Analysis Components

The analysis module provides AI-powered visual analysis capabilities for understanding and comparing screenshots through the unified LayoutLens API.

LayoutLens Core API

class layoutlens.api.core.LayoutLens(api_key: str | None = None, model: str = 'gpt-4o-mini', provider: str = 'openai', output_dir: str = 'layoutlens_output', cache_enabled: bool = True, cache_type: str = 'memory', cache_ttl: int = 3600)[source]

Bases: object

Simple API for AI-powered UI testing with natural language.

This class provides an intuitive interface for analyzing websites and screenshots using natural language queries, designed for developer workflows and CI/CD integration.

Examples

>>> lens = LayoutLens(api_key="sk-...")
>>> result = lens.analyze("https://example.com", "Is the navigation clearly visible?")
>>> print(result.answer)

>>> # Compare two designs
>>> result = lens.compare(
...     ["before.png", "after.png"],
...     "Are these layouts consistent?"
... )
__init__(api_key: str | None = None, model: str = 'gpt-4o-mini', provider: str = 'openai', output_dir: str = 'layoutlens_output', cache_enabled: bool = True, cache_type: str = 'memory', cache_ttl: int = 3600)[source]

Initialize LayoutLens with AI provider credentials.

Parameters:

  • api_key – API key for the provider. If not provided, will try OPENAI_API_KEY environment variable.

  • model – Model to use for analysis (LiteLLM naming: “gpt-4o”, “anthropic/claude-3-5-sonnet”, “google/gemini-1.5-pro”).

  • provider – AI provider to use (“openai”, “anthropic”, “google”, “gemini”, “litellm”).

  • output_dir – Directory for storing screenshots and results.

  • cache_enabled – Whether to enable result caching for performance.

  • cache_type – Type of cache backend: “memory” or “file”.

  • cache_ttl – Cache time-to-live in seconds (1 hour default).

Raises:

  • AuthenticationError – If no valid API key is found.

  • ConfigurationError – If invalid provider or configuration is specified.

async analyze(source: str | Path | list[str | Path], query: str | list[str], viewport: Viewport | str = 'desktop', context: dict[str, Any] | None = None, instructions: Instructions | None = None, max_concurrent: int = 5) → AnalysisResult | BatchResult[source]

Smart analyze method that handles single or multiple sources and queries.

Parameters:

  • source – Single URL/path or list of URLs/paths to analyze.

  • query – Single question or list of questions about the UI.

  • viewport – Viewport for capture (Viewport.DESKTOP, “desktop”, etc.).

  • context – Additional context for analysis (user_type, browser, etc.). Legacy format.

  • instructions – Rich instruction set with expert personas and structured context. Takes precedence over context if both provided.

  • max_concurrent – Maximum concurrent operations for batch analysis.

Returns:

AnalysisResult for single source+query, BatchResult for multiple.

Examples

# Single analysis >>> result = await lens.analyze(”https://github.com”, “Is it accessible?”)

# Multiple queries on one source >>> result = await lens.analyze(”https://github.com”, [“Is it accessible?”, “Mobile-friendly?”])

# Multiple sources, one query >>> result = await lens.analyze([“page1.html”, “page2.html”], “Is it good?”)

# Multiple sources and queries >>> result = await lens.analyze([“page1.html”, “page2.html”], [“Accessible?”, “Mobile?”])

async compare(sources: list[str | Path], query: str = 'Are these layouts consistent?', viewport: Viewport | str = 'desktop', context: dict[str, Any] | None = None, instructions: Instructions | None = None) → ComparisonResult[source]

Compare multiple URLs or screenshots.

Parameters:

  • sources – List of URLs or screenshot paths to compare.

  • query – Natural language question for comparison.

  • viewport – Viewport for captures (Viewport.DESKTOP or string).

  • context – Additional context for analysis.

  • instructions – Rich instructions for expert analysis.

Returns:

Comparison analysis with overall assessment.

Example

>>> result = lens.compare([
...     "https://mysite.com/before",
...     "https://mysite.com/after"
... ], "Did the redesign improve the user experience?")
async capture(source: str | Path | list[str | Path], viewport: Viewport | str = 'desktop', wait_for_selector: str | None = None, wait_time: int | None = None, max_concurrent: int = 3) → str | dict[str, str][source]

Smart capture method that handles single or multiple sources uniformly.

Parameters:

  • source – Single URL/path or list of URLs/paths to capture.

  • viewport – Viewport for capture (Viewport.DESKTOP, “desktop”, etc.).

  • wait_for_selector – CSS selector to wait for before capturing.

  • wait_time – Additional wait time in milliseconds.

  • max_concurrent – Maximum concurrent captures for multiple sources.

Returns:

Returns screenshot path as string. Multiple sources: Returns dict mapping source to screenshot path.

Return type:

Single source

Examples

# Single URL >>> path = await lens.capture(”https://example.com”) # Returns: “/path/to/screenshot.png”

# Multiple URLs >>> paths = await lens.capture([”https://site1.com”, “https://site2.com”]) # Returns: {”https://site1.com”: “/path1.png”, “https://site2.com”: “/path2.png”}

# HTML files >>> path = await lens.capture(“page.html”) >>> paths = await lens.capture([“page1.html”, “page2.html”])

# Existing images (validation) >>> path = await lens.capture(“screenshot.png”)

async check_accessibility(source: str | Path, viewport: Viewport | str = 'desktop') → AnalysisResult[source]

Quick accessibility check with common WCAG queries.

async check_mobile_friendly(source: str | Path) → AnalysisResult[source]

Quick mobile responsiveness check.

async check_conversion_optimization(source: str | Path, viewport: Viewport | str = 'desktop') → AnalysisResult[source]

Check for conversion-focused design elements.

async audit_accessibility(source: str | Path, standards: list[str] = None, compliance_level: ComplianceLevel | str = 'AA', viewport: Viewport | str = 'desktop') → AnalysisResult[source]

Professional accessibility audit using WCAG expert knowledge.

Parameters:

  • source – URL or file path to analyze

  • standards – Accessibility standards to apply (default: WCAG 2.1, Section 508)

  • compliance_level – WCAG compliance level (ComplianceLevel.AA or string)

  • viewport – Viewport for analysis (Viewport.DESKTOP or string)

Returns:

Detailed accessibility assessment with specific WCAG guidance

Raises:

ValueError – If compliance_level is not a valid WCAG level

async optimize_conversions(source: str | Path, business_goals: list[str] = None, industry: str = None, target_audience: str = None, viewport: Viewport | str = 'desktop') → AnalysisResult[source]

Conversion rate optimization analysis using CRO expert knowledge.

Parameters:

  • source – URL or file path to analyze

  • business_goals – Business objectives (e.g., reduce_cart_abandonment)

  • industry – Industry context for specialized recommendations

  • target_audience – Target audience for optimization focus

  • viewport – Viewport for analysis (Viewport.DESKTOP or string)

Returns:

Detailed CRO recommendations with A/B testing suggestions

async analyze_mobile_ux(source: str | Path, device_types: list[str] = None, performance_focus: bool = True) → AnalysisResult[source]

Mobile UX analysis using mobile expert knowledge.

Parameters:

  • source – URL or file path to analyze

  • device_types – Target devices (smartphone, tablet)

  • performance_focus – Include performance optimization analysis

Returns:

Mobile-specific UX recommendations and optimizations

async audit_ecommerce(source: str | Path, page_type: str = 'product_page', business_model: str = 'b2c', viewport: Viewport | str = 'desktop') → AnalysisResult[source]

E-commerce UX audit using retail expert knowledge.

Parameters:

  • source – URL or file path to analyze

  • page_type – Type of e-commerce page (product_page, checkout, homepage)

  • business_model – Business model (b2c, b2b)

  • viewport – Viewport for analysis (Viewport.DESKTOP or string)

Returns:

E-commerce specific recommendations for conversion improvement

async analyze_with_expert(source: str | Path, query: str, expert_persona: Expert | str, focus_areas: list[str] = None, user_context: dict[str, Any] = None, viewport: Viewport | str = 'desktop') → AnalysisResult[source]

Analyze using a specific domain expert persona.

Parameters:

  • source – URL or file path to analyze

  • query – Question to analyze

  • expert_persona – Expert to use (Expert.ACCESSIBILITY or string)

  • focus_areas – Specific areas to focus analysis on

  • user_context – Rich context about users and requirements

  • viewport – Viewport for analysis (Viewport.DESKTOP or string)

Returns:

Expert-level analysis with domain-specific recommendations

async compare_with_expert(sources: list[str | Path], query: str, expert_persona: Expert | str, focus_areas: list[str] = None, viewport: Viewport | str = 'desktop') → ComparisonResult[source]

Compare multiple sources using domain expert knowledge.

Parameters:

  • sources – List of URLs or file paths to compare

  • query – Comparison question

  • expert_persona – Expert to use for comparison (Expert.ACCESSIBILITY or string)

  • focus_areas – Specific areas to focus comparison on

  • viewport – Viewport for analysis (Viewport.DESKTOP or string)

Returns:

Expert comparison with domain-specific insights

get_cache_stats() → dict[str, Any][source]

Get cache performance statistics.

clear_cache() → None[source]

Clear all cached analysis results.

enable_cache() → None[source]

Enable caching.

disable_cache() → None[source]

Disable caching.

create_test_suite(name: str, description: str, test_cases: list[dict[str, Any]]) → UITestSuite

Create a test suite from specifications.

Parameters:

  • name – Name of the test suite

  • description – Description of the test suite

  • test_cases – List of test case specifications

Returns:

UITestSuite object

async run_test_suite(suite: UITestSuite, parallel: bool = False, max_workers: int = 4) → list[UITestResult]

Run a test suite and return results.

Parameters:

  • suite – The test suite to run

  • parallel – Whether to run tests in parallel

  • max_workers – Maximum number of parallel workers

Returns:

List of UITestResult objects

Analysis Results

class layoutlens.api.core.AnalysisResult(source: str, query: str, answer: str, confidence: float, reasoning: str, screenshot_path: str | None = None, viewport: str = 'desktop', timestamp: str = <factory>, execution_time: float = 0.0, metadata: dict[str, ~typing.Any] = <factory>)[source]

Bases: object

Result from analyzing a single URL or screenshot.

source: str
query: str
answer: str
confidence: float
reasoning: str
screenshot_path: str | None
viewport: str
timestamp: str
execution_time: float
metadata: dict[str, Any]
to_json() → str[source]

Export result to JSON string.

__init__(source: str, query: str, answer: str, confidence: float, reasoning: str, screenshot_path: str | None = None, viewport: str = 'desktop', timestamp: str = <factory>, execution_time: float = 0.0, metadata: dict[str, ~typing.Any] = <factory>) → None
class layoutlens.api.core.ComparisonResult(sources: list[str], query: str, answer: str, confidence: float, reasoning: str, individual_analyses: list[~layoutlens.api.core.AnalysisResult] = <factory>, screenshot_paths: list[str] = <factory>, timestamp: str = <factory>, execution_time: float = 0.0, metadata: dict[str, ~typing.Any] = <factory>)[source]

Bases: object

Result from comparing multiple sources.

sources: list[str]
query: str
answer: str
confidence: float
reasoning: str
individual_analyses: list[AnalysisResult]
screenshot_paths: list[str]
timestamp: str
execution_time: float
metadata: dict[str, Any]
to_json() → str[source]

Export result to JSON string.

__init__(sources: list[str], query: str, answer: str, confidence: float, reasoning: str, individual_analyses: list[~layoutlens.api.core.AnalysisResult] = <factory>, screenshot_paths: list[str] = <factory>, timestamp: str = <factory>, execution_time: float = 0.0, metadata: dict[str, ~typing.Any] = <factory>) → None
class layoutlens.api.core.BatchResult(results: list[~layoutlens.api.core.AnalysisResult], total_queries: int, successful_queries: int, average_confidence: float, total_execution_time: float, timestamp: str = <factory>)[source]

Bases: object

Result from batch analysis.

results: list[AnalysisResult]
total_queries: int
successful_queries: int
average_confidence: float
total_execution_time: float
timestamp: str
to_json() → str[source]

Export result to JSON string.

__init__(results: list[~layoutlens.api.core.AnalysisResult], total_queries: int, successful_queries: int, average_confidence: float, total_execution_time: float, timestamp: str = <factory>) → None