github-actions[bot]
commented
4 months ago Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊
Note: This repo is for beginners to learn and start with Opensource we won't accept more than 10 issues from a single person, This restriction applies to Gssoc project which has a similar kind of adding folder files, Points will be reduced when we find Spam.
I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.
You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
anushkasaxena07
Is there an existing issue for this?
Feature Description
This feature involves deploying the trained Breast Cancer Detection model as a web-based API using either FastAPI or Flask. This API will accept input data in the form of JSON, preprocess the data, make predictions using the trained model, and return the prediction results in JSON format.
Use Case
By serving the model as an API, users can easily integrate the breast cancer prediction model into various applications, such as mobile apps, web platforms, or other systems that require real-time predictions. This feature makes the model accessible to healthcare providers, researchers, and developers, enabling them to leverage the model's capabilities without needing in-depth knowledge of machine learning or the underlying codebase.
Benefits
Accessibility:
Makes the model easily accessible to a broader audience, including non-technical users. Allows integration into various applications, enhancing usability and adoption. Scalability:
Enables the model to handle multiple requests concurrently, providing scalability for real-world applications. Facilitates easy updates and maintenance of the model without disrupting the end-users. Real-time Predictions:
Provides immediate predictions, which are crucial for time-sensitive applications such as medical diagnostics. Enhances user experience by reducing the time taken to obtain results. Standardization:
Offers a standardized interface (API) for interacting with the model, making it easier for developers to integrate with other systems. Promotes consistency in how predictions are made and results are interpreted.
Add ScreenShots
No response
Priority
High
Record