Brand DNA Extractor#

Extract a structured brand identity profile from any website URL. Analyzes colors, typography, and visual style to produce a reusable brand profile for on-brand content generation.

Environment Variables#

bash

export OPENAI_API_KEY="your_openai_key"          # for VLM visual analysis (fallback)
export GOOGLE_GENAI_API_KEY="your_gemini_key"    # for VLM visual analysis (primary)
export SUPABASE_URL="your_supabase_url"          # optional: for caching results
export SUPABASE_KEY="your_supabase_key"          # optional: service role key

What It Extracts#

ComponentDetailsColor palettePrimary, secondary, accent, background, and text colors — sourced from CSS variables, computed styles, and K-means image clusteringTypographyHeading and body fonts, weights, sources (Google Fonts, Adobe Fonts, system)Visual styleMood descriptors, photography styles, composition notes, lighting characterization, brand personality, target audience signalsImageryLogo, favicon, hero images, product images, other images — classified and ranked

Python Usage#

python

import asyncio
from brand_dna_extractor.extractor import BrandDNAExtractor, extract_brand_dna

# Quick extraction
async def main():
    result = await extract_brand_dna(
        url="https://example.com",
        user_id="optional-user-id",
        force_refresh=False,
    )

    if result.success:
        dna = result.brand_dna
        print(dna.color_palette.dominant_color)       # "#2563EB"
        print(dna.typography.primary_font.family)     # "Inter"
        print(dna.visual_style.moods)                 # ["warm minimalism", "approachable"]
        print(dna.visual_style.brand_personality)     # "Confident and calm..."
    else:
        print(result.error)

asyncio.run(main())

# Full control
extractor = BrandDNAExtractor(
    vlm_provider="gemini",      # "gemini" (default) or "openai"
    enable_storage=True,        # cache results in Supabase
    enable_embeddings=False,    # skip CLIP embedding generation
)

result = await extractor.extract(
    url="https://example.com",
    include_subpages=True,      # also scrape about/product pages
    max_subpages=5,
    force_refresh=False,
)

5-Step Extraction Pipeline#

Step 1: Website Scraping#

Uses a two-tier scraping strategy:

Primary — DOM Structure Scraper ( SimpleScraper)

• Fast HTTP requests with structured HTML parsing
• Extracts CSS variables, computed styles, stylesheets, JSON-LD data
• Optimized for Shopify stores (reads product JSON-LD)
• Follows include_subpages to crawl up to max_subpages additional URLs

Fallback — Playwright Scraper ( PlaywrightScraper)

• Activates when simple scraper yields < 3 gallery/product images
• Handles JavaScript-rendered content
• Optional dependency: pip install playwright && playwright install

Step 2: Image Extraction and Classification#

Images are classified into types:

TypeDescriptionlogoSite logo (detected by position, alt text, size)faviconSite faviconheroLarge above-the-fold banner imagesproductProduct photographylifestyleContextual/lifestyle imageryotherRemaining UI images

Up to 100 images extracted; top 30 product + 30 other retained.

Step 3: Color Analysis#

Multi-source color extraction and classification:

text

CSS custom properties (--primary-color, --brand-color, etc.)
    +
Computed element styles (headerBackground, ctaBackground, linkColor, etc.)
    +
K-means clustering on logo pixels (3 colors)
    +
K-means clustering on hero/product images (3 colors each, up to 5 images)
    ↓
Deduplicate (Euclidean distance threshold = 30)
    ↓
Classify by lightness/saturation:
  L > 0.9  → background
  L < 0.15 → text
  S > 0.6  → accent
  source=primary → primary
  else     → secondary

ColorPalette output:

python

palette.dominant_color        # "#2563EB" (hex string)
palette.primary_colors        # List[ColorInfo] (up to 3)
palette.secondary_colors      # List[ColorInfo] (up to 3)
palette.accent_colors         # List[ColorInfo] (up to 2)
palette.background_colors     # List[ColorInfo] (up to 2)
palette.text_colors           # List[ColorInfo] (up to 2)

ColorInfo fields: hex, rgb, hsl, role, source, name, frequency, css_property

Step 4: Typography Analysis#

Font detection from three sources:

CSS Computed Fonts

• Parses font-family declarations from computed element styles
• Classifies by role: heading, body, cta, nav
• Identifies system fonts vs custom fonts

Google Fonts (detected from stylesheet URLs)

• Parses both old ( /css?family=) and new ( /css2?family=) API formats
• Extracts family names and weight variants

Adobe Fonts / Typekit (detected from stylesheet URLs)

• Flags usage of use.typekit.net or use.adobe.com

Typography output:

python

typography.primary_font         # FontInfo — main body font
typography.secondary_font       # FontInfo — heading font (if different)
typography.heading_fonts        # List[FontInfo]
typography.body_fonts           # List[FontInfo]
typography.accent_fonts         # List[FontInfo]
typography.google_fonts_urls    # List[str]
typography.detected_from_google_fonts  # bool
typography.detected_from_adobe_fonts   # bool

FontInfo fields: family, weight, role, source, fallbacks, url

Step 5: Visual Style Analysis (VLM)#

Up to 5 representative images (prioritized: hero > product > lifestyle) are analyzed by a VLM using a structured creative director prompt.

Analysis dimensions:

1. Visual mood and atmosphere (3-5 compound descriptors)
2. Photography/visual style (2-3 technical descriptors)
3. Composition analysis (negative space, focal point, depth)
4. Lighting characterization (quality, direction, color temperature)
5. Texture and material language
6. Dominant subjects
7. Brand personality inference
8. Target audience signals

VLM provider selection:

• Default: Gemini ( gemini-3-flash-preview or env GEMINI_MODEL)
• Fallback: OpenAI Vision (env OPENAI_MODEL)
• Automatic retry with exponential backoff (3 attempts)

VisualStyle output:

python

style.moods                  # List[str] — top 5 by frequency across images
style.photography_styles     # List[str] — top 3
style.composition_notes      # str — aggregated composition analysis
style.lighting_style         # str
style.texture_notes          # str
style.dominant_subjects      # List[str] — top 5
style.brand_personality      # str — 2-3 sentences
style.target_audience_hint   # str — 2-3 sentences
style.confidence_score       # float — 0.0-1.0 (higher with more images analyzed)
style.images_analyzed        # int

BrandDNA Object#

python

@dataclass
class BrandDNA:
    url: str
    domain: str
    logo: Optional[ExtractedImage]
    favicon: Optional[ExtractedImage]
    hero_images: List[ExtractedImage]
    product_images: List[ExtractedImage]
    other_images: List[ExtractedImage]
    color_palette: ColorPalette
    typography: Typography
    visual_style: VisualStyle
    id: Optional[str]                 # UUID if stored in database
    style_embedding: Optional[List[float]]  # CLIP embedding if enabled

Caching#

When enable_storage=True and Supabase credentials are configured, results are automatically cached by domain.

python

# Force re-extraction (ignore cache)
result = await extractor.extract(url, force_refresh=True)

# Retrieve cached result by domain
from brand_dna_extractor.extractor import get_brand_dna_by_domain
dna = await get_brand_dna_by_domain("example.com")

# Retrieve by stored ID
from brand_dna_extractor.extractor import get_brand_dna
dna = await get_brand_dna("uuid-string")

Error Handling#

BrandDNAResponse always returns a result object:

python

result = await extractor.extract(url)

result.success         # bool
result.brand_dna       # BrandDNA | None
result.error           # str | None — human-readable error description
result.from_cache      # bool — True if returned from cache

Common failure modes:

• Both scrapers fail (site blocks bots, requires login)
• VLM API quota exhausted
• URL is not a public website

Installation#

bash

pip install aiohttp Pillow numpy scikit-learn openai google-generativeai
# Optional for JS-heavy sites:
pip install playwright && playwright install chromium

Example Output#

python

BrandDNA(
    domain="allbirds.com",
    color_palette=ColorPalette(
        dominant_color="#2B2B2B",
        primary_colors=[ColorInfo(hex="#2B2B2B", role="primary"), ...],
        accent_colors=[ColorInfo(hex="#E8D5C0", role="accent"), ...],
    ),
    typography=Typography(
        primary_font=FontInfo(family="Flanders Sans", weight="400", source="css"),
        detected_from_google_fonts=False,
    ),
    visual_style=VisualStyle(
        moods=["warm minimalism", "earthy authenticity", "understated confidence"],
        photography_styles=["lifestyle documentary", "naturalistic color treatment"],
        brand_personality="Calm and purposeful, with a commitment to sustainability...",
        target_audience_hint="Environmentally conscious millennials and Gen Z...",
        confidence_score=0.83,
        images_analyzed=5,
    ),
)

---

Author#

Canlah AI — Run performance marketing without breaking your brand.

• GitHub: github.com/PHY041
• All Skills: clawhub.ai/PHY041