.py) to start the application.
6. Access the UI through a web browser (usually http://127.0.0.1:5000/).

Acknowledgement:

This project references learning materials from codebasics.io.