AI-Powered Frontend

AI-powered frontend development generates UI components, styles, and layouts from natural language descriptions, accelerating the development workflow.

UI Component Generator

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate

class UIGenerator:
    def __init__(self, framework: str = "react"):
        self.llm = ChatOpenAI(model="gpt-4", temperature=0)
        self.framework = framework

    def generate_component(self, description: str, style: str = "tailwind") -> dict:
        prompt = PromptTemplate.from_template(
            """Generate a {framework} component based on this description.
            Use {style} for styling.
            Description: {description}
            Return: component code, props interface, usage example"""
        )
        chain = prompt | self.llm
        result = chain.invoke({"framework": self.framework, "style": style, "description": description})
        return {"code": result.content, "framework": self.framework}

    def generate_page(self, layout_description: str) -> dict:
        prompt = PromptTemplate.from_template(
            """Generate a complete {framework} page with this layout:
            {layout}
            Include: Header, main content, footer. Use responsive design."""
        )
        chain = prompt | self.llm
        result = chain.invoke({"framework": self.framework, "layout": layout_description})
        return {"page_code": result.content}

    def generate_styles(self, component_code: str, design_system: str = "modern") -> str:
        prompt = PromptTemplate.from_template(
            """Generate CSS/Tailwind styles for this component:
            {component}
            Design system: {design_system}
            Return styled version:"""
        )
        chain = prompt | self.llm
        return chain.invoke({"component": component_code, "design_system": design_system}).content

# Usage
generator = UIGenerator(framework="react")
result = generator.generate_component("A user profile card with avatar, name, and bio")

Design System Integration

class DesignSystemMapper:
    def __init__(self, design_tokens: dict):
        self.tokens = design_tokens

    def map_to_tailwind(self, design_spec: dict) -> str:
        classes = []
        if "spacing" in design_spec:
            classes.append(f"p-{design_spec['spacing']}")
        if "color" in design_spec:
            color = self.tokens.get("colors", {}).get(design_spec["color"], design_spec["color"])
            classes.append(f"bg-{color}")
        if "rounded" in design_spec:
            classes.append(f"rounded-{design_spec['rounded']}")
        return " ".join(classes)

    def generate_css_variables(self) -> str:
        css = ":root {\n"
        for category, values in self.tokens.items():
            for name, value in values.items():
                css += f"  --{category}-{name}: {value};\n"
        css += "}"
        return css

    def validate_accessibility(self, component: dict) -> list:
        issues = []
        if "alt" not in component.get("image", {}):
            issues.append("Missing alt text for image")
        if component.get("contrast_ratio", 0) < 4.5:
            issues.append("Low contrast ratio")
        return issues

# Usage
design_tokens = {
    "colors": {"primary": "#3b82f6", "secondary": "#8b5cf6"},
    "spacing": {"sm": "0.5rem", "md": "1rem", "lg": "1.5rem"}
}
mapper = DesignSystemMapper(design_tokens)

AI-Assisted Accessibility

class AccessibilityChecker:
    def __init__(self, llm):
        self.llm = llm

    def check_component(self, component_code: str) -> dict:
        prompt = f"""Review this UI component for accessibility issues:
        {component_code}
        Check for: ARIA labels, keyboard navigation, contrast, semantic HTML."""
        return self.llm.invoke(prompt).content

    def generate_accessible_version(self, component_code: str) -> str:
        prompt = f"""Rewrite this component to be fully accessible:
        {component_code}
        Add ARIA attributes, keyboard handlers, and proper semantics."""
        return self.llm.invoke(prompt).content

    def generate_alt_text(self, image_description: str) -> str:
        prompt = f"""Generate descriptive alt text for: {image_description}
        Keep it concise but descriptive for screen readers."""
        return self.llm.invoke(prompt).content

# Usage
checker = AccessibilityChecker(llm)
issues = checker.check_component("<img src='logo.png'>")
accessible = checker.generate_accessible_version("<img src='logo.png'>")

Key Takeaways

• UI generation creates components from natural language descriptions
• Design systems ensure consistent styling and branding
• Accessibility is automatically checked and improved
• Component libraries enable reusable AI-generated components
• Live preview validates generated UI in real-time