Bilayers simplifies the process of creating web-based (Gradio) or Jupyter Notebook interfaces for any deep-learning cell segmentation algorithm. Instead of writing extensive UI code, you only need to fill out a structured YAML file—just like filling out a Google Form!
Before You Begin:¶
To successfully generate an interface, familiarize yourself with the key Bilayers components:
- Supported Interfaces
Bilayers currently supports the following interface types:- Gradio - A no code web UI
- Jupyter Notebook - Interactive notebooks
- Understanding config.yaml requirements
The config.yaml file defines the behavior of your interface, including input/output handling and algorithm execution.
Each section of this file serves a distinct purpose:
- citations - Reference publications or software licenses
- docker_image - Specify the pre-built Docker image
- algorithm
_folder _name - Define a workspace directory - exec_function - Configure how the algorithm runs
- inputs - Define expected input files/data
- outputs - Specify expected results
- parameters - Configure user-adjustable options
- display_only - Read-only UI fields
- Choosing the right base docker image
Your algorithm’s Docker image must meet Bilayers’ compatibility requirements. Follow these guidelines to ensure seamless integration:
- Requirements for the Algorithm (Base) Docker Image - Must include Python, package managers, and other essentials
- Adapting Non-Compliant Base Images - Steps to modify an existing image
- Steps to create your custom Algorithm’s Interfaces Once you’ve selected an interface type, configured config.yaml, and prepared a compatible Docker image, follow the Step-by-Step Guide to generate your UI automatically.