Kolaps27/UI-layout-optimizer

UI Layout Optimizer: Adaptive UI Optimization Environment (OpenEnv)

 

🚀 Motivation

In modern digital products, static A/B testing often fails to capture the nuance of diverse user behaviors. The UI Layout Optimizer is an OpenEnv-compliant environment designed to train agents that dynamically adapt layout configurations—such as button sizes, form lengths, and wizard steps—to maximize conversion rates and user satisfaction in real-time.

By simulating various user personas (impatient, careful, new users) and their psychological responses to UI friction, this environment provides a standardized benchmark for autonomous UI optimization agents.

🛠️ Environment Specification

Action Space

The agent can manipulate the UI layout through seven distinct actions:

Observation Space

At each step, the agent receives an Observation containing:

• —Device: mobile or desktop (affects user tolerance thresholds).
• —Layout: Current button_size, form_length, and number of steps.
• —Progress: A scalar value (0.0 to 1.0) representing task completion.
• —Last Action: Feedback on the previous operation.

Task Descriptions

Evaluation is conducted across three difficulty tiers:

1. 1.Easy: Discrete actions only, stable user types, and low noise levels.
2. 2.Medium: Mixed user personas with stochastic drop-off rates.
3. 3.Hard: Hidden user types, continuous action tuning, and highly noisy feedback.

💻 Usage

Prerequisites

• —Python 3.10+
• —Hugging Face API Token (for LLM-based agents)

Local Execution

1. 1.Install dependencies:

   pip install -r requirements.txt

1. 1.Run the baseline evaluation:

   export HF_TOKEN="your_token_here"
   python baseline.py

Running with Docker

1. 1.Build the image:

   docker build -t ui-optimizer .

1. 1.Run the container:

   docker run -e HF_TOKEN="your_token_here" ui-optimizer

☁️ Deployment to Hugging Face Spaces

This project is optimized for deployment as a Docker Space.

1. 1.Create a new Space on Hugging Face.
2. 2.Select Docker as the SDK.
3. 3.In the Space Settings, add your HF_TOKEN as a Secret.
4. 4.Push the project files (including Dockerfile and requirements.txt) to the Space repository.
5. 5.Hugging Face will automatically build and deploy the container.

📊 Baseline Results (Example)

Evaluation results using the provided baseline.py hybrid agent:

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.