This project is a Quart web application that incorporates machine learning to predict the risk of diabetes based on user input. The application allows users to input various health parameters and get a prediction regarding their risk of diabetes.
The project consists of the following files and directories:
quart_app/: The main application directory.app.py: Initializes the Quart web application and integrates the machine learning component.routes/: Contains route definitions for the application.index.py: Defines the main route for user input and prediction.
assets/: Contains datasets used for machine learning operations.
ml.py: The machine learning module responsible for handling data preprocessing, model training, and predictions.utils.py: Utility functions and decorators used throughout the project, including data conversion and async decorators.__main__.py: Launches the application using the Quart runner.templates/: Contains the HTML template for the user interface.index.html: The user input form and result display. Installation To run this project, make sure you have Python installed. You can set up a virtual environment and install the required dependencies listed in requirements.txt:
pip install -r requirements.txtpython __main__.py-
Access the web application at http://localhost:5000 in your web browser.
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Fill in the health parameters in the input form and submit.
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The application will provide a prediction on whether you are at risk of diabetes or not.
project.toml: Contains tool configurations, including code formatting and linting settings.
The project relies on the following Python libraries:
hypercorn: An ASGI web server for Quart.numpy: For numerical operations.pandas: For data manipulation.Quart: A web framework for building asynchronous web applications.scikit-learn: For machine learning model training and prediction. You can install these dependencies using the requirements.txt file.
Feel free to contribute to this project by opening issues, suggesting improvements, or creating pull requests.