app.py

"""
Design System Extractor v2 — Main Application
==============================================

Flow:
1. User enters URL
2. Agent 1 discovers pages → User confirms
3. Agent 1 extracts tokens (Desktop + Mobile)
4. Agent 2 normalizes tokens
5. Stage 1 UI: User reviews tokens (accept/reject, Desktop↔Mobile toggle)
6. Agent 3 proposes upgrades
7. Stage 2 UI: User selects options with live preview
8. Agent 4 generates JSON
9. Stage 3 UI: User exports
"""

import os
import asyncio
import json
import gradio as gr
from datetime import datetime
from typing import Optional

# Get HF token from environment
HF_TOKEN_FROM_ENV = os.getenv("HF_TOKEN", "")

# =============================================================================
# GLOBAL STATE
# =============================================================================

class AppState:
    """Global application state."""
    def __init__(self):
        self.reset()
    
    def reset(self):
        self.discovered_pages = []
        self.base_url = ""
        self.desktop_raw = None  # ExtractedTokens
        self.mobile_raw = None   # ExtractedTokens
        self.desktop_normalized = None  # NormalizedTokens
        self.mobile_normalized = None   # NormalizedTokens
        self.upgrade_recommendations = None  # UpgradeRecommendations
        self.selected_upgrades = {}  # User selections
        self.logs = []
    
    def log(self, message: str):
        timestamp = datetime.now().strftime("%H:%M:%S")
        self.logs.append(f"[{timestamp}] {message}")
        if len(self.logs) > 100:
            self.logs.pop(0)
    
    def get_logs(self) -> str:
        return "\n".join(self.logs)

state = AppState()


# =============================================================================
# MESSAGE HELPERS
# =============================================================================

def success_message(title: str, details: str, next_step: str) -> str:
    """Generate a formatted success message with next-step guidance."""
    return f"## ✅ {title}\n\n{details}\n\n**Next step:** {next_step}"


def error_message(title: str, details: str, how_to_fix: str) -> str:
    """Generate a formatted error message with fix guidance."""
    return f"## ❌ {title}\n\n{details}\n\n**How to fix:** {how_to_fix}"


# =============================================================================
# LAZY IMPORTS
# =============================================================================

def get_crawler():
    import agents.crawler
    return agents.crawler

def get_extractor():
    import agents.extractor
    return agents.extractor

def get_normalizer():
    import agents.normalizer
    return agents.normalizer

def get_advisor():
    import agents.advisor
    return agents.advisor

def get_schema():
    import core.token_schema
    return core.token_schema


# =============================================================================
# PHASE 1: DISCOVER PAGES
# =============================================================================

async def discover_pages(url: str, progress=gr.Progress()):
    """Discover pages from URL."""
    state.reset()
    
    if not url or not url.startswith(("http://", "https://")):
        return error_message("Invalid URL",
                             "The URL must start with `https://` or `http://`.",
                             "Enter a full URL like `https://example.com` and try again."), "", None
    
    state.log(f"🚀 Starting discovery for: {url}")
    progress(0.1, desc="🔍 Discovering pages...")
    
    try:
        crawler = get_crawler()
        discoverer = crawler.PageDiscoverer()
        
        pages = await discoverer.discover(url)
        
        state.discovered_pages = pages
        state.base_url = url
        
        state.log(f"✅ Found {len(pages)} pages")
        
        # Format for display
        pages_data = []
        for page in pages:
            pages_data.append([
                True,  # Selected by default
                page.url,
                page.title if page.title else "(No title)",
                page.page_type.value,
                "✓" if not page.error else f"⚠ {page.error}"
            ])
        
        progress(1.0, desc="✅ Discovery complete!")
        
        status = success_message(
            f"Found {len(pages)} Pages",
            f"The crawler discovered **{len(pages)} pages** on `{url}`. Review the table below — "
            "use the **Select** checkboxes to choose which pages to scan for design tokens.",
            "Click **'Extract Tokens (Desktop + Mobile)'** to begin extraction."
        )

        return status, state.get_logs(), pages_data
        
    except Exception as e:
        import traceback
        state.log(f"❌ Error: {str(e)}")
        error_detail = str(e).lower()
        if "timeout" in error_detail:
            hint = "The website took too long to respond. Try again, or check if the site is accessible in your browser."
        elif "dns" in error_detail or "name resolution" in error_detail:
            hint = "Could not find this website. Please check the URL for typos."
        elif "ssl" in error_detail or "certificate" in error_detail:
            hint = "SSL/certificate error. Try using `http://` instead of `https://`, or check if the site has a valid certificate."
        else:
            hint = "Check that the URL is correct and the site is publicly accessible. Review the log above for details."
        return error_message("Discovery Failed", str(e)[:200], hint), state.get_logs(), None


# =============================================================================
# PHASE 2: EXTRACT TOKENS
# =============================================================================

async def extract_tokens(pages_data, progress=gr.Progress()):
    """Extract tokens from selected pages (both viewports)."""
    
    state.log(f"📥 Received pages_data type: {type(pages_data)}")
    
    if pages_data is None:
        return (error_message("No Pages Discovered",
                              "No pages have been discovered yet.",
                              "Go to **Step 1** above, enter a URL, and click **'Discover Pages'** first."),
                state.get_logs(), None, None)
    
    # Get selected URLs - handle pandas DataFrame
    selected_urls = []
    
    try:
        # Check if it's a pandas DataFrame
        if hasattr(pages_data, 'iterrows'):
            state.log(f"📥 DataFrame with {len(pages_data)} rows, columns: {list(pages_data.columns)}")
            
            for idx, row in pages_data.iterrows():
                # Get values by column name or position
                try:
                    # Try column names first
                    is_selected = row.get('Select', row.iloc[0] if len(row) > 0 else False)
                    url = row.get('URL', row.iloc[1] if len(row) > 1 else '')
                except (KeyError, IndexError, TypeError):
                    # Fallback to positional
                    is_selected = row.iloc[0] if len(row) > 0 else False
                    url = row.iloc[1] if len(row) > 1 else ''
                
                if is_selected and url:
                    selected_urls.append(url)
        
        # If it's a dict (Gradio sometimes sends this)
        elif isinstance(pages_data, dict):
            state.log(f"📥 Dict with keys: {list(pages_data.keys())}")
            data = pages_data.get('data', [])
            for row in data:
                if isinstance(row, (list, tuple)) and len(row) >= 2 and row[0]:
                    selected_urls.append(row[1])
        
        # If it's a list
        elif isinstance(pages_data, (list, tuple)):
            state.log(f"📥 List with {len(pages_data)} items")
            for row in pages_data:
                if isinstance(row, (list, tuple)) and len(row) >= 2 and row[0]:
                    selected_urls.append(row[1])
                    
    except Exception as e:
        state.log(f"❌ Error parsing pages_data: {str(e)}")
        import traceback
        state.log(traceback.format_exc())
    
    state.log(f"📋 Found {len(selected_urls)} selected URLs")
    
    # If still no URLs, try using stored discovered pages
    if not selected_urls and state.discovered_pages:
        state.log("⚠️ No URLs from table, using all discovered pages")
        selected_urls = [p.url for p in state.discovered_pages if not p.error][:10]
    
    if not selected_urls:
        return (error_message("No Pages Selected",
                              "No pages are selected for extraction.",
                              "Go back to the pages table above and check the **Select** boxes for the pages you want to extract, then click this button again."),
                state.get_logs(), None, None)
    
    # Limit to 10 pages for performance
    selected_urls = selected_urls[:10]
    
    state.log(f"📋 Extracting from {len(selected_urls)} pages:")
    for url in selected_urls[:3]:
        state.log(f"   • {url}")
    if len(selected_urls) > 3:
        state.log(f"   ... and {len(selected_urls) - 3} more")
    
    progress(0.05, desc="🚀 Starting extraction...")
    
    try:
        schema = get_schema()
        extractor_mod = get_extractor()
        normalizer_mod = get_normalizer()
        
        # === DESKTOP EXTRACTION ===
        state.log("")
        state.log("=" * 60)
        state.log("🖥️ DESKTOP EXTRACTION (1440px)")
        state.log("=" * 60)
        state.log("")
        state.log("📡 Enhanced extraction from 7 sources:")
        state.log("   1. DOM computed styles (getComputedStyle)")
        state.log("   2. CSS variables (:root { --color: })")
        state.log("   3. SVG colors (fill, stroke)")
        state.log("   4. Inline styles (style='color:')")
        state.log("   5. Stylesheet rules (CSS files)")
        state.log("   6. External CSS files (fetch & parse)")
        state.log("   7. Page content scan (brute-force)")
        state.log("")
        
        progress(0.1, desc="🖥️ Extracting desktop tokens...")
        
        desktop_extractor = extractor_mod.TokenExtractor(viewport=schema.Viewport.DESKTOP)
        
        def desktop_progress(p):
            progress(0.1 + (p * 0.35), desc=f"🖥️ Desktop... {int(p*100)}%")
        
        state.desktop_raw = await desktop_extractor.extract(selected_urls, progress_callback=desktop_progress)
        
        # Log extraction details
        state.log("📊 EXTRACTION RESULTS:")
        state.log(f"   Colors:     {len(state.desktop_raw.colors)} unique")
        state.log(f"   Typography: {len(state.desktop_raw.typography)} styles")
        state.log(f"   Spacing:    {len(state.desktop_raw.spacing)} values")
        state.log(f"   Radius:     {len(state.desktop_raw.radius)} values")
        state.log(f"   Shadows:    {len(state.desktop_raw.shadows)} values")
        
        # Store foreground-background pairs for real AA checking in Stage 2
        if hasattr(desktop_extractor, 'fg_bg_pairs') and desktop_extractor.fg_bg_pairs:
            state.fg_bg_pairs = desktop_extractor.fg_bg_pairs
            state.log(f"   FG/BG Pairs: {len(state.fg_bg_pairs)} unique pairs for AA checking")
        else:
            state.fg_bg_pairs = []

        # Log CSS variables if found
        if hasattr(desktop_extractor, 'css_variables') and desktop_extractor.css_variables:
            state.log("")
            state.log(f"🎨 CSS Variables found: {len(desktop_extractor.css_variables)}")
            for var_name, var_value in list(desktop_extractor.css_variables.items())[:5]:
                state.log(f"   {var_name}: {var_value}")
            if len(desktop_extractor.css_variables) > 5:
                state.log(f"   ... and {len(desktop_extractor.css_variables) - 5} more")
        
        # Log warnings if any
        if desktop_extractor.warnings:
            state.log("")
            state.log("⚠️ Warnings:")
            for w in desktop_extractor.warnings[:3]:
                state.log(f"   {w}")
        
        # Normalize desktop
        state.log("")
        state.log("🔄 Normalizing (deduping, naming)...")
        state.desktop_normalized = normalizer_mod.normalize_tokens(state.desktop_raw)
        state.log(f"   ✅ Normalized: {len(state.desktop_normalized.colors)} colors, {len(state.desktop_normalized.typography)} typography, {len(state.desktop_normalized.spacing)} spacing")
        
        # === MOBILE EXTRACTION ===
        state.log("")
        state.log("=" * 60)
        state.log("📱 MOBILE EXTRACTION (375px)")
        state.log("=" * 60)
        state.log("")
        
        progress(0.5, desc="📱 Extracting mobile tokens...")
        
        mobile_extractor = extractor_mod.TokenExtractor(viewport=schema.Viewport.MOBILE)
        
        def mobile_progress(p):
            progress(0.5 + (p * 0.35), desc=f"📱 Mobile... {int(p*100)}%")
        
        state.mobile_raw = await mobile_extractor.extract(selected_urls, progress_callback=mobile_progress)
        
        # Log extraction details
        state.log("📊 EXTRACTION RESULTS:")
        state.log(f"   Colors:     {len(state.mobile_raw.colors)} unique")
        state.log(f"   Typography: {len(state.mobile_raw.typography)} styles")
        state.log(f"   Spacing:    {len(state.mobile_raw.spacing)} values")
        state.log(f"   Radius:     {len(state.mobile_raw.radius)} values")
        state.log(f"   Shadows:    {len(state.mobile_raw.shadows)} values")
        
        # Normalize mobile
        state.log("")
        state.log("🔄 Normalizing...")
        state.mobile_normalized = normalizer_mod.normalize_tokens(state.mobile_raw)
        state.log(f"   ✅ Normalized: {len(state.mobile_normalized.colors)} colors, {len(state.mobile_normalized.typography)} typography, {len(state.mobile_normalized.spacing)} spacing")
        
        # === FIRECRAWL CSS EXTRACTION (Agent 1B) ===
        progress(0.88, desc="🔥 Firecrawl CSS analysis...")
        
        try:
            from agents.firecrawl_extractor import extract_css_colors
            
            # Get base URL for Firecrawl
            base_url = selected_urls[0] if selected_urls else state.base_url
            
            # Extract CSS colors using Firecrawl
            firecrawl_result = await extract_css_colors(
                url=base_url,
                api_key=None,  # Will use fallback method
                log_callback=state.log
            )
            
            # Merge Firecrawl colors into desktop normalized
            firecrawl_colors = firecrawl_result.get("colors", {})
            
            if firecrawl_colors:
                state.log("")
                state.log("🔀 Merging Firecrawl colors with Playwright extraction...")
                
                # Count new colors
                new_colors_count = 0
                
                for hex_val, color_data in firecrawl_colors.items():
                    # Check if this color already exists
                    existing = False
                    for name, existing_color in state.desktop_normalized.colors.items():
                        if existing_color.value.lower() == hex_val.lower():
                            existing = True
                            # Update frequency
                            existing_color.frequency += color_data.get("frequency", 1)
                            if "firecrawl" not in existing_color.contexts:
                                existing_color.contexts.append("firecrawl")
                            break
                    
                    if not existing:
                        # Add new color from Firecrawl
                        from core.token_schema import ColorToken, TokenSource, Confidence
                        
                        new_token = ColorToken(
                            value=hex_val,
                            frequency=color_data.get("frequency", 1),
                            contexts=["firecrawl"] + color_data.get("contexts", []),
                            elements=["css-file"],
                            css_properties=color_data.get("sources", []),
                            contrast_white=color_data.get("contrast_white", 0),
                            contrast_black=color_data.get("contrast_black", 0),
                            source=TokenSource.DETECTED,
                            confidence=Confidence.MEDIUM,
                        )
                        
                        # Generate name based on color characteristics (not garbage like firecrawl.34)
                        # This will be a fallback; semantic analysis may override later
                        new_token.suggested_name = None  # Let consolidation generate proper name

                        state.desktop_normalized.colors[hex_val] = new_token
                        new_colors_count += 1
                
                state.log(f"   ✅ Added {new_colors_count} new colors from Firecrawl")
                state.log(f"   📊 Total colors now: {len(state.desktop_normalized.colors)}")
        
        except Exception as e:
            state.log(f"   ⚠️ Firecrawl extraction skipped: {str(e)}")
        
        # === SEMANTIC COLOR ANALYSIS (Agent 1C) ===
        progress(0.92, desc="🧠 Semantic color analysis...")
        
        semantic_result = {}
        semantic_preview_html = ""
        
        try:
            from agents.semantic_analyzer import SemanticColorAnalyzer, generate_semantic_preview_html
            
            # Create analyzer (using rule-based for now, can add LLM later)
            semantic_analyzer = SemanticColorAnalyzer(llm_provider=None)
            
            # Run analysis
            semantic_result = semantic_analyzer.analyze_sync(
                colors=state.desktop_normalized.colors,
                log_callback=state.log
            )
            
            # Store in state for Stage 2
            state.semantic_analysis = semantic_result
            
            # Generate preview HTML
            semantic_preview_html = generate_semantic_preview_html(semantic_result)
            
        except Exception as e:
            state.log(f"   ⚠️ Semantic analysis skipped: {str(e)}")
            import traceback
            state.log(traceback.format_exc())
        
        progress(0.95, desc="📊 Preparing results...")
        
        # Format results for Stage 1 UI
        desktop_data = format_tokens_for_display(state.desktop_normalized)
        mobile_data = format_tokens_for_display(state.mobile_normalized)
        
        # Generate visual previews - AS-IS for Stage 1 (no ramps, no enhancements)
        state.log("")
        state.log("🎨 Generating AS-IS visual previews...")
        
        from core.preview_generator import (
            generate_typography_preview_html,
            generate_colors_asis_preview_html,
            generate_spacing_asis_preview_html,
            generate_radius_asis_preview_html,
            generate_shadows_asis_preview_html,
        )
        
        # Get detected font
        fonts = get_detected_fonts()
        primary_font = fonts.get("primary", "Open Sans")
        
        # Convert typography tokens to dict format for preview
        typo_dict = {}
        for name, t in state.desktop_normalized.typography.items():
            typo_dict[name] = {
                "font_size": t.font_size,
                "font_weight": t.font_weight,
                "line_height": t.line_height or "1.5",
                "letter_spacing": "0",
            }
        
        # Convert color tokens to dict format for preview (with full metadata)
        color_dict = {}
        for name, c in state.desktop_normalized.colors.items():
            color_dict[name] = {
                "value": c.value,
                "frequency": c.frequency,
                "contexts": c.contexts[:3] if c.contexts else [],
                "elements": c.elements[:3] if c.elements else [],
                "css_properties": c.css_properties[:3] if c.css_properties else [],
                "contrast_white": c.contrast_white,
                "contrast_black": getattr(c, 'contrast_black', 0),
            }
        
        # Convert spacing tokens to dict format
        spacing_dict = {}
        for name, s in state.desktop_normalized.spacing.items():
            spacing_dict[name] = {
                "value": s.value,
                "value_px": s.value_px,
            }
        
        # Convert radius tokens to dict format
        radius_dict = {}
        for name, r in state.desktop_normalized.radius.items():
            radius_dict[name] = {"value": r.value}
        
        # Convert shadow tokens to dict format
        shadow_dict = {}
        for name, s in state.desktop_normalized.shadows.items():
            shadow_dict[name] = {"value": s.value}
        
        # Generate AS-IS previews (Stage 1 - raw extracted values)
        typography_preview_html = generate_typography_preview_html(
            typography_tokens=typo_dict,
            font_family=primary_font,
            sample_text="The quick brown fox jumps over the lazy dog",
        )
        
        # AS-IS color preview (no ramps)
        colors_asis_preview_html = generate_colors_asis_preview_html(
            color_tokens=color_dict,
        )
        
        # AS-IS spacing preview
        spacing_asis_preview_html = generate_spacing_asis_preview_html(
            spacing_tokens=spacing_dict,
        )
        
        # AS-IS radius preview
        radius_asis_preview_html = generate_radius_asis_preview_html(
            radius_tokens=radius_dict,
        )
        
        # AS-IS shadows preview
        shadows_asis_preview_html = generate_shadows_asis_preview_html(
            shadow_tokens=shadow_dict,
        )
        
        state.log("   ✅ Typography preview generated")
        state.log("   ✅ Colors AS-IS preview generated (no ramps)")
        state.log("   ✅ Semantic color analysis preview generated")
        state.log("   ✅ Spacing AS-IS preview generated")
        state.log("   ✅ Radius AS-IS preview generated")
        state.log("   ✅ Shadows AS-IS preview generated")
        
        # Get semantic summary for status
        brand_count = len(semantic_result.get("brand", {}))
        text_count = len(semantic_result.get("text", {}))
        bg_count = len(semantic_result.get("background", {}))
        
        state.log("")
        state.log("=" * 50)
        state.log("✅ EXTRACTION COMPLETE!")
        state.log(f"   Enhanced extraction captured:")
        state.log(f"   • {len(state.desktop_normalized.colors)} colors (DOM + CSS vars + SVG + inline)")
        state.log(f"   • {len(state.desktop_normalized.typography)} typography styles")
        state.log(f"   • {len(state.desktop_normalized.spacing)} spacing values")
        state.log(f"   • {len(state.desktop_normalized.radius)} radius values")
        state.log(f"   • {len(state.desktop_normalized.shadows)} shadow values")
        state.log(f"   Semantic Analysis:")
        state.log(f"   • {brand_count} brand colors identified")
        state.log(f"   • {text_count} text colors identified")
        state.log(f"   • {bg_count} background colors identified")
        state.log("=" * 50)
        
        progress(1.0, desc="✅ Complete!")
        
        status = f"""## ✅ Extraction Complete!

| Viewport | Colors | Typography | Spacing | Radius | Shadows |
|----------|--------|------------|---------|--------|---------|
| Desktop | {len(state.desktop_normalized.colors)} | {len(state.desktop_normalized.typography)} | {len(state.desktop_normalized.spacing)} | {len(state.desktop_normalized.radius)} | {len(state.desktop_normalized.shadows)} |
| Mobile | {len(state.mobile_normalized.colors)} | {len(state.mobile_normalized.typography)} | {len(state.mobile_normalized.spacing)} | {len(state.mobile_normalized.radius)} | {len(state.mobile_normalized.shadows)} |

**Primary Font:** {primary_font}

**Semantic Analysis:** {brand_count} brand, {text_count} text, {bg_count} background colors

**Enhanced Extraction:** DOM + CSS Variables + SVG + Inline + Stylesheets + Firecrawl

**Next:** Review the tokens below. Accept or reject, then proceed to Stage 2.
"""
        
        # Return all AS-IS previews including semantic
        return (
            status, 
            state.get_logs(), 
            desktop_data, 
            mobile_data, 
            typography_preview_html, 
            colors_asis_preview_html,
            semantic_preview_html,
            spacing_asis_preview_html,
            radius_asis_preview_html,
            shadows_asis_preview_html,
        )
        
    except Exception as e:
        import traceback
        state.log(f"❌ Error: {str(e)}")
        state.log(traceback.format_exc())
        error_detail = str(e).lower()
        if "timeout" in error_detail or "navigation" in error_detail:
            hint = "The page took too long to load. Try selecting fewer pages, or check if the site requires authentication."
        elif "no tokens" in error_detail or "empty" in error_detail:
            hint = "No design tokens could be extracted. The site may use unusual CSS patterns. Try a different page selection."
        else:
            hint = "Check the log above for details. Try selecting fewer pages or a different set of pages."
        return (error_message("Extraction Failed", str(e)[:200], hint),
                state.get_logs(), None, None, "", "", "", "", "", "")


def format_tokens_for_display(normalized) -> dict:
    """Format normalized tokens for Gradio display."""
    if normalized is None:
        return {"colors": [], "typography": [], "spacing": []}
    
    # Colors are now a dict
    colors = []
    color_items = list(normalized.colors.values()) if isinstance(normalized.colors, dict) else normalized.colors
    for c in sorted(color_items, key=lambda x: -x.frequency)[:50]:
        colors.append([
            True,  # Accept checkbox
            c.value,
            c.suggested_name or "",
            c.frequency,
            c.confidence.value if c.confidence else "medium",
            f"{c.contrast_white:.1f}:1" if c.contrast_white else "N/A",
            "✓" if c.wcag_aa_small_text else "✗",
            ", ".join(c.contexts[:2]) if c.contexts else "",
        ])
    
    # Typography
    typography = []
    typo_items = list(normalized.typography.values()) if isinstance(normalized.typography, dict) else normalized.typography
    for t in sorted(typo_items, key=lambda x: -x.frequency)[:30]:
        typography.append([
            True,  # Accept checkbox
            t.font_family,
            t.font_size,
            str(t.font_weight),
            t.line_height or "",
            t.suggested_name or "",
            t.frequency,
            t.confidence.value if t.confidence else "medium",
        ])
    
    # Spacing
    spacing = []
    spacing_items = list(normalized.spacing.values()) if isinstance(normalized.spacing, dict) else normalized.spacing
    for s in sorted(spacing_items, key=lambda x: x.value_px)[:20]:
        spacing.append([
            True,  # Accept checkbox
            s.value,
            f"{s.value_px}px",
            s.suggested_name or "",
            s.frequency,
            "✓" if s.fits_base_8 else "",
            s.confidence.value if s.confidence else "medium",
        ])
    
    # Radius
    radius = []
    radius_items = list(normalized.radius.values()) if isinstance(normalized.radius, dict) else normalized.radius
    for r in sorted(radius_items, key=lambda x: -x.frequency)[:20]:
        radius.append([
            True,  # Accept checkbox
            r.value,
            r.frequency,
            ", ".join(r.elements[:3]) if r.elements else "",
        ])

    return {
        "colors": colors,
        "typography": typography,
        "spacing": spacing,
        "radius": radius,
    }


def switch_viewport(viewport: str):
    """Switch between desktop and mobile view."""
    if viewport == "Desktop (1440px)":
        data = format_tokens_for_display(state.desktop_normalized)
    else:
        data = format_tokens_for_display(state.mobile_normalized)

    return data["colors"], data["typography"], data["spacing"], data["radius"]


# =============================================================================
# STAGE 2: AI ANALYSIS (Multi-Agent)
# =============================================================================

async def run_stage2_analysis(competitors_str: str = "", progress=gr.Progress()):
    """Run multi-agent analysis on extracted tokens."""
    
    if not state.desktop_normalized or not state.mobile_normalized:
        return ("❌ Please complete Stage 1 first", "", "", "", None, None, None, "", "", "", "")
    
    # Parse competitors from input
    default_competitors = [
        "Material Design 3",
        "Apple Human Interface Guidelines", 
        "Shopify Polaris",
        "IBM Carbon",
        "Atlassian Design System"
    ]
    
    if competitors_str and competitors_str.strip():
        competitors = [c.strip() for c in competitors_str.split(",") if c.strip()]
    else:
        competitors = default_competitors
    
    progress(0.05, desc="🤖 Initializing multi-agent analysis...")
    
    try:
        # Import the multi-agent workflow
        from agents.stage2_graph import run_stage2_multi_agent
        
        # Convert normalized tokens to dict for the workflow
        desktop_dict = normalized_to_dict(state.desktop_normalized)
        mobile_dict = normalized_to_dict(state.mobile_normalized)
        
        # Run multi-agent analysis with semantic context
        progress(0.1, desc="🚀 Running parallel LLM analysis...")
        
        result = await run_stage2_multi_agent(
            desktop_tokens=desktop_dict,
            mobile_tokens=mobile_dict,
            competitors=competitors,
            log_callback=state.log,
            semantic_analysis=getattr(state, 'semantic_analysis', None),  # Pass semantic context!
        )
        
        progress(0.8, desc="📊 Processing results...")
        
        # Extract results
        final_recs = result.get("final_recommendations", {})
        llm1_analysis = result.get("llm1_analysis", {})
        llm2_analysis = result.get("llm2_analysis", {})
        rule_calculations = result.get("rule_calculations", {})
        cost_tracking = result.get("cost_tracking", {})
        
        # Store for later use
        state.upgrade_recommendations = final_recs
        state.multi_agent_result = result
        
        # Get font info
        fonts = get_detected_fonts()
        base_size = get_base_font_size()
        
        progress(0.9, desc="📊 Formatting results...")
        
        # Build status markdown
        status = build_analysis_status(final_recs, cost_tracking, result.get("errors", []))
        
        # Format brand/competitor comparison from LLM analyses
        brand_md = format_multi_agent_comparison(llm1_analysis, llm2_analysis, final_recs)
        
        # Format font families display
        font_families_md = format_font_families_display(fonts)
        
        # Format typography with BOTH desktop and mobile
        typography_desktop_data = format_typography_comparison_viewport(
            state.desktop_normalized, base_size, "desktop"
        )
        typography_mobile_data = format_typography_comparison_viewport(
            state.mobile_normalized, base_size, "mobile"
        )
        
        # Format spacing comparison table
        spacing_data = format_spacing_comparison_from_rules(rule_calculations)
        
        # Format color display: BASE colors + ramps separately
        base_colors_md = format_base_colors()
        color_ramps_md = format_color_ramps_from_rules(rule_calculations)
        
        # Format radius display (with token suggestions)
        radius_md = format_radius_with_tokens()
        
        # Format shadows display (with token suggestions)
        shadows_md = format_shadows_with_tokens()
        
        # Generate visual previews for Stage 2
        state.log("")
        state.log("🎨 Generating visual previews...")
        
        from core.preview_generator import (
            generate_typography_preview_html, 
            generate_color_ramps_preview_html,
            generate_semantic_color_ramps_html
        )
        
        primary_font = fonts.get("primary", "Open Sans")
        
        # Convert typography tokens to dict format for preview
        typo_dict = {}
        for name, t in state.desktop_normalized.typography.items():
            typo_dict[name] = {
                "font_size": t.font_size,
                "font_weight": t.font_weight,
                "line_height": t.line_height or "1.5",
                "letter_spacing": "0",
            }
        
        # Convert color tokens to dict format for preview (with frequency for sorting)
        color_dict = {}
        for name, c in state.desktop_normalized.colors.items():
            color_dict[name] = {
                "value": c.value,
                "frequency": c.frequency,
            }
        
        typography_preview_html = generate_typography_preview_html(
            typography_tokens=typo_dict,
            font_family=primary_font,
            sample_text="The quick brown fox jumps over the lazy dog",
        )
        
        # Use semantic color ramps if available, otherwise fallback to regular
        semantic_analysis = getattr(state, 'semantic_analysis', None)
        if semantic_analysis:
            # Extract LLM color recommendations
            llm_color_recs = {}
            if final_recs and isinstance(final_recs, dict):
                llm_color_recs = final_recs.get("color_recommendations", {})
                # Also add accessibility fixes
                aa_fixes = final_recs.get("accessibility_fixes", [])
                if aa_fixes:
                    llm_color_recs["changes_made"] = [
                        f"AA fix suggested for {f.get('color', '?')}" 
                        for f in aa_fixes if isinstance(f, dict)
                    ][:5]
            
            color_ramps_preview_html = generate_semantic_color_ramps_html(
                semantic_analysis=semantic_analysis,
                color_tokens=color_dict,
                llm_recommendations={"color_recommendations": llm_color_recs} if llm_color_recs else None,
            )
            state.log("   ✅ Semantic color ramps preview generated (with LLM recommendations)")
        else:
            color_ramps_preview_html = generate_color_ramps_preview_html(
                color_tokens=color_dict,
            )
            state.log("   ✅ Color ramps preview generated (no semantic data)")
        
        state.log("   ✅ Typography preview generated")
        
        # Generate LLM recommendations display
        llm_recs_html = format_llm_color_recommendations_html(final_recs, semantic_analysis)
        llm_recs_table = format_llm_color_recommendations_table(final_recs, semantic_analysis)
        
        state.log("   ✅ LLM recommendations formatted")
        
        progress(1.0, desc="✅ Analysis complete!")
        
        return (status, state.get_logs(), brand_md, font_families_md,
                typography_desktop_data, typography_mobile_data, spacing_data, 
                base_colors_md, color_ramps_md, radius_md, shadows_md,
                typography_preview_html, color_ramps_preview_html,
                llm_recs_html, llm_recs_table)
        
    except Exception as e:
        import traceback
        state.log(f"❌ Error: {str(e)}")
        state.log(traceback.format_exc())
        return (f"❌ Analysis failed: {str(e)}", state.get_logs(), "", "", None, None, None, "", "", "", "", "", "", "", [])


def normalized_to_dict(normalized) -> dict:
    """Convert NormalizedTokens to dict for workflow."""
    if not normalized:
        return {}
    
    result = {
        "colors": {},
        "typography": {},
        "spacing": {},
        "radius": {},
        "shadows": {},
    }
    
    # Colors
    for name, c in normalized.colors.items():
        result["colors"][name] = {
            "value": c.value,
            "frequency": c.frequency,
            "suggested_name": c.suggested_name,
            "contrast_white": c.contrast_white,
            "contrast_black": c.contrast_black,
        }
    
    # Typography
    for name, t in normalized.typography.items():
        result["typography"][name] = {
            "font_family": t.font_family,
            "font_size": t.font_size,
            "font_weight": t.font_weight,
            "line_height": t.line_height,
            "frequency": t.frequency,
        }
    
    # Spacing
    for name, s in normalized.spacing.items():
        result["spacing"][name] = {
            "value": s.value,
            "value_px": s.value_px,
            "frequency": s.frequency,
        }
    
    # Radius
    for name, r in normalized.radius.items():
        result["radius"][name] = {
            "value": r.value,
            "frequency": r.frequency,
        }
    
    # Shadows
    for name, s in normalized.shadows.items():
        result["shadows"][name] = {
            "value": s.value,
            "frequency": s.frequency,
        }
    
    return result


# =============================================================================
# STAGE 2: NEW ARCHITECTURE (Rule Engine + Benchmark Research + LLM Agents)
# =============================================================================

async def run_stage2_analysis_v2(
    selected_benchmarks: list[str] = None,
    progress=gr.Progress()
):
    """
    Run Stage 2 analysis with new architecture:
    - Layer 1: Rule Engine (FREE)
    - Layer 2: Benchmark Research (Firecrawl + Cache)
    - Layer 3: LLM Agents (Brand ID, Benchmark Advisor, Best Practices)
    - Layer 4: HEAD Synthesizer
    
    Includes comprehensive error handling for graceful degradation.
    """
    
    # Validate Stage 1 completion
    if not state.desktop_normalized or not state.mobile_normalized:
        return create_stage2_error_response(
            error_message("Stage 1 Not Complete",
                          "No extracted tokens found. Stage 1 extraction must be completed before running analysis.",
                          "Go back to **Step 1**, enter a URL, discover pages, and extract tokens first.")
        )
    
    # Default benchmarks if none selected
    if not selected_benchmarks or len(selected_benchmarks) == 0:
        selected_benchmarks = [
            "material_design_3",
            "shopify_polaris", 
            "atlassian_design",
        ]
    
    state.log("")
    state.log("═" * 60)
    state.log("🚀 STAGE 2: MULTI-AGENT ANALYSIS")
    state.log("═" * 60)
    state.log(f"   Started: {datetime.now().strftime('%H:%M:%S')}")
    state.log(f"   Benchmarks: {', '.join(selected_benchmarks)}")
    state.log("")
    
    # Import dataclasses early so fallbacks always work
    # (even if the full agent import fails at runtime)
    try:
        from agents.llm_agents import (
            BrandIdentification,
            BenchmarkAdvice,
            BestPracticesResult,
        )
    except ImportError:
        # Minimal fallback dataclasses if agents module unavailable
        from dataclasses import dataclass, field
        @dataclass
        class BrandIdentification:
            brand_primary: dict = field(default_factory=dict)
            brand_secondary: dict = field(default_factory=dict)
            brand_accent: dict = field(default_factory=dict)
            palette_strategy: str = ""
            cohesion_score: int = 5
            cohesion_notes: str = ""
            semantic_names: dict = field(default_factory=dict)
            self_evaluation: dict = field(default_factory=dict)
            def to_dict(self):
                return {k: getattr(self, k) for k in ['brand_primary', 'brand_secondary', 'brand_accent', 'palette_strategy', 'cohesion_score', 'cohesion_notes', 'semantic_names', 'self_evaluation']}

        @dataclass
        class BenchmarkAdvice:
            recommended_benchmark: str = ""
            recommended_benchmark_name: str = ""
            reasoning: str = ""
            alignment_changes: list = field(default_factory=list)
            pros_of_alignment: list = field(default_factory=list)
            cons_of_alignment: list = field(default_factory=list)
            alternative_benchmarks: list = field(default_factory=list)
            def to_dict(self):
                return {k: getattr(self, k) for k in ['recommended_benchmark', 'recommended_benchmark_name', 'reasoning', 'alignment_changes', 'pros_of_alignment', 'cons_of_alignment', 'alternative_benchmarks']}

        @dataclass
        class BestPracticesResult:
            overall_score: int = 50
            checks: dict = field(default_factory=dict)
            priority_fixes: list = field(default_factory=list)
            passing_practices: list = field(default_factory=list)
            failing_practices: list = field(default_factory=list)
            def to_dict(self):
                return {k: getattr(self, k) for k in ['overall_score', 'checks', 'priority_fixes', 'passing_practices', 'failing_practices']}

    # Initialize results with defaults (for graceful degradation)
    rule_results = None
    benchmark_comparisons = []
    brand_result = None
    benchmark_advice = None
    best_practices = None
    final_synthesis = None
    
    progress(0.05, desc="⚙️ Running Rule Engine...")
    
    try:
        # =================================================================
        # LAYER 1: RULE ENGINE (FREE) - Critical, must succeed
        # =================================================================
        try:
            from core.rule_engine import run_rule_engine
            
            # Convert tokens to dict
            desktop_dict = normalized_to_dict(state.desktop_normalized)
            mobile_dict = normalized_to_dict(state.mobile_normalized)
            
            # Validate we have data
            if not desktop_dict.get("colors") and not desktop_dict.get("typography"):
                raise ValueError("No tokens extracted from Stage 1")
            
            # Run rule engine
            rule_results = run_rule_engine(
                typography_tokens=desktop_dict.get("typography", {}),
                color_tokens=desktop_dict.get("colors", {}),
                spacing_tokens=desktop_dict.get("spacing", {}),
                radius_tokens=desktop_dict.get("radius", {}),
                shadow_tokens=desktop_dict.get("shadows", {}),
                log_callback=state.log,
                fg_bg_pairs=getattr(state, 'fg_bg_pairs', None),
            )
            
            state.rule_engine_results = rule_results
            state.log("")
            state.log("   ✅ Rule Engine: SUCCESS")
            
        except Exception as e:
            state.log(f"   ❌ Rule Engine FAILED: {str(e)[:100]}")
            state.log("   └─ Cannot proceed without rule engine results")
            import traceback
            state.log(traceback.format_exc()[:500])
            return create_stage2_error_response(
                error_message("Rule Engine Failed",
                              f"The rule engine could not analyze your tokens: {str(e)[:150]}",
                              "This usually means the extracted tokens are incomplete. Try re-running Stage 1 extraction with different pages selected.")
            )
        
        progress(0.20, desc="🔬 Researching benchmarks...")
        
        # =================================================================
        # LAYER 2: BENCHMARK RESEARCH - Can use fallback
        # =================================================================
        try:
            from agents.benchmark_researcher import BenchmarkResearcher, FALLBACK_BENCHMARKS, BenchmarkData
            
            # Try to get Firecrawl client (optional)
            firecrawl_client = None
            try:
                from agents.firecrawl_extractor import get_firecrawl_client
                firecrawl_client = get_firecrawl_client()
                state.log("   ├─ Firecrawl client: Available")
            except Exception as fc_err:
                state.log(f"   ├─ Firecrawl client: Not available ({str(fc_err)[:30]})")
                state.log("   │  └─ Will use cached/fallback data")
            
            # Get HF client for LLM extraction (optional)
            hf_client = None
            try:
                from core.hf_inference import get_inference_client
                hf_client = get_inference_client()
                state.log("   ├─ HF client: Available")
            except Exception as hf_err:
                state.log(f"   ├─ HF client: Not available ({str(hf_err)[:30]})")
            
            researcher = BenchmarkResearcher(
                firecrawl_client=firecrawl_client,
                hf_client=hf_client,
            )
            
            # Research selected benchmarks (with fallback)
            try:
                benchmarks = await researcher.research_selected_benchmarks(
                    selected_keys=selected_benchmarks,
                    log_callback=state.log,
                )
            except Exception as research_err:
                state.log(f"   ⚠️ Research failed, using fallback: {str(research_err)[:50]}")
                # Use fallback data
                benchmarks = []
                for key in selected_benchmarks:
                    if key in FALLBACK_BENCHMARKS:
                        data = FALLBACK_BENCHMARKS[key]
                        benchmarks.append(BenchmarkData(
                            key=key,
                            name=key.replace("_", " ").title(),
                            short_name=key.split("_")[0].title(),
                            vendor="",
                            icon="📦",
                            typography=data.get("typography", {}),
                            spacing=data.get("spacing", {}),
                            colors=data.get("colors", {}),
                            fetched_at=datetime.now().isoformat(),
                            confidence="fallback",
                            best_for=[],
                        ))
            
            # Compare to benchmarks
            if benchmarks and rule_results:
                benchmark_comparisons = researcher.compare_to_benchmarks(
                    your_ratio=rule_results.typography.detected_ratio,
                    your_base_size=int(rule_results.typography.base_size) if rule_results.typography.sizes_px else 16,
                    your_spacing_grid=rule_results.spacing.detected_base,
                    benchmarks=benchmarks,
                    log_callback=state.log,
                )
                state.benchmark_comparisons = benchmark_comparisons
                state.log("")
                state.log(f"   ✅ Benchmark Research: SUCCESS ({len(benchmarks)} systems)")
            else:
                state.log("   ⚠️ No benchmarks available for comparison")
            
        except Exception as e:
            state.log(f"   ⚠️ Benchmark Research FAILED: {str(e)[:100]}")
            state.log("   └─ Continuing without benchmark comparison...")
            benchmark_comparisons = []
        
        progress(0.40, desc="🤖 Running LLM Agents...")
        
        # =================================================================
        # LAYER 3: LLM AGENTS - Can fail gracefully
        # =================================================================
        try:
            from agents.llm_agents import (
                BrandIdentifierAgent,
                BenchmarkAdvisorAgent,
                BestPracticesValidatorAgent,
                BrandIdentification,
                BenchmarkAdvice,
                BestPracticesResult,
            )
            
            state.log("")
            state.log("═" * 60)
            state.log("🤖 LAYER 3: LLM ANALYSIS")
            state.log("═" * 60)
            
            # Check if HF client is available
            if not hf_client:
                try:
                    from core.hf_inference import get_inference_client
                    hf_client = get_inference_client()
                except Exception:
                    state.log("   ⚠️ HF client not available - skipping LLM agents")
                    hf_client = None
            
            if hf_client:
                # Initialize agents
                brand_agent = BrandIdentifierAgent(hf_client)
                benchmark_agent = BenchmarkAdvisorAgent(hf_client)
                best_practices_agent = BestPracticesValidatorAgent(hf_client)
                
                # Get semantic analysis from Stage 1
                semantic_analysis = getattr(state, 'semantic_analysis', {})
                desktop_dict = normalized_to_dict(state.desktop_normalized)
                
                # Run AURORA, ATLAS, SENTINEL in PARALLEL (then NEXUS sequentially)
                async def _run_aurora():
                    """Run AURORA (Brand Identifier) agent."""
                    try:
                        result = await brand_agent.analyze(
                            color_tokens=desktop_dict.get("colors", {}),
                            semantic_analysis=semantic_analysis,
                            log_callback=state.log,
                        )
                        if result:
                            bp = result.brand_primary or {}
                            bs = result.brand_secondary or {}
                            state.log(f"   ├─ Brand Primary: {bp.get('color', 'N/A')} ({bp.get('confidence', 'N/A')} confidence)")
                            state.log(f"   │  └─ Reasoning: {bp.get('reasoning', 'N/A')[:80]}")
                            state.log(f"   ├─ Brand Secondary: {bs.get('color', 'N/A')}")
                            state.log(f"   ├─ Palette Strategy: {result.palette_strategy or 'N/A'}")
                            state.log(f"   └─ Cohesion Score: {result.cohesion_score}/10 — {result.cohesion_notes[:60] if result.cohesion_notes else 'N/A'}")
                        return result
                    except Exception as e:
                        state.log(f"   ⚠️ AURORA (Brand Identifier) failed: {str(e)[:120]}")
                        return BrandIdentification()

                async def _run_atlas():
                    """Run ATLAS (Benchmark Advisor) agent."""
                    if not benchmark_comparisons:
                        return BenchmarkAdvice()
                    try:
                        result = await benchmark_agent.analyze(
                            user_ratio=rule_results.typography.detected_ratio,
                            user_base=int(rule_results.typography.base_size) if rule_results.typography.sizes_px else 16,
                            user_spacing=rule_results.spacing.detected_base,
                            benchmark_comparisons=benchmark_comparisons,
                            log_callback=state.log,
                        )
                        if result:
                            state.log(f"   ├─ Recommended: {result.recommended_benchmark_name or result.recommended_benchmark or 'N/A'}")
                            state.log(f"   │  └─ Reasoning: {result.reasoning[:80] if result.reasoning else 'N/A'}")
                            changes = result.alignment_changes or []
                            state.log(f"   ├─ Changes Needed: {len(changes)}")
                            for i, ch in enumerate(changes[:3]):
                                if isinstance(ch, dict):
                                    state.log(f"   │  {i+1}. {ch.get('change', ch.get('what', str(ch)[:60]))}")
                                else:
                                    state.log(f"   │  {i+1}. {str(ch)[:60]}")
                            if result.pros_of_alignment:
                                state.log(f"   └─ Pros: {', '.join(str(p)[:30] for p in result.pros_of_alignment[:2])}")
                        return result
                    except Exception as e:
                        state.log(f"   ⚠️ ATLAS (Benchmark Advisor) failed: {str(e)[:120]}")
                        return BenchmarkAdvice()

                async def _run_sentinel():
                    """Run SENTINEL (Best Practices Validator) agent."""
                    try:
                        # Get shadow tokens from desktop_dict for analysis
                        shadow_tokens = desktop_dict.get("shadows", {})
                        result = await best_practices_agent.analyze(
                            rule_engine_results=rule_results,
                            shadow_tokens=shadow_tokens,
                            log_callback=state.log,
                        )
                        if result:
                            state.log(f"   ├─ Overall Score: {result.overall_score}/100")
                            passing_count = len(result.passing_practices) if result.passing_practices else 0
                            failing_count = len(result.failing_practices) if result.failing_practices else 0
                            state.log(f"   ├─ Passing: {passing_count} | Failing: {failing_count}")
                            if result.checks:
                                for check_name, check_data in list(result.checks.items())[:3]:
                                    if isinstance(check_data, dict):
                                        status = check_data.get('status', '?')
                                        note = check_data.get('note', '')[:50]
                                        icon = "✅" if status == "pass" else "⚠️" if status == "warn" else "❌"
                                        state.log(f"   │  {icon} {check_name}: {note}")
                                    else:
                                        state.log(f"   │  • {check_name}: {check_data}")
                            if result.priority_fixes:
                                top_fix = result.priority_fixes[0]
                                if isinstance(top_fix, dict):
                                    state.log(f"   └─ Top Fix: {top_fix.get('issue', top_fix.get('action', str(top_fix)[:60]))}")
                                else:
                                    state.log(f"   └─ Top Fix: {str(top_fix)[:60]}")
                        return result
                    except Exception as e:
                        state.log(f"   ⚠️ SENTINEL (Best Practices) failed: {str(e)[:120]}")
                        return BestPracticesResult(overall_score=rule_results.consistency_score)

                # Execute AURORA + ATLAS + SENTINEL in parallel
                import asyncio
                state.log("")
                state.log("   🚀 Running 3 agents in parallel: AURORA | ATLAS | SENTINEL")
                brand_result, benchmark_advice, best_practices = await asyncio.gather(
                    _run_aurora(),
                    _run_atlas(),
                    _run_sentinel(),
                )
            else:
                # No HF client - use defaults
                state.log("   └─ Using default values (no LLM)")
                brand_result = BrandIdentification()
                benchmark_advice = BenchmarkAdvice()
                best_practices = BestPracticesResult(overall_score=rule_results.consistency_score)
                
        except Exception as e:
            state.log(f"   ⚠️ LLM Agents FAILED: {str(e)[:100]}")
            brand_result = BrandIdentification() if not brand_result else brand_result
            benchmark_advice = BenchmarkAdvice() if not benchmark_advice else benchmark_advice
            best_practices = BestPracticesResult(overall_score=rule_results.consistency_score if rule_results else 50)
        
        progress(0.70, desc="🧠 Synthesizing results...")
        
        # =================================================================
        # LAYER 4: HEAD SYNTHESIZER - Can use fallback
        # =================================================================
        try:
            from agents.llm_agents import HeadSynthesizerAgent, HeadSynthesis
            
            if hf_client and brand_result and benchmark_advice and best_practices:
                head_agent = HeadSynthesizerAgent(hf_client)
                
                try:
                    final_synthesis = await head_agent.synthesize(
                        rule_engine_results=rule_results,
                        benchmark_comparisons=benchmark_comparisons,
                        brand_identification=brand_result,
                        benchmark_advice=benchmark_advice,
                        best_practices=best_practices,
                        log_callback=state.log,
                    )
                    if final_synthesis:
                        # Detailed logging is handled by NEXUS agent persona itself
                        if final_synthesis.executive_summary:
                            state.log(f"   📝 Summary: {final_synthesis.executive_summary[:120]}...")
                except Exception as e:
                    state.log(f"   ⚠️ HEAD Synthesizer failed: {str(e)[:120]}")
                    import traceback
                    state.log(f"   └─ {traceback.format_exc()[:200]}")
                    final_synthesis = None
            
            # Create fallback synthesis if needed
            if not final_synthesis:
                state.log("   └─ Creating fallback synthesis...")
                final_synthesis = create_fallback_synthesis(
                    rule_results, benchmark_comparisons, brand_result, best_practices
                )
            
            state.final_synthesis = final_synthesis
            state.brand_result = brand_result  # Preserve AURORA semantic_names for export

            # ═══════════════════════════════════════
            # AGENT EVALUATION SUMMARY
            # ═══════════════════════════════════════
            state.log("")
            state.log("═" * 50)
            state.log("🔍 AGENT EVALUATION SUMMARY")
            state.log("═" * 50)

            def _eval_line(name, emoji, result_obj):
                se = getattr(result_obj, 'self_evaluation', None) or {}
                if isinstance(se, dict) and se:
                    conf = se.get('confidence', '?')
                    dq = se.get('data_quality', '?')
                    flags = se.get('flags', [])
                    flag_str = f", flags={flags}" if flags else ""
                    return f"   {emoji} {name}: confidence={conf}/10, data={dq}{flag_str}"
                return f"   {emoji} {name}: no self-evaluation returned"

            if brand_result:
                state.log(_eval_line("AURORA  (Brand ID)", "🎨", brand_result))
            if benchmark_advice:
                state.log(_eval_line("ATLAS   (Benchmark)", "🏢", benchmark_advice))
            if best_practices:
                bp_se = getattr(best_practices, 'self_evaluation', None) or {}
                bp_score = getattr(best_practices, 'overall_score', '?')
                state.log(_eval_line("SENTINEL (Practices)", "✅", best_practices) + f", score={bp_score}/100")
            if final_synthesis:
                synth_se = getattr(final_synthesis, 'self_evaluation', None) or {}
                synth_overall = final_synthesis.scores.get('overall', '?') if final_synthesis.scores else '?'
                state.log(_eval_line("NEXUS   (Synthesis)", "🧠", final_synthesis) + f", overall={synth_overall}/100")

            state.log("═" * 50)
            state.log("")

        except Exception as e:
            state.log(f"   ⚠️ Synthesis FAILED: {str(e)[:100]}")
            final_synthesis = create_fallback_synthesis(
                rule_results, benchmark_comparisons, brand_result, best_practices
            )
            state.final_synthesis = final_synthesis

        progress(0.85, desc="📊 Formatting results...")
        
        # =================================================================
        # FORMAT OUTPUTS FOR UI
        # =================================================================
        
        try:
            # Build status markdown
            status_md = format_stage2_status_v2(
                rule_results=rule_results,
                final_synthesis=final_synthesis,
                best_practices=best_practices,
            )
            
            # Build benchmark comparison HTML
            benchmark_md = format_benchmark_comparison_v2(
                benchmark_comparisons=benchmark_comparisons,
                benchmark_advice=benchmark_advice,
            )
            
            # Build scores dashboard HTML
            scores_html = format_scores_dashboard_v2(
                rule_results=rule_results,
                final_synthesis=final_synthesis,
                best_practices=best_practices,
            )
            
            # Build priority actions HTML
            actions_html = format_priority_actions_v2(
                rule_results=rule_results,
                final_synthesis=final_synthesis,
                best_practices=best_practices,
            )
            
            # Build color recommendations table
            color_recs_table = format_color_recommendations_table_v2(
                rule_results=rule_results,
                brand_result=brand_result,
                final_synthesis=final_synthesis,
            )
            
            # Get fonts and typography data
            fonts = get_detected_fonts()
            base_size = get_base_font_size()

            typography_desktop_data = format_typography_comparison_viewport(
                state.desktop_normalized, base_size, "desktop"
            )
            typography_mobile_data = format_typography_comparison_viewport(
                state.mobile_normalized, base_size, "mobile"
            )

            # Generate spacing comparison table from rule_results
            spacing_data = []
            if rule_results and rule_results.spacing:
                sp = rule_results.spacing
                current_vals = sp.current_values or []
                suggested_8 = [i * 8 for i in range(1, 11)]
                suggested_4 = [i * 4 for i in range(1, 11)]
                for i in range(min(10, max(len(current_vals), 10))):
                    cur = f"{current_vals[i]}px" if i < len(current_vals) else "—"
                    g8 = f"{suggested_8[i]}px" if i < len(suggested_8) else "—"
                    g4 = f"{suggested_4[i]}px" if i < len(suggested_4) else "—"
                    spacing_data.append([cur, g8, g4])

            # Generate base colors, color ramps, radius, shadows markdown
            base_colors_md = format_base_colors()
            color_ramps_md = ""  # Visual ramps are in color_ramps_preview_html
            try:
                from core.preview_generator import generate_color_ramp
                colors = list(state.desktop_normalized.colors.values())
                colors.sort(key=lambda c: -c.frequency)
                ramp_lines = ["### 🌈 Color Ramps (Top Colors)", ""]
                for c in colors[:6]:
                    ramp = generate_color_ramp(c.value)
                    if ramp:
                        shades_str = " → ".join(f"`{s['hex']}`" for s in ramp[::2])  # every other shade
                        ramp_lines.append(f"**{c.value}** ({c.frequency}x): {shades_str}")
                        ramp_lines.append("")
                color_ramps_md = "\n".join(ramp_lines)
            except Exception:
                color_ramps_md = "*Color ramps shown in visual preview above*"

            radius_md = format_radius_with_tokens()
            shadows_md = format_shadows_with_tokens()
            
            # Generate visual previews
            typography_preview_html = ""
            color_ramps_preview_html = ""
            llm_recs_html = ""

            try:
                from core.preview_generator import (
                    generate_typography_preview_html,
                    generate_semantic_color_ramps_html,
                    generate_color_ramps_preview_html,
                )

                primary_font = fonts.get("primary", "Open Sans")
                desktop_typo_dict = {
                    name: {
                        "font_size": t.font_size,
                        "font_weight": t.font_weight,
                        "line_height": t.line_height,
                    }
                    for name, t in state.desktop_normalized.typography.items()
                }
                typography_preview_html = generate_typography_preview_html(desktop_typo_dict, primary_font)

                # Generate color ramps preview (semantic groups)
                semantic_analysis = getattr(state, 'semantic_analysis', {})
                desktop_dict_for_colors = normalized_to_dict(state.desktop_normalized)

                if semantic_analysis:
                    color_ramps_preview_html = generate_semantic_color_ramps_html(
                        semantic_analysis=semantic_analysis,
                        color_tokens=desktop_dict_for_colors.get("colors", {}),
                    )
                else:
                    color_ramps_preview_html = generate_color_ramps_preview_html(
                        color_tokens=desktop_dict_for_colors.get("colors", {}),
                    )

                state.log("   ✅ Color ramps preview generated")

            except Exception as preview_err:
                state.log(f"   ⚠️ Preview generation failed: {str(preview_err)[:80]}")
                typography_preview_html = typography_preview_html or "<div class='placeholder-msg'>Preview unavailable</div>"
                color_ramps_preview_html = "<div class='placeholder-msg'>Color ramps preview unavailable</div>"

            # Generate LLM recommendations HTML
            try:
                # Build recs dict in the format expected by the HTML formatter
                synth_recs = {}
                if final_synthesis:
                    # Convert list of color recs to dict keyed by role
                    # HeadSynthesis uses: {role, current, suggested, reason, accept}
                    # Formatter expects: {current, suggested, action, rationale}
                    color_recs_dict = {}
                    for rec in (final_synthesis.color_recommendations or []):
                        if isinstance(rec, dict) and rec.get("role"):
                            current_val = rec.get("current", "?")
                            suggested_val = rec.get("suggested", current_val)
                            accept = rec.get("accept", True)
                            reason = rec.get("reason", "")
                            # Determine action: if suggested differs from current, it's a change
                            if suggested_val and suggested_val != current_val and not accept:
                                action = "change"
                            elif suggested_val and suggested_val != current_val:
                                action = "change"
                            else:
                                action = "keep"
                            color_recs_dict[rec["role"]] = {
                                "current": current_val,
                                "suggested": suggested_val,
                                "action": action,
                                "rationale": reason,
                            }
                    synth_recs["color_recommendations"] = color_recs_dict

                    # Add AA fixes from rule engine
                    # Formatter expects: {color, role, issue, fix, current_contrast, fixed_contrast}
                    aa_fixes = []
                    if rule_results and rule_results.accessibility:
                        for a in rule_results.accessibility:
                            if not a.passes_aa_normal:
                                best_contrast = a.contrast_on_white if a.best_text_color == "#FFFFFF" else a.contrast_on_black
                                aa_fixes.append({
                                    "color": a.hex_color,
                                    "role": a.name or "unknown",
                                    "issue": f"Fails AA normal ({best_contrast:.1f}:1 < 4.5:1)",
                                    "fix": a.suggested_fix or a.hex_color,
                                    "current_contrast": f"{best_contrast:.1f}",
                                    "fixed_contrast": f"{a.suggested_fix_contrast:.1f}" if a.suggested_fix_contrast else "—",
                                })
                    synth_recs["accessibility_fixes"] = aa_fixes

                llm_recs_html = format_llm_color_recommendations_html(
                    final_recs=synth_recs,
                    semantic_analysis=getattr(state, 'semantic_analysis', {}),
                )
            except Exception as recs_err:
                state.log(f"   ⚠️ LLM recs HTML failed: {str(recs_err)[:120]}")
                import traceback
                state.log(f"   └─ {traceback.format_exc()[:200]}")
                llm_recs_html = "<div class='placeholder-msg'>LLM recommendations unavailable</div>"

            # Store upgrade_recommendations for Apply Upgrades button
            aa_failures_list = []
            if rule_results and rule_results.accessibility:
                aa_failures_list = [
                    a.to_dict() for a in rule_results.accessibility
                    if not a.passes_aa_normal
                ]
            state.upgrade_recommendations = {
                "color_recommendations": (final_synthesis.color_recommendations if final_synthesis else []),
                "accessibility_fixes": aa_failures_list,
                "scores": (final_synthesis.scores if final_synthesis else {}),
                "top_3_actions": (final_synthesis.top_3_actions if final_synthesis else []),
            }

        except Exception as format_err:
            state.log(f"   ⚠️ Formatting failed: {str(format_err)[:100]}")
            import traceback
            state.log(traceback.format_exc()[:500])
            # Return minimal results (must match 16 outputs)
            return (
                f"⚠️ Analysis completed with formatting errors: {str(format_err)[:50]}",
                state.get_logs(),
                "*Benchmark comparison unavailable*",
                "<div class='placeholder-msg'>Scores unavailable</div>",
                "<div class='placeholder-msg'>Actions unavailable</div>",
                [],
                None,
                None,
                "<div class='placeholder-msg'>Typography preview unavailable</div>",
                "<div class='placeholder-msg'>Color ramps preview unavailable</div>",
                "<div class='placeholder-msg'>LLM recommendations unavailable</div>",
                [],  # spacing_data
                "*Formatting error - base colors unavailable*",  # base_colors_md
                "*Formatting error - color ramps unavailable*",  # color_ramps_md
                "*Formatting error - radius tokens unavailable*",  # radius_md
                "*Formatting error - shadow tokens unavailable*",  # shadows_md
            )
        
        progress(0.95, desc="✅ Complete!")
        
        # Final log summary
        state.log("")
        state.log("═" * 60)
        state.log("📊 FINAL RESULTS")
        state.log("═" * 60)
        state.log("")
        overall_score = final_synthesis.scores.get('overall', rule_results.consistency_score) if final_synthesis else rule_results.consistency_score
        state.log(f"   🎯 OVERALL SCORE: {overall_score}/100")
        if final_synthesis and final_synthesis.scores:
            state.log(f"   ├─ Accessibility:  {final_synthesis.scores.get('accessibility', '?')}/100")
            state.log(f"   ├─ Consistency:    {final_synthesis.scores.get('consistency', '?')}/100")
            state.log(f"   └─ Organization:   {final_synthesis.scores.get('organization', '?')}/100")
        state.log("")
        if benchmark_comparisons:
            state.log(f"   🏆 Closest Benchmark: {benchmark_comparisons[0].benchmark.name if benchmark_comparisons else 'N/A'}")
        state.log("")
        state.log("   🎯 TOP 3 ACTIONS:")
        if final_synthesis and final_synthesis.top_3_actions:
            for i, action in enumerate(final_synthesis.top_3_actions[:3]):
                impact = action.get('impact', 'medium')
                icon = "🔴" if impact == "high" else "🟡" if impact == "medium" else "🟢"
                state.log(f"   │  {i+1}. {icon} {action.get('action', 'N/A')}")
        else:
            state.log(f"   │  1. 🔴 Fix {rule_results.aa_failures} AA compliance failures")
        state.log("")
        state.log("═" * 60)
        state.log(f"   💰 TOTAL COST: ~$0.003")
        state.log(f"   ⏱️  COMPLETED: {datetime.now().strftime('%H:%M:%S')}")
        state.log("═" * 60)
        
        return (
            status_md,
            state.get_logs(),
            benchmark_md,
            scores_html,
            actions_html,
            color_recs_table,
            typography_desktop_data,
            typography_mobile_data,
            typography_preview_html,
            color_ramps_preview_html,
            llm_recs_html,
            spacing_data,
            base_colors_md,
            color_ramps_md,
            radius_md,
            shadows_md,
        )

    except Exception as e:
        import traceback
        state.log(f"❌ Critical Error: {str(e)}")
        state.log(traceback.format_exc())
        error_detail = str(e).lower()
        if "token" in error_detail or "auth" in error_detail or "401" in error_detail:
            hint = "Your HuggingFace token may be invalid or expired. Go to **Configuration** above and re-enter your token."
        elif "rate" in error_detail or "429" in error_detail:
            hint = "Rate limit reached. Wait a few minutes and try again."
        else:
            hint = "Check the analysis log above for details. Try running the analysis again, or try Legacy Analysis as a fallback."
        return create_stage2_error_response(
            error_message("Analysis Failed", str(e)[:200], hint)
        )


def create_fallback_synthesis(rule_results, benchmark_comparisons, brand_result, best_practices):
    """Create a fallback synthesis when LLM synthesis fails."""
    try:
        from agents.llm_agents import HeadSynthesis
    except ImportError:
        from dataclasses import dataclass, field
        @dataclass
        class HeadSynthesis:
            executive_summary: str = ""
            scores: dict = field(default_factory=dict)
            benchmark_fit: dict = field(default_factory=dict)
            brand_analysis: dict = field(default_factory=dict)
            top_3_actions: list = field(default_factory=list)
            color_recommendations: list = field(default_factory=list)
            type_scale_recommendation: dict = field(default_factory=dict)
            spacing_recommendation: dict = field(default_factory=dict)
            self_evaluation: dict = field(default_factory=dict)
            def to_dict(self):
                return {k: getattr(self, k) for k in ['executive_summary', 'scores', 'benchmark_fit', 'brand_analysis', 'top_3_actions', 'color_recommendations', 'type_scale_recommendation', 'spacing_recommendation', 'self_evaluation']}
    
    # Calculate scores from rule engine
    overall = rule_results.consistency_score if rule_results else 50
    accessibility = max(0, 100 - (rule_results.aa_failures * 10)) if rule_results else 50
    
    # Build actions from rule engine
    actions = []
    if rule_results and rule_results.aa_failures > 0:
        actions.append({
            "action": f"Fix {rule_results.aa_failures} colors failing AA compliance",
            "impact": "high",
            "effort": "30 min",
        })
    if rule_results and not rule_results.typography.is_consistent:
        actions.append({
            "action": f"Align type scale to {rule_results.typography.recommendation} ({rule_results.typography.recommendation_name})",
            "impact": "medium",
            "effort": "1 hour",
        })
    if rule_results and rule_results.color_stats.unique_count > 30:
        actions.append({
            "action": f"Consolidate {rule_results.color_stats.unique_count} colors to ~15 semantic colors",
            "impact": "medium",
            "effort": "2 hours",
        })
    
    return HeadSynthesis(
        executive_summary=f"Your design system scores {overall}/100. Analysis completed with fallback synthesis.",
        scores={
            "overall": overall,
            "accessibility": accessibility,
            "consistency": overall,
            "organization": 50,
        },
        benchmark_fit={
            "closest": benchmark_comparisons[0].benchmark.name if benchmark_comparisons else "Unknown",
            "similarity": f"{benchmark_comparisons[0].overall_match_pct:.0f}%" if benchmark_comparisons else "N/A",
        },
        brand_analysis={
            "primary": brand_result.brand_primary.get("color", "Unknown") if brand_result else "Unknown",
            "cohesion": brand_result.cohesion_score if brand_result else 5,
        },
        top_3_actions=actions[:3],
        color_recommendations=[],
        type_scale_recommendation={
            "current_ratio": rule_results.typography.detected_ratio if rule_results else 1.0,
            "recommended_ratio": rule_results.typography.recommendation if rule_results else 1.25,
        },
        spacing_recommendation={
            "current": f"{rule_results.spacing.detected_base}px" if rule_results else "Unknown",
            "recommended": f"{rule_results.spacing.recommendation}px" if rule_results else "8px",
        },
    )


def create_stage2_error_response(error_msg: str):
    """Create error response tuple for Stage 2 (must match 16 outputs)."""
    return (
        error_msg,
        state.get_logs(),
        "",  # benchmark_md
        f"<div class='placeholder-msg'>{error_msg}</div>",  # scores_html
        "",  # actions_html
        [],  # color_recs_table
        None,  # typography_desktop
        None,  # typography_mobile
        "",  # typography_preview
        "",  # color_ramps_preview
        "",  # llm_recs_html
        [],  # spacing_data
        "*Run analysis to see base colors*",  # base_colors_md
        "*Run analysis to see color ramps*",  # color_ramps_md
        "*Run analysis to see radius tokens*",  # radius_md
        "*Run analysis to see shadow tokens*",  # shadows_md
    )


def format_stage2_status_v2(rule_results, final_synthesis, best_practices) -> str:
    """Format Stage 2 status with new architecture results."""
    
    lines = []
    lines.append("## ✅ Analysis Complete!")
    lines.append("")
    
    # Overall Score
    overall = final_synthesis.scores.get('overall', rule_results.consistency_score)
    lines.append(f"### 🎯 Overall Score: {overall}/100")
    lines.append("")
    
    # Executive Summary
    if final_synthesis.executive_summary:
        lines.append(f"*{final_synthesis.executive_summary}*")
        lines.append("")
    
    # Quick Stats
    lines.append("### 📊 Quick Stats")
    lines.append(f"- **AA Failures:** {rule_results.aa_failures}")
    lines.append(f"- **Type Scale:** {rule_results.typography.detected_ratio:.3f} ({rule_results.typography.scale_name})")
    lines.append(f"- **Spacing Grid:** {rule_results.spacing.detected_base}px ({rule_results.spacing.alignment_percentage:.0f}% aligned)")
    lines.append(f"- **Unique Colors:** {rule_results.color_stats.unique_count}")
    lines.append("")
    
    # Cost
    lines.append("### 💰 Cost")
    lines.append("**Total:** ~$0.003 (Rule Engine: $0 + LLM: ~$0.003)")
    lines.append("")

    # Next step guidance
    lines.append("---")
    lines.append("**Next:** Review the analysis results below. Accept or reject color recommendations, "
                 "choose your type scale and spacing grid, then click **'Apply Selected Upgrades'** at the bottom.")

    return "\n".join(lines)


def format_benchmark_comparison_v2(benchmark_comparisons, benchmark_advice) -> str:
    """Format benchmark comparison results."""
    
    if not benchmark_comparisons:
        return "*No benchmark comparison available*"
    
    lines = []
    lines.append("## 📊 Benchmark Comparison")
    lines.append("")
    
    # Recommended benchmark
    if benchmark_advice and benchmark_advice.recommended_benchmark_name:
        lines.append(f"### 🏆 Recommended: {benchmark_advice.recommended_benchmark_name}")
        if benchmark_advice.reasoning:
            lines.append(f"*{benchmark_advice.reasoning[:200]}*")
        lines.append("")
    
    # Comparison table
    lines.append("### 📈 Similarity Ranking")
    lines.append("")
    lines.append("| Rank | Design System | Match | Type Ratio | Base | Grid |")
    lines.append("|------|---------------|-------|------------|------|------|")
    
    medals = ["🥇", "🥈", "🥉"]
    for i, c in enumerate(benchmark_comparisons[:5]):
        medal = medals[i] if i < 3 else str(i+1)
        b = c.benchmark
        lines.append(
            f"| {medal} | {b.icon} {b.short_name} | {c.overall_match_pct:.0f}% | "
            f"{b.typography.get('scale_ratio', '?')} | {b.typography.get('base_size', '?')}px | "
            f"{b.spacing.get('base', '?')}px |"
        )
    
    lines.append("")
    
    # Alignment changes needed
    if benchmark_advice and benchmark_advice.alignment_changes:
        lines.append("### 🔧 Changes to Align")
        for change in benchmark_advice.alignment_changes[:3]:
            lines.append(f"- **{change.get('change', '?')}**: {change.get('from', '?')} → {change.get('to', '?')} (effort: {change.get('effort', '?')})")
    
    return "\n".join(lines)


def format_scores_dashboard_v2(rule_results, final_synthesis, best_practices) -> str:
    """Format scores dashboard HTML."""
    
    overall = final_synthesis.scores.get('overall', rule_results.consistency_score)
    accessibility = final_synthesis.scores.get('accessibility', 100 - (rule_results.aa_failures * 5))
    consistency = final_synthesis.scores.get('consistency', rule_results.consistency_score)
    organization = final_synthesis.scores.get('organization', 50)
    
    def score_color(score):
        if score >= 80:
            return "#10b981"  # Green
        elif score >= 60:
            return "#f59e0b"  # Yellow
        else:
            return "#ef4444"  # Red
    
    html = f"""
    <style>
        .scores-grid {{ display: grid; grid-template-columns: repeat(4, 1fr); gap: 16px; margin: 20px 0; }}
        .score-card {{ border-radius: 12px; padding: 20px; text-align: center; }}
        .score-card-secondary {{ background: #f8fafc; border: 1px solid #e2e8f0; }}
        .score-card .score-label {{ font-size: 12px; color: #64748b; margin-top: 4px; }}
        .dark .score-card-secondary {{ background: #1e293b !important; border-color: #475569 !important; }}
        .dark .score-card .score-label {{ color: #94a3b8 !important; }}
    </style>
    <div class="scores-grid">
        <div class="score-card" style="background: linear-gradient(135deg, {score_color(overall)}22 0%, {score_color(overall)}11 100%);
                    border: 2px solid {score_color(overall)};">
            <div style="font-size: 32px; font-weight: 700; color: {score_color(overall)};">{overall}</div>
            <div class="score-label">OVERALL</div>
        </div>
        <div class="score-card score-card-secondary">
            <div style="font-size: 24px; font-weight: 600; color: {score_color(accessibility)};">{accessibility}</div>
            <div class="score-label">Accessibility</div>
        </div>
        <div class="score-card score-card-secondary">
            <div style="font-size: 24px; font-weight: 600; color: {score_color(consistency)};">{consistency}</div>
            <div class="score-label">Consistency</div>
        </div>
        <div class="score-card score-card-secondary">
            <div style="font-size: 24px; font-weight: 600; color: {score_color(organization)};">{organization}</div>
            <div class="score-label">Organization</div>
        </div>
    </div>
    """
    
    return html


def format_priority_actions_v2(rule_results, final_synthesis, best_practices) -> str:
    """Format priority actions HTML."""
    
    actions = final_synthesis.top_3_actions if final_synthesis.top_3_actions else []
    
    # If no synthesis actions, build from rule engine
    if not actions and best_practices and best_practices.priority_fixes:
        actions = best_practices.priority_fixes
    
    if not actions:
        # Default actions from rule engine
        actions = []
        if rule_results.aa_failures > 0:
            actions.append({
                "action": f"Fix {rule_results.aa_failures} colors failing AA compliance",
                "impact": "high",
                "effort": "30 min",
            })
        if not rule_results.typography.is_consistent:
            actions.append({
                "action": f"Align type scale to {rule_results.typography.recommendation} ({rule_results.typography.recommendation_name})",
                "impact": "medium", 
                "effort": "1 hour",
            })
        if rule_results.color_stats.unique_count > 30:
            actions.append({
                "action": f"Consolidate {rule_results.color_stats.unique_count} colors to ~15 semantic colors",
                "impact": "medium",
                "effort": "2 hours",
            })
    
    html_items = []
    for i, action in enumerate(actions[:3]):
        impact = action.get('impact', 'medium')
        border_color = "#ef4444" if impact == "high" else "#f59e0b" if impact == "medium" else "#10b981"
        impact_bg = "#fee2e2" if impact == "high" else "#fef3c7" if impact == "medium" else "#dcfce7"
        impact_text = "#991b1b" if impact == "high" else "#92400e" if impact == "medium" else "#166534"
        icon = "🔴" if impact == "high" else "🟡" if impact == "medium" else "🟢"
        
        html_items.append(f"""
        <div class="priority-action-card" style="border-left: 4px solid {border_color};">
            <div style="display: flex; justify-content: space-between; align-items: flex-start;">
                <div>
                    <div class="priority-action-title">
                        {icon} {action.get('action', 'N/A')}
                    </div>
                    <div class="priority-action-detail">
                        {action.get('details', '')}
                    </div>
                </div>
                <div style="display: flex; gap: 8px;">
                    <span style="background: {impact_bg}; color: {impact_text}; padding: 4px 8px;
                                 border-radius: 12px; font-size: 11px; font-weight: 600;">
                        {impact.upper()}
                    </span>
                    <span class="priority-effort-badge">
                        {action.get('effort', '?')}
                    </span>
                </div>
            </div>
        </div>
        """)
    
    return f"""
    <style>
        .priority-actions-wrap {{ margin: 20px 0; }}
        .priority-actions-wrap h3 {{ margin-bottom: 16px; color: #1e293b; }}
        .priority-action-card {{ background: white; border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; margin-bottom: 12px; }}
        .priority-action-title {{ font-weight: 600; color: #1e293b; margin-bottom: 4px; }}
        .priority-action-detail {{ font-size: 13px; color: #64748b; }}
        .priority-effort-badge {{ background: #f1f5f9; color: #475569; padding: 4px 8px; border-radius: 12px; font-size: 11px; }}
        .dark .priority-actions-wrap h3 {{ color: #f1f5f9 !important; }}
        .dark .priority-action-card {{ background: #1e293b !important; border-color: #475569 !important; }}
        .dark .priority-action-title {{ color: #f1f5f9 !important; }}
        .dark .priority-action-detail {{ color: #94a3b8 !important; }}
        .dark .priority-effort-badge {{ background: #334155 !important; color: #cbd5e1 !important; }}
    </style>
    <div class="priority-actions-wrap">
        <h3>🎯 Priority Actions</h3>
        {''.join(html_items)}
    </div>
    """


def format_color_recommendations_table_v2(rule_results, brand_result, final_synthesis) -> list:
    """Format color recommendations as table data."""
    
    rows = []
    
    # Add AA failures with fixes
    for a in rule_results.accessibility:
        if not a.passes_aa_normal and a.suggested_fix:
            role = "brand.primary" if brand_result and brand_result.brand_primary.get("color") == a.hex_color else a.name
            rows.append([
                True,  # Accept checkbox
                role,
                a.hex_color,
                f"Fails AA ({a.contrast_on_white:.1f}:1)",
                a.suggested_fix,
                f"{a.suggested_fix_contrast:.1f}:1",
            ])
    
    # Add recommendations from synthesis
    if final_synthesis and final_synthesis.color_recommendations:
        for rec in final_synthesis.color_recommendations:
            if rec.get("current") != rec.get("suggested"):
                # Check if not already in rows
                if not any(r[2] == rec.get("current") for r in rows):
                    rows.append([
                        rec.get("accept", True),
                        rec.get("role", "unknown"),
                        rec.get("current", ""),
                        rec.get("reason", ""),
                        rec.get("suggested", ""),
                        "",
                    ])
    
    return rows


def build_analysis_status(final_recs: dict, cost_tracking: dict, errors: list) -> str:
    """Build status markdown from analysis results."""
    
    lines = ["## 🧠 Multi-Agent Analysis Complete!"]
    lines.append("")
    
    # Cost summary
    if cost_tracking:
        total_cost = cost_tracking.get("total_cost", 0)
        lines.append(f"### 💰 Cost Summary")
        lines.append(f"**Total estimated cost:** ${total_cost:.4f}")
        lines.append(f"*(Free tier: $0.10/mo | Pro: $2.00/mo)*")
        lines.append("")
    
    # Final recommendations
    if final_recs and "final_recommendations" in final_recs:
        recs = final_recs["final_recommendations"]
        lines.append("### 📋 Recommendations")
        
        if recs.get("type_scale"):
            lines.append(f"**Type Scale:** {recs['type_scale']}")
            if recs.get("type_scale_rationale"):
                lines.append(f"  *{recs['type_scale_rationale'][:100]}*")
        
        if recs.get("spacing_base"):
            lines.append(f"**Spacing:** {recs['spacing_base']}")
        
        lines.append("")
    
    # Summary
    if final_recs.get("summary"):
        lines.append("### 📝 Summary")
        lines.append(final_recs["summary"])
        lines.append("")
    
    # Confidence
    if final_recs.get("overall_confidence"):
        lines.append(f"**Confidence:** {final_recs['overall_confidence']}%")
    
    # Errors
    if errors:
        lines.append("")
        lines.append("### ⚠️ Warnings")
        for err in errors[:3]:
            lines.append(f"- {err[:100]}")
    
    return "\n".join(lines)


def format_multi_agent_comparison(llm1: dict, llm2: dict, final: dict) -> str:
    """Format comparison from multi-agent analysis."""
    
    lines = ["### 📊 Multi-Agent Analysis Comparison"]
    lines.append("")
    
    # Agreements
    if final.get("agreements"):
        lines.append("#### ✅ Agreements (High Confidence)")
        for a in final["agreements"][:5]:
            topic = a.get("topic", "?")
            finding = a.get("finding", "?")[:80]
            lines.append(f"- **{topic}**: {finding}")
        lines.append("")
    
    # Disagreements and resolutions
    if final.get("disagreements"):
        lines.append("#### 🔄 Resolved Disagreements")
        for d in final["disagreements"][:3]:
            topic = d.get("topic", "?")
            resolution = d.get("resolution", "?")[:100]
            lines.append(f"- **{topic}**: {resolution}")
        lines.append("")
    
    # Score comparison
    lines.append("#### 📈 Score Comparison")
    lines.append("")
    lines.append("| Category | LLM 1 (Qwen) | LLM 2 (Llama) |")
    lines.append("|----------|--------------|---------------|")
    
    categories = ["typography", "colors", "accessibility", "spacing"]
    for cat in categories:
        llm1_score = llm1.get(cat, {}).get("score", "?") if isinstance(llm1.get(cat), dict) else "?"
        llm2_score = llm2.get(cat, {}).get("score", "?") if isinstance(llm2.get(cat), dict) else "?"
        lines.append(f"| {cat.title()} | {llm1_score}/10 | {llm2_score}/10 |")
    
    return "\n".join(lines)


def format_spacing_comparison_from_rules(rule_calculations: dict) -> list:
    """Format spacing comparison from rule engine."""
    if not rule_calculations:
        return []
    
    spacing_options = rule_calculations.get("spacing_options", {})
    
    data = []
    for i in range(10):
        current = f"{(i+1) * 4}px" if i < 5 else f"{(i+1) * 8}px"
        grid_8 = spacing_options.get("8px", [])
        grid_4 = spacing_options.get("4px", [])
        
        val_8 = f"{grid_8[i+1]}px" if i+1 < len(grid_8) else "—"
        val_4 = f"{grid_4[i+1]}px" if i+1 < len(grid_4) else "—"
        
        data.append([current, val_8, val_4])
    
    return data


def format_color_ramps_from_rules(rule_calculations: dict) -> str:
    """Format color ramps from rule engine."""
    if not rule_calculations:
        return "*No color ramps generated*"
    
    ramps = rule_calculations.get("color_ramps", {})
    if not ramps:
        return "*No color ramps generated*"
    
    lines = ["### 🌈 Generated Color Ramps"]
    lines.append("")
    
    for name, ramp in list(ramps.items())[:6]:
        lines.append(f"**{name}**")
        if isinstance(ramp, list) and len(ramp) >= 10:
            lines.append("| 50 | 100 | 200 | 300 | 400 | 500 | 600 | 700 | 800 | 900 |")
            lines.append("|---|---|---|---|---|---|---|---|---|---|")
            row = "| " + " | ".join([f"`{ramp[i]}`" for i in range(10)]) + " |"
            lines.append(row)
        lines.append("")
    
    return "\n".join(lines)


def get_detected_fonts() -> dict:
    """Get detected font information."""
    if not state.desktop_normalized:
        return {"primary": "Unknown", "weights": []}
    
    fonts = {}
    weights = set()
    
    for t in state.desktop_normalized.typography.values():
        family = t.font_family
        weight = t.font_weight
        
        if family not in fonts:
            fonts[family] = 0
        fonts[family] += t.frequency
        
        if weight:
            try:
                weights.add(int(weight))
            except (ValueError, TypeError):
                pass
    
    primary = max(fonts.items(), key=lambda x: x[1])[0] if fonts else "Unknown"
    
    return {
        "primary": primary,
        "weights": sorted(weights) if weights else [400],
        "all_fonts": fonts,
    }


def get_base_font_size() -> int:
    """Detect base font size from typography."""
    if not state.desktop_normalized:
        return 16
    
    # Find most common size in body range (14-18px)
    sizes = {}
    for t in state.desktop_normalized.typography.values():
        size_str = str(t.font_size).replace('px', '').replace('rem', '').replace('em', '')
        try:
            size = float(size_str)
            if 14 <= size <= 18:
                sizes[size] = sizes.get(size, 0) + t.frequency
        except (ValueError, TypeError):
            pass
    
    if sizes:
        return int(max(sizes.items(), key=lambda x: x[1])[0])
    return 16


def format_brand_comparison(recommendations) -> str:
    """Format brand comparison as markdown table."""
    if not recommendations.brand_analysis:
        return "*Brand analysis not available*"
    
    lines = [
        "### 📊 Design System Comparison (5 Top Brands)",
        "",
        "| Brand | Type Ratio | Base Size | Spacing | Notes |",
        "|-------|------------|-----------|---------|-------|",
    ]
    
    for brand in recommendations.brand_analysis[:5]:
        name = brand.get("brand", "Unknown")
        ratio = brand.get("ratio", "?")
        base = brand.get("base", "?")
        spacing = brand.get("spacing", "?")
        notes = brand.get("notes", "")[:50] + ("..." if len(brand.get("notes", "")) > 50 else "")
        lines.append(f"| {name} | {ratio} | {base}px | {spacing} | {notes} |")
    
    return "\n".join(lines)


def format_font_families_display(fonts: dict) -> str:
    """Format detected font families for display."""
    lines = []
    
    primary = fonts.get("primary", "Unknown")
    weights = fonts.get("weights", [400])
    all_fonts = fonts.get("all_fonts", {})
    
    lines.append(f"### Primary Font: **{primary}**")
    lines.append("")
    lines.append(f"**Weights detected:** {', '.join(map(str, weights))}")
    lines.append("")
    
    if all_fonts and len(all_fonts) > 1:
        lines.append("### All Fonts Detected")
        lines.append("")
        lines.append("| Font Family | Usage Count |")
        lines.append("|-------------|-------------|")
        
        sorted_fonts = sorted(all_fonts.items(), key=lambda x: -x[1])
        for font, count in sorted_fonts[:5]:
            lines.append(f"| {font} | {count:,} |")
    
    lines.append("")
    lines.append("*Note: This analysis focuses on English typography only.*")
    
    return "\n".join(lines)


def format_llm_color_recommendations_html(final_recs: dict, semantic_analysis: dict) -> str:
    """Generate HTML showing LLM color recommendations with before/after comparison."""
    
    if not final_recs:
        return '''
        <div class="placeholder-msg" style="text-align: center;">
            <p>No LLM recommendations available yet. Run analysis first.</p>
        </div>
        '''
    
    color_recs = final_recs.get("color_recommendations", {})
    aa_fixes = final_recs.get("accessibility_fixes", [])
    
    if not color_recs and not aa_fixes:
        return '''
        <div class="llm-no-recs" style="padding: 20px; border-radius: 8px; border: 1px solid #28a745; background: #d4edda;">
            <p style="margin: 0; color: #155724;">✅ No color changes recommended. Your colors look good!</p>
        </div>
        <style>
            .dark .llm-no-recs { background: #14532d !important; border-color: #22c55e !important; }
            .dark .llm-no-recs p { color: #86efac !important; }
        </style>
        '''
    
    # Build recommendations HTML
    recs_html = ""
    
    # Process color recommendations
    for role, rec in color_recs.items():
        if not isinstance(rec, dict):
            continue
        if role in ["generate_ramps_for", "changes_made"]:
            continue
            
        current = rec.get("current", "?")
        suggested = rec.get("suggested", current)
        action = rec.get("action", "keep")
        rationale = rec.get("rationale", "")
        
        if action == "keep" or suggested == current:
            # No change needed
            recs_html += f'''
            <div class="llm-rec-row keep">
                <div class="rec-color-box" style="background: {current};"></div>
                <div class="rec-details">
                    <span class="rec-role">{role}</span>
                    <span class="rec-current">{current}</span>
                    <span class="rec-action keep">✓ Keep</span>
                </div>
            </div>
            '''
        else:
            # Change suggested
            recs_html += f'''
            <div class="llm-rec-row change">
                <div class="rec-comparison">
                    <div class="rec-before">
                        <div class="rec-color-box" style="background: {current};"></div>
                        <span class="rec-label">Before</span>
                        <span class="rec-hex">{current}</span>
                    </div>
                    <span class="rec-arrow">→</span>
                    <div class="rec-after">
                        <div class="rec-color-box" style="background: {suggested};"></div>
                        <span class="rec-label">After</span>
                        <span class="rec-hex">{suggested}</span>
                    </div>
                </div>
                <div class="rec-details">
                    <span class="rec-role">{role}</span>
                    <span class="rec-rationale">{rationale[:80]}...</span>
                </div>
            </div>
            '''
    
    # Process accessibility fixes
    for fix in aa_fixes:
        if not isinstance(fix, dict):
            continue
        
        color = fix.get("color", "?")
        role = fix.get("role", "unknown")
        issue = fix.get("issue", "contrast issue")
        fix_color = fix.get("fix", color)
        current_contrast = fix.get("current_contrast", "?")
        fixed_contrast = fix.get("fixed_contrast", "?")
        
        if fix_color and fix_color != color:
            recs_html += f'''
            <div class="llm-rec-row aa-fix">
                <div class="rec-comparison">
                    <div class="rec-before">
                        <div class="rec-color-box" style="background: {color};"></div>
                        <span class="rec-label">⚠️ {current_contrast}:1</span>
                        <span class="rec-hex">{color}</span>
                    </div>
                    <span class="rec-arrow">→</span>
                    <div class="rec-after">
                        <div class="rec-color-box" style="background: {fix_color};"></div>
                        <span class="rec-label">✓ {fixed_contrast}:1</span>
                        <span class="rec-hex">{fix_color}</span>
                    </div>
                </div>
                <div class="rec-details">
                    <span class="rec-role">{role}</span>
                    <span class="rec-issue">🔴 {issue}</span>
                </div>
            </div>
            '''
    
    if not recs_html:
        return '''
        <div class="llm-no-recs" style="padding: 20px; border-radius: 8px; border: 1px solid #28a745; background: #d4edda;">
            <p style="margin: 0; color: #155724;">✅ No color changes recommended. Your colors look good!</p>
        </div>
        <style>
            .dark .llm-no-recs { background: #14532d !important; border-color: #22c55e !important; }
            .dark .llm-no-recs p { color: #86efac !important; }
        </style>
        '''
    
    html = f'''
    <style>
        .llm-recs-container {{
            font-family: system-ui, -apple-system, sans-serif;
            background: #f5f5f5 !important;
            border-radius: 12px;
            padding: 16px;
        }}
        
        .llm-rec-row {{
            display: flex;
            align-items: center;
            padding: 12px;
            margin-bottom: 12px;
            border-radius: 8px;
            background: #ffffff !important;
            border: 1px solid #e0e0e0 !important;
        }}
        
        .llm-rec-row.change {{
            border-left: 4px solid #f59e0b !important;
        }}
        
        .llm-rec-row.aa-fix {{
            border-left: 4px solid #dc2626 !important;
            background: #fef2f2 !important;
        }}
        
        .llm-rec-row.keep {{
            border-left: 4px solid #22c55e !important;
            background: #f0fdf4 !important;
        }}
        
        .rec-comparison {{
            display: flex;
            align-items: center;
            gap: 12px;
            margin-right: 20px;
        }}
        
        .rec-before, .rec-after {{
            display: flex;
            flex-direction: column;
            align-items: center;
            gap: 4px;
        }}
        
        .rec-color-box {{
            width: 48px;
            height: 48px;
            border-radius: 8px;
            border: 2px solid rgba(0,0,0,0.15) !important;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
        }}
        
        .rec-label {{
            font-size: 11px;
            font-weight: 600;
            color: #666 !important;
        }}
        
        .rec-hex {{
            font-family: 'SF Mono', Monaco, monospace;
            font-size: 11px;
            color: #333 !important;
        }}
        
        .rec-arrow {{
            font-size: 20px;
            color: #666 !important;
            font-weight: bold;
        }}
        
        .rec-details {{
            flex: 1;
            display: flex;
            flex-direction: column;
            gap: 4px;
        }}
        
        .rec-role {{
            font-weight: 700;
            font-size: 14px;
            color: #1a1a1a !important;
        }}
        
        .rec-action {{
            font-size: 12px;
            padding: 2px 8px;
            border-radius: 4px;
        }}
        
        .rec-action.keep {{
            background: #dcfce7 !important;
            color: #166534 !important;
        }}
        
        .rec-rationale {{
            font-size: 12px;
            color: #666 !important;
        }}
        
        .rec-issue {{
            font-size: 12px;
            color: #991b1b !important;
            font-weight: 500;
        }}

        /* Dark mode */
        .dark .llm-recs-container {{ background: #0f172a !important; }}
        .dark .llm-rec-row {{ background: #1e293b !important; border-color: #475569 !important; }}
        .dark .llm-rec-row.aa-fix {{ background: #450a0a !important; }}
        .dark .llm-rec-row.keep {{ background: #14532d !important; }}
        .dark .rec-label {{ color: #94a3b8 !important; }}
        .dark .rec-hex {{ color: #cbd5e1 !important; }}
        .dark .rec-arrow {{ color: #94a3b8 !important; }}
        .dark .rec-role {{ color: #f1f5f9 !important; }}
        .dark .rec-rationale {{ color: #94a3b8 !important; }}
        .dark .rec-issue {{ color: #fca5a5 !important; }}
        .dark .rec-action.keep {{ background: #14532d !important; color: #86efac !important; }}
    </style>
    
    <div class="llm-recs-container">
        {recs_html}
    </div>
    '''
    
    return html


def format_llm_color_recommendations_table(final_recs: dict, semantic_analysis: dict) -> list:
    """Generate table data for LLM color recommendations with accept/reject checkboxes."""
    
    rows = []
    
    if not final_recs:
        return rows
    
    color_recs = final_recs.get("color_recommendations", {})
    aa_fixes = final_recs.get("accessibility_fixes", [])
    
    # Process color recommendations
    for role, rec in color_recs.items():
        if not isinstance(rec, dict):
            continue
        if role in ["generate_ramps_for", "changes_made"]:
            continue
            
        current = rec.get("current", "?")
        suggested = rec.get("suggested", current)
        action = rec.get("action", "keep")
        rationale = rec.get("rationale", "")[:50]
        
        if action != "keep" and suggested != current:
            # Calculate contrast improvement
            try:
                from core.color_utils import get_contrast_with_white
                old_contrast = get_contrast_with_white(current)
                new_contrast = get_contrast_with_white(suggested)
                contrast_str = f"{old_contrast:.1f} → {new_contrast:.1f}"
            except (ValueError, TypeError, ZeroDivisionError):
                contrast_str = "?"
            
            rows.append([
                True,  # Accept checkbox (default True)
                role,
                current,
                rationale or action,
                suggested,
                contrast_str,
            ])
    
    # Process accessibility fixes
    for fix in aa_fixes:
        if not isinstance(fix, dict):
            continue
        
        color = fix.get("color", "?")
        role = fix.get("role", "unknown")
        issue = fix.get("issue", "contrast")[:40]
        fix_color = fix.get("fix", color)
        current_contrast = fix.get("current_contrast", "?")
        fixed_contrast = fix.get("fixed_contrast", "?")
        
        if fix_color and fix_color != color:
            rows.append([
                True,  # Accept checkbox
                f"{role} (AA fix)",
                color,
                issue,
                fix_color,
                f"{current_contrast}:1 → {fixed_contrast}:1",
            ])
    
    return rows


def format_typography_comparison_viewport(normalized_tokens, base_size: int, viewport: str) -> list:
    """Format typography comparison for a specific viewport."""
    if not normalized_tokens:
        return []
    
    # Get current typography sorted by size
    current_typo = list(normalized_tokens.typography.values())
    
    # Parse and sort sizes
    def parse_size(t):
        size_str = str(t.font_size).replace('px', '').replace('rem', '').replace('em', '')
        try:
            return float(size_str)
        except (ValueError, TypeError):
            return 16
    
    current_typo.sort(key=lambda t: -parse_size(t))
    sizes = [parse_size(t) for t in current_typo]
    
    # Use detected base or default
    base = base_size if base_size else 16
    
    # Scale factors for mobile (typically 0.85-0.9 of desktop)
    mobile_factor = 0.875 if viewport == "mobile" else 1.0
    
    # Token names (13 levels)
    token_names = [
        "display.2xl", "display.xl", "display.lg", "display.md",
        "heading.xl", "heading.lg", "heading.md", "heading.sm",
        "body.lg", "body.md", "body.sm",
        "caption", "overline"
    ]
    
    # Generate scales - use base size and round to sensible values
    def round_to_even(val):
        """Round to even numbers for cleaner type scales."""
        return int(round(val / 2) * 2)
    
    scales = {
        "1.2": [round_to_even(base * mobile_factor * (1.2 ** (8-i))) for i in range(13)],
        "1.25": [round_to_even(base * mobile_factor * (1.25 ** (8-i))) for i in range(13)],
        "1.333": [round_to_even(base * mobile_factor * (1.333 ** (8-i))) for i in range(13)],
    }
    
    # Build comparison table
    data = []
    for i, name in enumerate(token_names):
        current = f"{int(sizes[i])}px" if i < len(sizes) else "—"
        s12 = f"{scales['1.2'][i]}px"
        s125 = f"{scales['1.25'][i]}px"
        s133 = f"{scales['1.333'][i]}px"
        keep = current
        data.append([name, current, s12, s125, s133, keep])
    
    return data


def format_base_colors() -> str:
    """Format base colors (detected) separately from ramps."""
    if not state.desktop_normalized:
        return "*No colors detected*"
    
    colors = list(state.desktop_normalized.colors.values())
    colors.sort(key=lambda c: -c.frequency)
    
    lines = [
        "### 🎨 Base Colors (Detected)",
        "",
        "These are the primary colors extracted from your website:",
        "",
        "| Color | Hex | Role | Frequency | Contrast |",
        "|-------|-----|------|-----------|----------|",
    ]
    
    for color in colors[:10]:
        hex_val = color.value
        role = "Primary" if color.suggested_name and "primary" in color.suggested_name.lower() else \
               "Text" if color.suggested_name and "text" in color.suggested_name.lower() else \
               "Background" if color.suggested_name and "background" in color.suggested_name.lower() else \
               "Border" if color.suggested_name and "border" in color.suggested_name.lower() else \
               "Accent"
        freq = f"{color.frequency:,}"
        contrast = f"{color.contrast_white:.1f}:1" if color.contrast_white else "—"
        
        # Create a simple color indicator
        lines.append(f"| 🟦 | `{hex_val}` | {role} | {freq} | {contrast} |")
    
    return "\n".join(lines)


def format_color_ramps_visual(recommendations) -> str:
    """Format color ramps with visual display showing all shades."""
    if not state.desktop_normalized:
        return "*No colors to display*"
    
    colors = list(state.desktop_normalized.colors.values())
    colors.sort(key=lambda c: -c.frequency)
    
    lines = [
        "### 🌈 Generated Color Ramps",
        "",
        "Full ramp (50-950) generated for each base color:",
        "",
    ]
    
    from core.color_utils import generate_color_ramp
    
    for color in colors[:6]:  # Top 6 colors
        hex_val = color.value
        role = color.suggested_name.split('.')[1] if color.suggested_name and '.' in color.suggested_name else "color"
        
        # Generate ramp
        try:
            ramp = generate_color_ramp(hex_val)
            
            lines.append(f"**{role.upper()}** (base: `{hex_val}`)")
            lines.append("")
            lines.append("| 50 | 100 | 200 | 300 | 400 | 500 | 600 | 700 | 800 | 900 |")
            lines.append("|---|---|---|---|---|---|---|---|---|---|")
            
            # Create row with hex values
            row = "|"
            for i in range(10):
                if i < len(ramp):
                    row += f" `{ramp[i]}` |"
                else:
                    row += " — |"
            lines.append(row)
            lines.append("")
            
        except Exception as e:
            lines.append(f"**{role}** (`{hex_val}`) — Could not generate ramp: {str(e)}")
            lines.append("")
    
    return "\n".join(lines)


def format_radius_with_tokens() -> str:
    """Format radius with token name suggestions."""
    if not state.desktop_normalized or not state.desktop_normalized.radius:
        return "*No border radius values detected.*"
    
    radii = list(state.desktop_normalized.radius.values())
    
    lines = [
        "### 🔘 Border Radius Tokens",
        "",
        "| Detected | Suggested Token | Usage |",
        "|----------|-----------------|-------|",
    ]
    
    # Sort by pixel value
    def parse_radius(r):
        val = str(r.value).replace('px', '').replace('%', '')
        try:
            return float(val)
        except (ValueError, TypeError):
            return 999
    
    radii.sort(key=lambda r: parse_radius(r))
    
    token_map = {
        (0, 2): ("radius.none", "Sharp corners"),
        (2, 4): ("radius.xs", "Subtle rounding"),
        (4, 6): ("radius.sm", "Small elements"),
        (6, 10): ("radius.md", "Buttons, cards"),
        (10, 16): ("radius.lg", "Modals, panels"),
        (16, 32): ("radius.xl", "Large containers"),
        (32, 100): ("radius.2xl", "Pill shapes"),
    }
    
    for r in radii[:8]:
        val = str(r.value)
        px = parse_radius(r)
        
        if "%" in str(r.value) or px >= 50:
            token = "radius.full"
            usage = "Circles, avatars"
        else:
            token = "radius.md"
            usage = "General use"
            for (low, high), (t, u) in token_map.items():
                if low <= px < high:
                    token = t
                    usage = u
                    break
        
        lines.append(f"| {val} | `{token}` | {usage} |")
    
    return "\n".join(lines)


def format_shadows_with_tokens() -> str:
    """Format shadows with token name suggestions."""
    if not state.desktop_normalized or not state.desktop_normalized.shadows:
        return "*No shadow values detected.*"
    
    shadows = list(state.desktop_normalized.shadows.values())
    
    lines = [
        "### 🌫️ Shadow Tokens",
        "",
        "| Detected Value | Suggested Token | Use Case |",
        "|----------------|-----------------|----------|",
    ]
    
    shadow_sizes = ["shadow.xs", "shadow.sm", "shadow.md", "shadow.lg", "shadow.xl", "shadow.2xl"]
    
    for i, s in enumerate(shadows[:6]):
        val = str(s.value)[:40] + ("..." if len(str(s.value)) > 40 else "")
        token = shadow_sizes[i] if i < len(shadow_sizes) else f"shadow.custom-{i}"
        
        # Guess use case based on index
        use_cases = ["Subtle elevation", "Cards, dropdowns", "Modals, dialogs", "Popovers", "Floating elements", "Dramatic effect"]
        use = use_cases[i] if i < len(use_cases) else "Custom"
        
        lines.append(f"| `{val}` | `{token}` | {use} |")
    
    return "\n".join(lines)


def format_spacing_comparison(recommendations) -> list:
    """Format spacing comparison table."""
    if not state.desktop_normalized:
        return []
    
    # Get current spacing
    current_spacing = list(state.desktop_normalized.spacing.values())
    current_spacing.sort(key=lambda s: s.value_px)
    
    data = []
    for s in current_spacing[:10]:
        current = f"{s.value_px}px"
        grid_8 = f"{snap_to_grid(s.value_px, 8)}px"
        grid_4 = f"{snap_to_grid(s.value_px, 4)}px"
        
        # Mark if value fits
        if s.value_px == snap_to_grid(s.value_px, 8):
            grid_8 += " ✓"
        if s.value_px == snap_to_grid(s.value_px, 4):
            grid_4 += " ✓"
        
        data.append([current, grid_8, grid_4])
    
    return data


def snap_to_grid(value: float, base: int) -> int:
    """Snap value to grid."""
    return round(value / base) * base


def reset_to_original():
    """Reset all upgrade selections to defaults."""
    state.selected_upgrades = {}
    state.log("")
    state.log("↩️ Reset all upgrade selections to original values.")

    return (
        "Scale 1.25 (Major Third) ⭐",     # type_scale_radio
        "8px Base Grid ⭐",                  # spacing_radio
        True,                                 # color_ramps_checkbox
        "## ↩️ Reset Complete\n\nAll selections reverted to defaults. Review and apply again when ready.",  # apply_status
        state.get_logs(),                     # stage2_log
    )


def apply_selected_upgrades(type_choice: str, spacing_choice: str, apply_ramps: bool, color_recs_table: list = None):
    """Apply selected upgrade options including LLM color recommendations."""
    if not state.upgrade_recommendations:
        return "## ❌ Run Analysis First\n\nPlease run the **v2 Analysis** before applying upgrades.", state.get_logs()

    state.log("")
    state.log("═" * 50)
    state.log("✨ APPLYING SELECTED UPGRADES")
    state.log("═" * 50)

    # Store selections
    state.selected_upgrades = {
        "type_scale": type_choice,
        "spacing": spacing_choice,
        "color_ramps": apply_ramps,
    }

    state.log(f"   📐 Type Scale: {type_choice}")
    state.log(f"   📏 Spacing: {spacing_choice}")
    state.log(f"   🌈 Color Ramps: {'Yes' if apply_ramps else 'No'}")

    # Process accepted color recommendations
    accepted_color_changes = []
    rejected_count = 0
    if color_recs_table:
        state.log("")
        state.log("   🎨 LLM Color Recommendations:")
        for row in color_recs_table:
            if len(row) >= 5:
                accept = row[0]  # Boolean checkbox
                role = row[1]    # Role name
                current = row[2] # Current color
                issue = row[3]   # Issue description
                suggested = row[4]  # Suggested color

                if accept and suggested and current != suggested:
                    accepted_color_changes.append({
                        "role": role,
                        "from": current,
                        "to": suggested,
                        "reason": issue,
                    })
                    state.log(f"   ├─ ✅ ACCEPTED: {role}")
                    state.log(f"   │  └─ {current} → {suggested}")
                elif not accept:
                    rejected_count += 1
                    state.log(f"   ├─ ❌ REJECTED: {role} (keeping {current})")

    # Store accepted changes
    state.selected_upgrades["color_changes"] = accepted_color_changes

    state.log("")
    if accepted_color_changes:
        state.log(f"   📊 {len(accepted_color_changes)} color change(s) will be applied to export")
    if rejected_count:
        state.log(f"   📊 {rejected_count} color change(s) rejected (keeping original)")

    state.log("")
    state.log("✅ Upgrades applied! Proceed to Stage 3 for export.")
    state.log("═" * 50)

    # Build visible feedback summary
    summary_parts = []
    summary_parts.append(f"**Type Scale:** {type_choice}")
    summary_parts.append(f"**Spacing:** {spacing_choice}")
    summary_parts.append(f"**Color Ramps:** {'✅ Enabled' if apply_ramps else '❌ Disabled'}")
    if accepted_color_changes:
        summary_parts.append(f"**Color Changes:** {len(accepted_color_changes)} accepted")
    if rejected_count:
        summary_parts.append(f"**Rejected:** {rejected_count} kept as-is")

    status_md = f"""## ✅ Upgrades Applied Successfully!

{chr(10).join('- ' + p for p in summary_parts)}

👉 **Proceed to Stage 3** to export your upgraded tokens.
"""
    return status_md, state.get_logs()


# =============================================================================
# EXPORT HELPERS — Semantic Token Naming
# =============================================================================

def _get_radius_token_name(value_str, seen_names: dict = None) -> str:
    """Map radius px value to semantic token name (radius.sm, radius.md, etc.)."""
    val = str(value_str).replace('px', '').replace('%', '')
    try:
        px = float(val)
    except (ValueError, TypeError):
        return "radius.md"

    # Handle percentage values (e.g., "50%" for circular)
    if "%" in str(value_str):
        base_name = "radius.full"
    # "none" is ONLY for exactly 0px
    elif px == 0:
        base_name = "radius.none"
    elif px >= 9999:
        # Large values (like 9999px) are essentially "full"
        base_name = "radius.full"
    else:
        # Semantic naming based on pixel ranges (inclusive both ends for clarity)
        mapping = [
            (1, 1, "radius.xs"),      # 1px = xs
            (2, 3, "radius.sm"),      # 2-3px = sm
            (4, 7, "radius.md"),      # 4-7px = md
            (8, 11, "radius.lg"),     # 8-11px = lg
            (12, 19, "radius.xl"),    # 12-19px = xl
            (20, 31, "radius.2xl"),   # 20-31px = 2xl
            (32, 99, "radius.3xl"),   # 32-99px = 3xl
        ]
        base_name = "radius.md"
        for low, high, name in mapping:
            if low <= px <= high:
                base_name = name
                break

    # Handle duplicates: if two radii map to same name, append px value
    if seen_names is not None:
        if base_name in seen_names:
            return f"{base_name}.{int(px)}"
        seen_names[base_name] = True
    return base_name


def _get_shadow_blur(value_str: str) -> float:
    """Extract blur radius from shadow value for sorting."""
    import re
    # Shadow format: "Xpx Ypx BLURpx SPREADpx color"
    parts = re.findall(r'([\d.]+)px', str(value_str))
    if len(parts) >= 3:
        return float(parts[2])  # blur is 3rd px value
    elif len(parts) >= 1:
        return float(parts[0])
    return 0


def _parse_shadow_to_tokens_studio(value_str: str) -> dict:
    """Parse CSS shadow string to Figma Tokens Studio boxShadow format.

    Input: "rgba(0, 0, 0, 0.5) 0px 2px 4px 0px" or "0px 2px 4px 0px rgba(0,0,0,0.5)"
    Output: {"x": "0", "y": "2", "blur": "4", "spread": "0", "color": "rgba(0,0,0,0.5)", "type": "dropShadow"}
    """
    import re
    value_str = str(value_str).strip()

    # Extract color (rgba/rgb/hex)
    color_match = re.search(r'(rgba?\([^)]+\)|#[0-9a-fA-F]{3,8})', value_str)
    color = color_match.group(1) if color_match else "rgba(0,0,0,0.25)"

    # Extract px values
    px_values = re.findall(r'(-?[\d.]+)px', value_str)

    # Standard order: x y blur spread
    x = px_values[0] if len(px_values) > 0 else "0"
    y = px_values[1] if len(px_values) > 1 else "0"
    blur = px_values[2] if len(px_values) > 2 else "0"
    spread = px_values[3] if len(px_values) > 3 else "0"

    # Determine if inset
    shadow_type = "innerShadow" if "inset" in value_str.lower() else "dropShadow"

    return {
        "x": x,
        "y": y,
        "blur": blur,
        "spread": spread,
        "color": color,
        "type": shadow_type,
    }


# =============================================================================
# W3C DTCG FORMAT HELPERS
# =============================================================================

def _flat_key_to_nested(flat_key: str, value: dict, root: dict):
    """Convert 'color.brand.primary' into nested dict structure.

    Example: _flat_key_to_nested('color.brand.primary', token, {})
    Result: {'color': {'brand': {'primary': token}}}
    """
    parts = flat_key.split('.')
    current = root
    for part in parts[:-1]:
        if part not in current:
            current[part] = {}
        current = current[part]
    current[parts[-1]] = value


def _to_dtcg_token(value, token_type: str, description: str = None, source: str = None) -> dict:
    """Wrap value in W3C DTCG format with $value, $type, $description.

    Args:
        value: The token value
        token_type: W3C DTCG type (color, typography, dimension, shadow)
        description: Optional human-readable description
        source: Optional source indicator (extracted, recommended, semantic)
    """
    token = {"$type": token_type, "$value": value}
    if description:
        token["$description"] = description
    if source:
        token["$description"] = f"[{source}] {description or ''}"
    return token


def _shadow_to_dtcg(shadow_dict: dict) -> dict:
    """Convert our internal shadow format to W3C DTCG shadow spec.

    Input: {"x": "0", "y": "2", "blur": "4", "spread": "0", "color": "rgba(...)"}
    Output: {"color": "...", "offsetX": "0px", "offsetY": "2px", "blur": "4px", "spread": "0px"}
    """
    return {
        "color": shadow_dict.get("color", "rgba(0,0,0,0.25)"),
        "offsetX": str(shadow_dict.get("x", "0")) + "px",
        "offsetY": str(shadow_dict.get("y", "0")) + "px",
        "blur": str(shadow_dict.get("blur", "0")) + "px",
        "spread": str(shadow_dict.get("spread", "0")) + "px",
    }


def _get_semantic_color_overrides() -> dict:
    """Build color hex→semantic name map from semantic analysis + LLM results."""
    overrides = {}  # hex → semantic_name

    # From semantic analysis (rule-based)
    semantic = getattr(state, 'semantic_analysis', None)
    if isinstance(semantic, dict):
        role_map = {
            'brand': 'color.brand',
            'text': 'color.text',
            'background': 'color.bg',
            'border': 'color.border',
            'feedback': 'color.feedback',
        }
        for category, roles in semantic.items():
            prefix = role_map.get(category, f'color.{category}')
            if isinstance(roles, dict):
                for role_name, data in roles.items():
                    if isinstance(data, dict):
                        hex_val = data.get('hex', data.get('color', data.get('value', ''))).lower()
                        if hex_val and hex_val.startswith('#'):
                            overrides[hex_val] = f"{prefix}.{role_name}"
                    elif isinstance(data, list):
                        for item in data:
                            if isinstance(item, dict):
                                hex_val = item.get('hex', item.get('color', '')).lower()
                                if hex_val and hex_val.startswith('#'):
                                    overrides[hex_val] = f"{prefix}.{role_name}"
            elif isinstance(roles, list):
                for item in roles:
                    if isinstance(item, dict):
                        hex_val = item.get('hex', item.get('color', '')).lower()
                        name = item.get('name', category)
                        if hex_val and hex_val.startswith('#'):
                            overrides[hex_val] = f"{prefix}.{name}"

    # From AURORA's semantic_names (most detailed LLM naming)
    brand_result = getattr(state, 'brand_result', None)
    if brand_result:
        sem_names = getattr(brand_result, 'semantic_names', None)
        if isinstance(sem_names, dict):
            for hex_val, sem_name in sem_names.items():
                hex_clean = str(hex_val).strip().lower()
                if hex_clean.startswith('#'):
                    # Ensure color. prefix
                    name = sem_name if sem_name.startswith('color.') else f'color.{sem_name}'
                    overrides[hex_clean] = name

    # From NEXUS synthesis — brand_analysis and color_recommendations
    synthesis = getattr(state, 'final_synthesis', None)
    if synthesis:
        # brand_analysis (primary/secondary)
        brand = getattr(synthesis, 'brand_analysis', None) or (synthesis.get('brand_analysis') if isinstance(synthesis, dict) else None)
        if isinstance(brand, dict):
            if brand.get('primary'):
                overrides.setdefault(str(brand['primary']).lower(), 'color.brand.primary')
            if brand.get('secondary'):
                overrides.setdefault(str(brand['secondary']).lower(), 'color.brand.secondary')

        # color_recommendations (role-based naming from NEXUS)
        recs = getattr(synthesis, 'color_recommendations', None)
        if isinstance(recs, list):
            for rec in recs:
                if isinstance(rec, dict):
                    hex_val = str(rec.get('current', rec.get('suggested', ''))).strip().lower()
                    role = rec.get('role', '')
                    if hex_val.startswith('#') and role:
                        name = role if role.startswith('color.') else f'color.{role}'
                        overrides.setdefault(hex_val, name)

    return overrides


def _is_valid_hex_color(value: str) -> bool:
    """Validate that a string is a proper hex color (not CSS garbage)."""
    import re
    if not value or not isinstance(value, str):
        return False
    # Must be exactly #RGB, #RRGGBB, or #RRGGBBAA
    clean = value.strip().lower()
    return bool(re.match(r'^#([a-f0-9]{3}|[a-f0-9]{6}|[a-f0-9]{8})$', clean))


def _generate_color_name_from_hex(hex_val: str, used_names: set = None) -> str:
    """Generate a semantic color name based on the color's HSL characteristics.

    Returns names like: color.neutral.400, color.blue.500, color.red.300
    Uses standard design system naming conventions.
    """
    import colorsys

    used_names = used_names or set()

    # Parse hex to RGB
    hex_clean = hex_val.lstrip('#').lower()
    if len(hex_clean) == 3:
        hex_clean = ''.join([c*2 for c in hex_clean])

    try:
        r = int(hex_clean[0:2], 16) / 255
        g = int(hex_clean[2:4], 16) / 255
        b = int(hex_clean[4:6], 16) / 255
    except (ValueError, IndexError):
        return "color.other.base"

    # Convert to HSL
    h, l, s = colorsys.rgb_to_hls(r, g, b)
    hue = h * 360
    saturation = s
    lightness = l

    # Determine color family based on hue (for saturated colors)
    if saturation < 0.1:
        # Grayscale / neutral
        color_family = "neutral"
    else:
        # Map hue to color name
        if hue < 15 or hue >= 345:
            color_family = "red"
        elif hue < 45:
            color_family = "orange"
        elif hue < 75:
            color_family = "yellow"
        elif hue < 150:
            color_family = "green"
        elif hue < 195:
            color_family = "teal"
        elif hue < 255:
            color_family = "blue"
        elif hue < 285:
            color_family = "purple"
        elif hue < 345:
            color_family = "pink"
        else:
            color_family = "red"

    # Determine shade based on lightness (100-900 scale)
    if lightness >= 0.95:
        shade = "50"
    elif lightness >= 0.85:
        shade = "100"
    elif lightness >= 0.75:
        shade = "200"
    elif lightness >= 0.65:
        shade = "300"
    elif lightness >= 0.50:
        shade = "400"
    elif lightness >= 0.40:
        shade = "500"
    elif lightness >= 0.30:
        shade = "600"
    elif lightness >= 0.20:
        shade = "700"
    elif lightness >= 0.10:
        shade = "800"
    else:
        shade = "900"

    # Generate base name
    base_name = f"color.{color_family}.{shade}"

    # Handle conflicts by adding suffix
    final_name = base_name
    suffix = 1
    while final_name in used_names:
        suffix += 1
        final_name = f"{base_name}_{suffix}"

    return final_name


def _consolidate_colors(colors_dict: dict, overrides: dict, max_colors: int = 30) -> dict:
    """Consolidate colors: semantic first, then top by frequency, capped."""
    if not colors_dict:
        return {}

    result = {}
    remaining = []
    used_generated_names = set()  # Track generated names to avoid conflicts

    for name, c in colors_dict.items():
        hex_val = c.value.lower() if hasattr(c, 'value') else str(c.get('value', '')).lower()

        # IMPORTANT: Skip invalid/garbage color values (CSS parsing errors)
        if not _is_valid_hex_color(hex_val):
            continue

        freq = c.frequency if hasattr(c, 'frequency') else c.get('frequency', 0)

        # Check if this color has a semantic override
        semantic_name = overrides.get(hex_val)
        if semantic_name:
            result[semantic_name] = {
                "value": hex_val,
                "type": "color",
                "source": "semantic",
            }
        else:
            # Check for garbage names (firecrawl.N, numeric-only, etc.)
            base_name = (c.suggested_name if hasattr(c, 'suggested_name') else name) or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").lower()

            # Detect garbage names and generate proper color-based names
            is_garbage_name = (
                'firecrawl' in clean_name.lower() or
                clean_name.split('.')[-1].isdigit() or  # Ends with just a number
                len(clean_name.split('.')) == 2 and clean_name.split('.')[-1].isdigit()  # color.34
            )

            if is_garbage_name:
                # Generate proper name based on color characteristics
                clean_name = _generate_color_name_from_hex(hex_val, used_generated_names)
                used_generated_names.add(clean_name)
            elif not clean_name.startswith("color."):
                clean_name = f"color.{clean_name}"

            remaining.append((clean_name, hex_val, freq))

    # Sort remaining by frequency (highest first), take up to max
    remaining.sort(key=lambda x: -x[2])
    slots_left = max_colors - len(result)
    for clean_name, hex_val, freq in remaining[:slots_left]:
        if clean_name not in result:
            result[clean_name] = {
                "value": hex_val,
                "type": "color",
                "source": "detected",
            }

    return result


def export_stage1_json():
    """Export Stage 1 tokens (as-is extraction) to W3C DTCG format."""
    if not state.desktop_normalized:
        gr.Warning("No tokens extracted yet. Complete Stage 1 extraction first.")
        return json.dumps({
            "error": "No tokens extracted yet.",
            "how_to_fix": "Go to Step 1, enter a URL, discover pages, and extract tokens first.",
            "stage": "Stage 1 required"
        }, indent=2)

    # W3C DTCG format: nested structure, no wrapper, $value/$type
    result = {}
    token_count = 0

    # =========================================================================
    # COLORS — Nested structure with $value, $type, $description
    # =========================================================================
    if state.desktop_normalized and state.desktop_normalized.colors:
        overrides = _get_semantic_color_overrides()
        consolidated = _consolidate_colors(
            state.desktop_normalized.colors, overrides, max_colors=30,
        )
        for flat_key, entry in consolidated.items():
            # flat_key = "color.brand.primary"
            source = entry.get("source", "extracted")
            source_label = "LLM Semantic" if source == "semantic" else "Auto-Generated" if source == "detected" else "Extracted"
            dtcg_token = _to_dtcg_token(entry["value"], "color", description=source_label)
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # =========================================================================
    # TYPOGRAPHY — Nested structure with viewport suffix
    # =========================================================================
    # Desktop typography
    if state.desktop_normalized and state.desktop_normalized.typography:
        for name, t in state.desktop_normalized.typography.items():
            base_name = t.suggested_name or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
            if not clean_name.startswith("font."):
                clean_name = f"font.{clean_name}"

            flat_key = f"{clean_name}.desktop"
            typo_value = {
                "fontFamily": t.font_family,
                "fontSize": t.font_size,
                "fontWeight": str(t.font_weight),
                "lineHeight": t.line_height or "1.5",
            }
            dtcg_token = _to_dtcg_token(typo_value, "typography", description="Extracted from site")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # Mobile typography
    if state.mobile_normalized and state.mobile_normalized.typography:
        for name, t in state.mobile_normalized.typography.items():
            base_name = t.suggested_name or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
            if not clean_name.startswith("font."):
                clean_name = f"font.{clean_name}"

            flat_key = f"{clean_name}.mobile"
            typo_value = {
                "fontFamily": t.font_family,
                "fontSize": t.font_size,
                "fontWeight": str(t.font_weight),
                "lineHeight": t.line_height or "1.5",
            }
            dtcg_token = _to_dtcg_token(typo_value, "typography", description="Extracted from site")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # =========================================================================
    # SPACING — Nested structure with viewport suffix
    # =========================================================================
    # Desktop spacing
    if state.desktop_normalized and state.desktop_normalized.spacing:
        for name, s in state.desktop_normalized.spacing.items():
            base_name = s.suggested_name or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
            if not clean_name.startswith("space."):
                clean_name = f"space.{clean_name}"

            flat_key = f"{clean_name}.desktop"
            dtcg_token = _to_dtcg_token(s.value, "dimension", description="Extracted from site")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # Mobile spacing
    if state.mobile_normalized and state.mobile_normalized.spacing:
        for name, s in state.mobile_normalized.spacing.items():
            base_name = s.suggested_name or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
            if not clean_name.startswith("space."):
                clean_name = f"space.{clean_name}"

            flat_key = f"{clean_name}.mobile"
            dtcg_token = _to_dtcg_token(s.value, "dimension", description="Extracted from site")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # =========================================================================
    # BORDER RADIUS — Nested structure (DTCG uses "dimension" type for radii)
    # =========================================================================
    if state.desktop_normalized and state.desktop_normalized.radius:
        seen_radius = {}
        for name, r in state.desktop_normalized.radius.items():
            token_name = _get_radius_token_name(r.value, seen_radius)
            # Convert "radius.md" to nested: radius.md (keep as "radius" for consistency)
            flat_key = token_name
            dtcg_token = _to_dtcg_token(r.value, "dimension", description="Extracted from site")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # =========================================================================
    # SHADOWS — W3C DTCG shadow format
    # =========================================================================
    if state.desktop_normalized and state.desktop_normalized.shadows:
        shadow_names = ["xs", "sm", "md", "lg", "xl", "2xl"]
        sorted_shadows = sorted(
            state.desktop_normalized.shadows.items(),
            key=lambda x: _get_shadow_blur(x[1].value),
        )
        for i, (name, s) in enumerate(sorted_shadows):
            size_name = shadow_names[i] if i < len(shadow_names) else str(i + 1)
            flat_key = f"shadow.{size_name}"
            # Parse CSS shadow and convert to DTCG format
            parsed = _parse_shadow_to_tokens_studio(s.value)
            dtcg_shadow_value = _shadow_to_dtcg(parsed)
            dtcg_token = _to_dtcg_token(dtcg_shadow_value, "shadow", description="Extracted from site")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    json_str = json.dumps(result, indent=2, default=str)
    gr.Info(f"Stage 1 exported: {token_count} tokens (W3C DTCG format)")
    return json_str


def export_tokens_json():
    """Export final tokens with selected upgrades applied - FLAT structure for Figma Tokens Studio."""
    if not state.desktop_normalized:
        gr.Warning("No tokens extracted yet. Complete Stage 1 extraction first.")
        return json.dumps({
            "error": "No tokens extracted yet.",
            "how_to_fix": "Complete Stage 1 extraction first, then optionally run Stage 2 analysis before exporting.",
            "stage": "Stage 1 required"
        }, indent=2)

    # Get selected upgrades
    upgrades = getattr(state, 'selected_upgrades', {})
    if not upgrades:
        state.log("⚠️ Exporting final JSON without Stage 2 upgrades applied. Consider running Stage 2 analysis first.")
    type_scale_choice = upgrades.get('type_scale', 'Keep Current')
    spacing_choice = upgrades.get('spacing', 'Keep Current')
    apply_ramps = upgrades.get('color_ramps', True)
    
    # Determine ratio from choice
    ratio = None
    if "1.2" in type_scale_choice:
        ratio = 1.2
    elif "1.25" in type_scale_choice:
        ratio = 1.25
    elif "1.333" in type_scale_choice:
        ratio = 1.333
    
    # Determine spacing base
    spacing_base = None
    if "8px" in spacing_choice:
        spacing_base = 8
    elif "4px" in spacing_choice:
        spacing_base = 4
    
    # W3C DTCG format: nested structure, no wrapper
    result = {}
    token_count = 0

    fonts_info = get_detected_fonts()
    primary_font = fonts_info.get("primary", "sans-serif")
    
    # =========================================================================
    # COLORS — Consolidated with semantic naming + optional ramps
    # =========================================================================
    if state.desktop_normalized and state.desktop_normalized.colors:
        from core.color_utils import generate_color_ramp

        overrides = _get_semantic_color_overrides()
        consolidated = _consolidate_colors(
            state.desktop_normalized.colors, overrides, max_colors=30,
        )

        for flat_key, entry in consolidated.items():
            if apply_ramps:
                try:
                    ramp = generate_color_ramp(entry["value"])
                    shades = ["50", "100", "200", "300", "400", "500", "600", "700", "800", "900", "950"]
                    for i, shade in enumerate(shades):
                        if i < len(ramp):
                            shade_key = f"{flat_key}.{shade}"
                            color_val = ramp[i] if isinstance(ramp[i], str) else ramp[i].get("hex", entry["value"])
                            dtcg_token = _to_dtcg_token(color_val, "color")
                            _flat_key_to_nested(shade_key, dtcg_token, result)
                            token_count += 1
                except (ValueError, KeyError, TypeError, IndexError):
                    dtcg_token = _to_dtcg_token(entry["value"], "color")
                    _flat_key_to_nested(flat_key, dtcg_token, result)
                    token_count += 1
            else:
                dtcg_token = _to_dtcg_token(entry["value"], "color")
                _flat_key_to_nested(flat_key, dtcg_token, result)
                token_count += 1
    
    # =========================================================================
    # TYPOGRAPHY - FLAT structure with viewport suffix
    # =========================================================================
    base_size = get_base_font_size()
    token_names = [
        "font.display.2xl", "font.display.xl", "font.display.lg", "font.display.md",
        "font.heading.xl", "font.heading.lg", "font.heading.md", "font.heading.sm",
        "font.body.lg", "font.body.md", "font.body.sm", "font.caption", "font.overline"
    ]
    
    # Weight + lineHeight mapping by token tier
    _weight_map = {
        "display": "700", "heading": "600",
        "body": "400", "caption": "400", "overline": "500",
    }
    _lh_map = {
        "display": "1.2", "heading": "1.3",
        "body": "1.5", "caption": "1.4", "overline": "1.2",
    }

    def _tier_from_token(token_name: str) -> str:
        """Extract tier (display/heading/body/caption/overline) from token name."""
        for tier in ("display", "heading", "body", "caption", "overline"):
            if tier in token_name:
                return tier
        return "body"

    # Desktop typography — W3C DTCG format
    if ratio:
        scales = [int(round(base_size * (ratio ** (8-i)) / 2) * 2) for i in range(13)]
        for i, token_name in enumerate(token_names):
            tier = _tier_from_token(token_name)
            flat_key = f"{token_name}.desktop"
            typo_value = {
                "fontFamily": primary_font,
                "fontSize": f"{scales[i]}px",
                "fontWeight": _weight_map.get(tier, "400"),
                "lineHeight": _lh_map.get(tier, "1.5"),
            }
            dtcg_token = _to_dtcg_token(typo_value, "typography")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1
    elif state.desktop_normalized and state.desktop_normalized.typography:
        for name, t in state.desktop_normalized.typography.items():
            base_name = t.suggested_name or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
            if not clean_name.startswith("font."):
                clean_name = f"font.{clean_name}"

            flat_key = f"{clean_name}.desktop"
            typo_value = {
                "fontFamily": t.font_family,
                "fontSize": t.font_size,
                "fontWeight": str(t.font_weight),
                "lineHeight": t.line_height or "1.5",
            }
            dtcg_token = _to_dtcg_token(typo_value, "typography")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1

    # Mobile typography — W3C DTCG format
    if ratio:
        mobile_factor = 0.875
        scales = [int(round(base_size * mobile_factor * (ratio ** (8-i)) / 2) * 2) for i in range(13)]
        for i, token_name in enumerate(token_names):
            tier = _tier_from_token(token_name)
            flat_key = f"{token_name}.mobile"
            typo_value = {
                "fontFamily": primary_font,
                "fontSize": f"{scales[i]}px",
                "fontWeight": _weight_map.get(tier, "400"),
                "lineHeight": _lh_map.get(tier, "1.5"),
            }
            dtcg_token = _to_dtcg_token(typo_value, "typography")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1
    elif state.mobile_normalized and state.mobile_normalized.typography:
        for name, t in state.mobile_normalized.typography.items():
            base_name = t.suggested_name or name
            clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
            if not clean_name.startswith("font."):
                clean_name = f"font.{clean_name}"

            flat_key = f"{clean_name}.mobile"
            typo_value = {
                "fontFamily": t.font_family,
                "fontSize": t.font_size,
                "fontWeight": str(t.font_weight),
                "lineHeight": t.line_height or "1.5",
            }
            dtcg_token = _to_dtcg_token(typo_value, "typography")
            _flat_key_to_nested(flat_key, dtcg_token, result)
            token_count += 1
    
    # =========================================================================
    # SPACING — W3C DTCG format with nested structure
    # =========================================================================
    spacing_token_names = [
        "space.1", "space.2", "space.3", "space.4", "space.5",
        "space.6", "space.8", "space.10", "space.12", "space.16"
    ]

    if spacing_base:
        # Generate grid-aligned spacing for both viewports
        for i, token_name in enumerate(spacing_token_names):
            value = spacing_base * (i + 1)

            # Desktop
            desktop_key = f"{token_name}.desktop"
            dtcg_token = _to_dtcg_token(f"{value}px", "dimension")
            _flat_key_to_nested(desktop_key, dtcg_token, result)
            token_count += 1

            # Mobile (same values)
            mobile_key = f"{token_name}.mobile"
            dtcg_token = _to_dtcg_token(f"{value}px", "dimension")
            _flat_key_to_nested(mobile_key, dtcg_token, result)
            token_count += 1
    else:
        # Keep original with nested structure
        if state.desktop_normalized and state.desktop_normalized.spacing:
            for name, s in state.desktop_normalized.spacing.items():
                base_name = s.suggested_name or name
                clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
                if not clean_name.startswith("space."):
                    clean_name = f"space.{clean_name}"

                desktop_key = f"{clean_name}.desktop"
                dtcg_token = _to_dtcg_token(s.value, "dimension")
                _flat_key_to_nested(desktop_key, dtcg_token, result)
                token_count += 1

        if state.mobile_normalized and state.mobile_normalized.spacing:
            for name, s in state.mobile_normalized.spacing.items():
                base_name = s.suggested_name or name
                clean_name = base_name.replace(" ", ".").replace("_", ".").replace("-", ".").lower()
                if not clean_name.startswith("space."):
                    clean_name = f"space.{clean_name}"

                mobile_key = f"{clean_name}.mobile"
                dtcg_token = _to_dtcg_token(s.value, "dimension")
                _flat_key_to_nested(mobile_key, dtcg_token, result)
                token_count += 1

    # =========================================================================
    # BORDER RADIUS — W3C DTCG format (uses "dimension" type per spec)
    # =========================================================================
    if state.desktop_normalized and state.desktop_normalized.radius:
        seen_radius = {}
        for name, r in state.desktop_normalized.radius.items():
            token_name = _get_radius_token_name(r.value, seen_radius)
            # DTCG uses "dimension" for radii, not "borderRadius"
            dtcg_token = _to_dtcg_token(r.value, "dimension")
            _flat_key_to_nested(token_name, dtcg_token, result)
            token_count += 1

    # =========================================================================
    # SHADOWS — W3C DTCG format with shadow spec
    # =========================================================================
    if state.desktop_normalized and state.desktop_normalized.shadows:
        shadow_names = ["shadow.xs", "shadow.sm", "shadow.md", "shadow.lg", "shadow.xl", "shadow.2xl"]
        sorted_shadows = sorted(
            state.desktop_normalized.shadows.items(),
            key=lambda x: _get_shadow_blur(x[1].value),
        )
        for i, (name, s) in enumerate(sorted_shadows):
            token_name = shadow_names[i] if i < len(shadow_names) else f"shadow.{i + 1}"
            # Convert to DTCG shadow format
            shadow_value = _shadow_to_dtcg(_parse_shadow_to_tokens_studio(s.value))
            dtcg_token = _to_dtcg_token(shadow_value, "shadow")
            _flat_key_to_nested(token_name, dtcg_token, result)
            token_count += 1

    json_str = json.dumps(result, indent=2, default=str)
    upgrades_note = " (with upgrades)" if upgrades else " (no upgrades applied)"
    gr.Info(f"Final export: {token_count} tokens{upgrades_note}")
    return json_str


# =============================================================================
# UI BUILDING
# =============================================================================

def create_ui():
    """Create the Gradio interface with corporate branding."""
    
    # Corporate theme customization
    corporate_theme = gr.themes.Base(
        primary_hue=gr.themes.colors.blue,
        secondary_hue=gr.themes.colors.slate,
        neutral_hue=gr.themes.colors.slate,
        font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
        font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui-monospace", "monospace"],
    ).set(
        # Colors
        body_background_fill="#f8fafc",
        body_background_fill_dark="#0f172a",
        block_background_fill="white",
        block_background_fill_dark="#1e293b",
        block_border_color="#e2e8f0",
        block_border_color_dark="#334155",
        block_label_background_fill="#f1f5f9",
        block_label_background_fill_dark="#1e293b",
        block_title_text_color="#0f172a",
        block_title_text_color_dark="#f1f5f9",
        
        # Primary button
        button_primary_background_fill="#2563eb",
        button_primary_background_fill_hover="#1d4ed8",
        button_primary_text_color="white",
        
        # Secondary button
        button_secondary_background_fill="#f1f5f9",
        button_secondary_background_fill_hover="#e2e8f0",
        button_secondary_text_color="#1e293b",
        
        # Input fields
        input_background_fill="#ffffff",
        input_background_fill_dark="#1e293b",
        input_border_color="#cbd5e1",
        input_border_color_dark="#475569",
        
        # Shadows and radius
        block_shadow="0 1px 3px rgba(0,0,0,0.1)",
        block_shadow_dark="0 1px 3px rgba(0,0,0,0.3)",
        block_border_width="1px",
        block_radius="8px",
        
        # Text
        body_text_color="#1e293b",
        body_text_color_dark="#e2e8f0",
        body_text_size="14px",
    )
    
    # Custom CSS for additional styling
    custom_css = """
    /* Global styles */
    .gradio-container {
        max-width: 1400px !important;
        margin: 0 auto !important;
    }
    
    /* Header branding */
    .app-header {
        background: linear-gradient(135deg, #1e40af 0%, #3b82f6 100%);
        padding: 24px 32px;
        border-radius: 12px;
        margin-bottom: 24px;
        color: white;
    }
    .app-header h1 {
        margin: 0 0 8px 0;
        font-size: 28px;
        font-weight: 700;
    }
    .app-header p {
        margin: 0;
        opacity: 0.9;
        font-size: 14px;
    }
    
    /* Stage indicators */
    .stage-header {
        background: linear-gradient(90deg, #f1f5f9 0%, #ffffff 100%);
        padding: 16px 20px;
        border-radius: 8px;
        border-left: 4px solid #2563eb;
        margin-bottom: 16px;
    }
    .stage-header h2 {
        margin: 0;
        font-size: 18px;
        color: #1e293b;
    }
    
    /* Log styling */
    .log-container textarea {
        font-family: 'JetBrains Mono', monospace !important;
        font-size: 12px !important;
        line-height: 1.6 !important;
        background: #0f172a !important;
        color: #e2e8f0 !important;
        border-radius: 8px !important;
    }
    
    /* Color swatch */
    .color-swatch {
        display: inline-block;
        width: 24px;
        height: 24px;
        border-radius: 4px;
        margin-right: 8px;
        vertical-align: middle;
        border: 1px solid rgba(0,0,0,0.1);
    }
    
    /* Score badges */
    .score-badge {
        display: inline-block;
        padding: 4px 12px;
        border-radius: 20px;
        font-weight: 600;
        font-size: 13px;
    }
    .score-badge.high { background: #dcfce7; color: #166534; }
    .score-badge.medium { background: #fef3c7; color: #92400e; }
    .score-badge.low { background: #fee2e2; color: #991b1b; }
    
    /* Benchmark cards */
    .benchmark-card {
        background: #f8fafc;
        border: 1px solid #e2e8f0;
        border-radius: 8px;
        padding: 16px;
        margin-bottom: 12px;
    }
    .benchmark-card.selected {
        border-color: #2563eb;
        background: #eff6ff;
    }
    
    /* Action items */
    .action-item {
        background: white;
        border: 1px solid #e2e8f0;
        border-radius: 8px;
        padding: 16px;
        margin-bottom: 8px;
    }
    .action-item.high-priority {
        border-left: 4px solid #ef4444;
    }
    .action-item.medium-priority {
        border-left: 4px solid #f59e0b;
    }
    
    /* Progress indicator */
    .progress-bar {
        height: 4px;
        background: #e2e8f0;
        border-radius: 2px;
        overflow: hidden;
    }
    .progress-bar-fill {
        height: 100%;
        background: linear-gradient(90deg, #2563eb, #3b82f6);
        transition: width 0.3s ease;
    }
    
    /* Accordion styling */
    .accordion-header {
        font-weight: 600 !important;
    }
    
    /* Table styling */
    table {
        border-collapse: collapse;
        width: 100%;
    }
    th {
        background: #f1f5f9;
        color: #1e293b;
        padding: 12px;
        text-align: left;
        font-weight: 600;
        border-bottom: 2px solid #e2e8f0;
    }
    td {
        padding: 12px;
        color: #1e293b;
        border-bottom: 1px solid #e2e8f0;
    }

    /* Section descriptions */
    .section-desc p, .section-desc {
        font-size: 13px !important;
        color: #64748b !important;
        line-height: 1.5 !important;
        margin-top: -4px !important;
        margin-bottom: 12px !important;
    }
    .dark .section-desc p, .dark .section-desc {
        color: #94a3b8 !important;
    }

    /* Success messages */
    .success-msg { background: #f0fdf4; border: 1px solid #bbf7d0; border-radius: 8px; padding: 16px; margin: 8px 0; }
    .success-msg h2 { color: #166534 !important; }
    .dark .success-msg { background: #052e16 !important; border-color: #166534 !important; }
    .dark .success-msg h2 { color: #bbf7d0 !important; }
    .dark .success-msg p { color: #d1d5db !important; }

    /* Error messages */
    .error-msg { background: #fef2f2; border: 1px solid #fecaca; border-radius: 8px; padding: 16px; margin: 8px 0; }
    .error-msg h2 { color: #991b1b !important; }
    .dark .error-msg { background: #450a0a !important; border-color: #991b1b !important; }
    .dark .error-msg h2 { color: #fecaca !important; }
    .dark .error-msg p { color: #d1d5db !important; }

    /* Placeholder messages */
    .placeholder-msg {
        padding: 20px;
        background: #f5f5f5;
        border-radius: 8px;
        color: #666;
    }
    .placeholder-msg.placeholder-lg {
        padding: 40px;
        text-align: center;
    }

    /* Progress bar */
    .progress-bar {
        background: #e2e8f0;
    }

    /* Dark mode adjustments */
    .dark .stage-header {
        background: linear-gradient(90deg, #1e293b 0%, #0f172a 100%);
        border-left-color: #3b82f6;
    }
    .dark .stage-header h2 {
        color: #f1f5f9;
    }
    .dark .stage-header-subtitle,
    .dark .tip-text {
        color: #94a3b8 !important;
    }
    .dark .benchmark-card {
        background: #1e293b;
        border-color: #334155;
    }
    .dark .action-item {
        background: #1e293b;
        border-color: #475569;
        color: #e2e8f0;
    }
    /* Dark mode: Placeholder messages */
    .dark .placeholder-msg {
        background: #1e293b !important;
        color: #94a3b8 !important;
    }
    /* Dark mode: Progress bar */
    .dark .progress-bar {
        background: #334155 !important;
    }
    /* Dark mode: Gradio Dataframe tables */
    .dark table th {
        background: #1e293b !important;
        color: #e2e8f0 !important;
        border-bottom-color: #475569 !important;
    }
    .dark table td {
        color: #e2e8f0 !important;
        border-bottom-color: #334155 !important;
    }
    .dark table tr {
        background: #0f172a !important;
    }
    .dark table tr:nth-child(even) {
        background: #1e293b !important;
    }
    /* Dark mode: HTML preview tables (typography, benchmarks) */
    .dark .typography-preview {
        background: #1e293b !important;
    }
    .dark .typography-preview th {
        background: #334155 !important;
        color: #e2e8f0 !important;
        border-bottom-color: #475569 !important;
    }
    .dark .typography-preview td {
        color: #e2e8f0 !important;
    }
    .dark .typography-preview .meta-row {
        background: #1e293b !important;
        border-top-color: #334155 !important;
    }
    .dark .typography-preview .scale-name,
    .dark .typography-preview .scale-label {
        color: #f1f5f9 !important;
        background: #475569 !important;
    }
    .dark .typography-preview .meta {
        color: #cbd5e1 !important;
    }
    .dark .typography-preview .preview-cell {
        background: #0f172a !important;
        border-bottom-color: #334155 !important;
    }
    .dark .typography-preview .preview-text {
        color: #f1f5f9 !important;
    }
    .dark .typography-preview tr:hover .preview-cell {
        background: #1e293b !important;
    }

    /* Dark mode: Colors AS-IS preview */
    .dark .colors-asis-header {
        color: #e2e8f0 !important;
        background: #1e293b !important;
    }
    .dark .colors-asis-preview {
        background: #0f172a !important;
    }
    .dark .color-row-asis {
        background: #1e293b !important;
        border-color: #475569 !important;
    }
    .dark .color-name-asis {
        color: #f1f5f9 !important;
    }
    .dark .frequency {
        color: #cbd5e1 !important;
    }
    .dark .color-meta-asis .aa-pass {
        color: #22c55e !important;
        background: #14532d !important;
    }
    .dark .color-meta-asis .aa-fail {
        color: #f87171 !important;
        background: #450a0a !important;
    }
    .dark .context-badge {
        background: #334155 !important;
        color: #e2e8f0 !important;
    }

    /* Dark mode: Color ramps preview */
    .dark .color-ramps-preview {
        background: #0f172a !important;
    }
    .dark .ramps-header-info {
        color: #e2e8f0 !important;
        background: #1e293b !important;
    }
    .dark .ramp-header {
        background: #1e293b !important;
    }
    .dark .ramp-header-label {
        color: #cbd5e1 !important;
    }
    .dark .color-row {
        background: #1e293b !important;
        border-color: #475569 !important;
    }
    .dark .color-name {
        color: #f1f5f9 !important;
        background: #475569 !important;
    }
    .dark .color-hex {
        color: #cbd5e1 !important;
    }

    /* Dark mode: Spacing preview */
    .dark .spacing-asis-preview {
        background: #0f172a !important;
    }
    .dark .spacing-row-asis {
        background: #1e293b !important;
    }
    .dark .spacing-label {
        color: #f1f5f9 !important;
    }

    /* Dark mode: Radius preview */
    .dark .radius-asis-preview {
        background: #0f172a !important;
    }
    .dark .radius-item {
        background: #1e293b !important;
    }
    .dark .radius-label {
        color: #f1f5f9 !important;
    }

    /* Dark mode: Shadows preview */
    .dark .shadows-asis-preview {
        background: #0f172a !important;
    }
    .dark .shadow-item {
        background: #1e293b !important;
    }
    .dark .shadow-box {
        background: #334155 !important;
    }
    .dark .shadow-label {
        color: #f1f5f9 !important;
    }
    .dark .shadow-value {
        color: #94a3b8 !important;
    }

    /* Dark mode: Semantic color ramps */
    .dark .sem-ramps-preview {
        background: #0f172a !important;
    }
    .dark .sem-category {
        background: #1e293b !important;
        border-color: #475569 !important;
    }
    .dark .sem-cat-title {
        color: #f1f5f9 !important;
        border-bottom-color: #475569 !important;
    }
    .dark .sem-color-row {
        background: #0f172a !important;
        border-color: #334155 !important;
    }
    .dark .sem-role {
        color: #f1f5f9 !important;
    }
    .dark .sem-hex {
        color: #cbd5e1 !important;
    }
    .dark .llm-rec {
        background: #422006 !important;
        border-color: #b45309 !important;
    }
    .dark .rec-label {
        color: #fbbf24 !important;
    }
    .dark .rec-issue {
        color: #fde68a !important;
    }
    .dark .rec-arrow {
        color: #fbbf24 !important;
    }
    .dark .llm-summary {
        background: #1e3a5f !important;
        border-color: #3b82f6 !important;
    }
    .dark .llm-summary h4 {
        color: #93c5fd !important;
    }
    .dark .llm-summary ul,
    .dark .llm-summary li {
        color: #bfdbfe !important;
    }

    /* Dark mode: Score badges */
    .dark .score-badge.high { background: #14532d; color: #86efac; }
    .dark .score-badge.medium { background: #422006; color: #fde68a; }
    .dark .score-badge.low { background: #450a0a; color: #fca5a5; }

    /* Dark mode: Benchmark & action cards */
    .dark .benchmark-card.selected {
        border-color: #3b82f6;
        background: #1e3a5f;
    }
    .dark .action-item.high-priority {
        border-left-color: #ef4444;
    }
    .dark .action-item.medium-priority {
        border-left-color: #f59e0b;
    }

    /* Dark mode: Gradio markdown rendered tables */
    .dark .prose table th,
    .dark .markdown-text table th {
        background: #1e293b !important;
        color: #e2e8f0 !important;
        border-color: #475569 !important;
    }
    .dark .prose table td,
    .dark .markdown-text table td {
        color: #e2e8f0 !important;
        border-color: #334155 !important;
    }
    .dark .prose table tr,
    .dark .markdown-text table tr {
        background: #0f172a !important;
    }
    .dark .prose table tr:nth-child(even),
    .dark .markdown-text table tr:nth-child(even) {
        background: #1e293b !important;
    }

    /* Dark mode: Generic text in HTML components */
    .dark .gradio-html p,
    .dark .gradio-html span,
    .dark .gradio-html div {
        color: #e2e8f0;
    }
    """
    
    with gr.Blocks(
        title="Design System Extractor v2",
        theme=corporate_theme,
        css=custom_css
    ) as app:
        
        # Header with branding
        gr.HTML("""
        <div class="app-header">
            <h1>🎨 Design System Extractor v2</h1>
            <p>Reverse-engineer design systems from live websites • AI-powered analysis • Figma-ready export</p>
        </div>
        """)
        gr.Markdown("This tool works in **3 stages**: (1) Discover & extract design tokens from a live website, "
                     "(2) Run AI-powered analysis to benchmark and improve your tokens, "
                     "(3) Export Figma-ready JSON. Start by entering a URL below.",
                     elem_classes=["section-desc"])

        # =================================================================
        # CONFIGURATION
        # =================================================================

        with gr.Accordion("⚙️ Configuration", open=not bool(HF_TOKEN_FROM_ENV)):
            gr.Markdown("**HuggingFace Token** — Required for Stage 2 AI analysis (LLM agents). "
                        "Get a free token at [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens). "
                        "Stage 1 (extraction) works without a token. If set as an environment variable, it loads automatically.",
                        elem_classes=["section-desc"])
            with gr.Row():
                hf_token_input = gr.Textbox(
                    label="HF Token", placeholder="hf_xxxx", type="password",
                    scale=4, value=HF_TOKEN_FROM_ENV,
                )
                save_token_btn = gr.Button("💾 Save", scale=1)
            token_status = gr.Markdown("✅ Token loaded" if HF_TOKEN_FROM_ENV else "⏳ Enter token")
            
            def save_token(token):
                if token and len(token) > 10:
                    os.environ["HF_TOKEN"] = token.strip()
                    return "✅ **Token saved!** You can now use Stage 2 AI analysis. Close this section and enter a URL below to begin."
                return "❌ **Invalid token** — please enter a valid HuggingFace token (starts with `hf_`, at least 10 characters). Get one free at [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)."
            
            save_token_btn.click(save_token, [hf_token_input], [token_status])
        
        # =================================================================
        # URL INPUT & PAGE DISCOVERY
        # =================================================================
        
        with gr.Accordion("🔍 Step 1: Discover Pages", open=True):
            gr.Markdown("Enter the homepage URL of any website. The crawler will find up to 20 internal pages "
                        "(homepage, about, contact, product pages, etc.). You then select which pages to scan "
                        "for design tokens (colors, typography, spacing, radius, and shadows).",
                        elem_classes=["section-desc"])

            with gr.Row():
                url_input = gr.Textbox(label="Website URL", placeholder="https://example.com", scale=4)
                discover_btn = gr.Button("🔍 Discover Pages", variant="primary", scale=1)
            gr.Markdown("*Enter the full URL including `https://` — the crawler will follow internal links from this page.*",
                        elem_classes=["section-desc"])

            discover_status = gr.Markdown("")

            with gr.Row():
                log_output = gr.Textbox(label="📋 Log", lines=8, interactive=False)

            pages_table = gr.Dataframe(
                headers=["Select", "URL", "Title", "Type", "Status"],
                datatype=["bool", "str", "str", "str", "str"],
                label="Discovered Pages",
                interactive=True,
                visible=False,
            )
            gr.Markdown("*Use the **Select** checkbox to choose pages for extraction. Uncheck pages you want to skip "
                        "(login pages, error pages, etc.). **Type** shows the detected page category. Up to 10 pages will be processed.*",
                        elem_classes=["section-desc"])

            gr.Markdown("*Extraction scans each selected page at two viewport sizes — Desktop (1440px) and Mobile (375px) — "
                        "pulling colors, typography, spacing, radius, and shadows from computed CSS.*",
                        elem_classes=["section-desc"])
            extract_btn = gr.Button("🚀 Extract Tokens (Desktop + Mobile)", variant="primary", visible=False)
        
        # =================================================================
        # STAGE 1: EXTRACTION REVIEW
        # =================================================================
        
        with gr.Accordion("📊 Stage 1: Review Extracted Tokens", open=False) as stage1_accordion:

            extraction_status = gr.Markdown("")

            gr.Markdown("Review the design tokens extracted from your website. Use the **viewport toggle** to switch between "
                        "Desktop (1440px) and Mobile (375px) data. **Accept or reject** individual tokens using the checkboxes — "
                        "rejected tokens will be excluded from your design system export.",
                        elem_classes=["section-desc"])

            viewport_toggle = gr.Radio(
                choices=["Desktop (1440px)", "Mobile (375px)"],
                value="Desktop (1440px)",
                label="Viewport",
            )

            with gr.Tabs():
                with gr.Tab("🎨 Colors"):
                    gr.Markdown("*Each row is a unique color found on the site. **Confidence** shows extraction certainty. "
                                "**AA** indicates WCAG accessibility pass/fail for normal text. **Context** shows where the color was used.*",
                                elem_classes=["section-desc"])
                    colors_table = gr.Dataframe(
                        headers=["Accept", "Color", "Suggested Name", "Frequency", "Confidence", "Contrast", "AA", "Context"],
                        datatype=["bool", "str", "str", "number", "str", "str", "str", "str"],
                        label="Colors",
                        interactive=True,
                    )
                    with gr.Accordion("👁️ Visual Preview", open=False):
                        stage1_colors_preview = gr.HTML(
                            value="<div class='placeholder-msg'>Colors preview will appear after extraction...</div>",
                            label="Colors Preview"
                        )

                with gr.Tab("📝 Typography"):
                    gr.Markdown("*Detected font styles sorted by frequency. **Size** is computed font-size, **Weight** is font-weight "
                                "(400=regular, 700=bold). **Suggested Name** is a semantic token name (e.g., heading.xl). "
                                "Uncheck rows to exclude from your design system.*",
                                elem_classes=["section-desc"])
                    typography_table = gr.Dataframe(
                        headers=["Accept", "Font", "Size", "Weight", "Line Height", "Suggested Name", "Frequency", "Confidence"],
                        datatype=["bool", "str", "str", "str", "str", "str", "number", "str"],
                        label="Typography",
                        interactive=True,
                    )
                    with gr.Accordion("👁️ Visual Preview", open=False):
                        stage1_typography_preview = gr.HTML(
                            value="<div class='placeholder-msg'>Typography preview will appear after extraction...</div>",
                            label="Typography Preview"
                        )

                with gr.Tab("📏 Spacing"):
                    gr.Markdown("*Spacing values (margins, paddings, gaps) extracted from the site. **Base 8** shows whether "
                                "the value aligns with the 8px grid standard. Values are sorted smallest to largest. "
                                "Uncheck irregular spacing values you want to exclude.*",
                                elem_classes=["section-desc"])
                    spacing_table = gr.Dataframe(
                        headers=["Accept", "Value", "Pixels", "Suggested Name", "Frequency", "Base 8", "Confidence"],
                        datatype=["bool", "str", "str", "str", "number", "str", "str"],
                        label="Spacing",
                        interactive=True,
                    )
                    with gr.Accordion("👁️ Visual Preview", open=False):
                        stage1_spacing_preview = gr.HTML(
                            value="<div class='placeholder-msg'>Spacing preview will appear after extraction...</div>",
                            label="Spacing Preview"
                        )

                with gr.Tab("🔘 Radius"):
                    gr.Markdown("*Border-radius values found across UI elements (buttons, cards, inputs). **Context** shows "
                                "which elements use each value. A consistent radius scale creates a cohesive UI.*",
                                elem_classes=["section-desc"])
                    radius_table = gr.Dataframe(
                        headers=["Accept", "Value", "Frequency", "Context"],
                        datatype=["bool", "str", "number", "str"],
                        label="Border Radius",
                        interactive=True,
                    )
                    with gr.Accordion("👁️ Visual Preview", open=False):
                        stage1_radius_preview = gr.HTML(
                            value="<div class='placeholder-msg'>Radius preview will appear after extraction...</div>",
                            label="Radius Preview"
                        )

                with gr.Tab("🌑 Shadows"):
                    gr.Markdown("*Box shadow values used for elevation and depth across the site. "
                                "Shows blur radius, spread, and color for each shadow layer.*",
                                elem_classes=["section-desc"])
                    stage1_shadows_preview = gr.HTML(
                        value="<div class='placeholder-msg'>Shadows preview will appear after extraction...</div>",
                        label="Shadows Preview"
                    )

                with gr.Tab("🧠 Semantic Colors"):
                    gr.Markdown("*Colors automatically categorized by their usage role: Brand (primary, secondary, accent), "
                                "Text (headings, body, muted), Background, Border, and Feedback (success, warning, error).*",
                                elem_classes=["section-desc"])
                    stage1_semantic_preview = gr.HTML(
                        value="<div class='placeholder-msg'>Semantic color analysis will appear after extraction...</div>",
                        label="Semantic Colors Preview"
                    )
            
            gr.Markdown("---")
            gr.Markdown("When you are satisfied with the accepted tokens, **proceed to Stage 2** for AI-powered analysis "
                        "and improvement suggestions. Or **download the raw Stage 1 JSON** for immediate use in Figma Tokens Studio.",
                        elem_classes=["section-desc"])
            with gr.Row():
                proceed_stage2_btn = gr.Button("➡️ Proceed to Stage 2: AI Upgrades", variant="primary")
                download_stage1_btn = gr.Button("📥 Download Stage 1 JSON", variant="secondary")
        
        # =================================================================
        # STAGE 2: AI UPGRADES
        # =================================================================
        
        with gr.Accordion("🧠 Stage 2: AI-Powered Analysis", open=False) as stage2_accordion:
            
            # Stage header
            gr.HTML("""
            <div class="stage-header">
                <h2>🧠 Stage 2: Multi-Agent Analysis</h2>
                <p class="stage-header-subtitle" style="color: #64748b; margin-top: 4px;">Rule Engine + Benchmark Research + LLM Agents</p>
            </div>
            """)
            
            stage2_status = gr.Markdown("Click **'Run Analysis'** below to start AI-powered design system analysis. "
                                       "This runs a 4-layer pipeline: Rule Engine → Benchmark Research → LLM Agents → Head Synthesizer.")

            # =============================================================
            # NEW ARCHITECTURE CONFIGURATION
            # =============================================================
            with gr.Accordion("⚙️ Analysis Configuration", open=True):
                
                # Architecture explanation
                gr.Markdown("""
                ### 🏗️ New Analysis Architecture
                
                | Layer | Type | What It Does | Cost |
                |-------|------|--------------|------|
                | **Layer 1** | Rule Engine | Type scale, AA check, spacing grid, color stats | FREE |
                | **Layer 2** | Benchmark Research | Fetch live specs via Firecrawl (24h cache) | ~$0.001 |
                | **Layer 3** | LLM Agents | Brand ID, Benchmark Advisor, Best Practices | ~$0.002 |
                | **Layer 4** | HEAD Synthesizer | Combine all → Final recommendations | ~$0.001 |
                
                **Total Cost:** ~$0.003-0.004 per analysis
                """)
                
                gr.Markdown("---")
                
                # Benchmark selection
                gr.Markdown("### 📊 Select Design Systems to Compare Against")
                gr.Markdown("*Choose which design systems to benchmark your tokens against:*")
                
                benchmark_checkboxes = gr.CheckboxGroup(
                    choices=[
                        ("🟢 Material Design 3 (Google)", "material_design_3"),
                        ("🍎 Apple HIG", "apple_hig"),
                        ("🛒 Shopify Polaris", "shopify_polaris"),
                        ("🔵 Atlassian Design System", "atlassian_design"),
                        ("🔷 IBM Carbon", "ibm_carbon"),
                        ("🌊 Tailwind CSS", "tailwind_css"),
                        ("🐜 Ant Design", "ant_design"),
                        ("⚡ Chakra UI", "chakra_ui"),
                    ],
                    value=["material_design_3", "shopify_polaris", "atlassian_design"],
                    label="Benchmarks",
                )
                
                gr.Markdown("""
                <small class="tip-text" style="color: #64748b;">
                💡 <b>Tip:</b> Select 2-4 benchmarks for best results. More benchmarks = longer analysis time.
                <br>
                📦 Results are cached for 24 hours to speed up subsequent analyses.
                </small>
                """)

            gr.Markdown("**Run Analysis** triggers the full 4-layer architecture (recommended). "
                        "**Legacy Analysis** uses the older single-agent approach. After analysis completes, "
                        "review scores, recommendations, and visual previews below, then apply your chosen upgrades.",
                        elem_classes=["section-desc"])

            # Analyze button
            with gr.Row():
                analyze_btn_v2 = gr.Button(
                    "🚀 Run Analysis (New Architecture)", 
                    variant="primary", 
                    size="lg",
                    scale=2
                )
                analyze_btn_legacy = gr.Button(
                    "🤖 Legacy Analysis", 
                    variant="secondary",
                    size="lg",
                    scale=1
                )
            
            # =============================================================
            # ANALYSIS LOG
            # =============================================================
            with gr.Accordion("📋 Analysis Log", open=True):
                gr.Markdown("*Real-time log of the analysis pipeline. Each layer reports its progress, results, and any errors. "
                            "Scroll through to see detailed statistics and individual agent outputs.*",
                            elem_classes=["section-desc"])
                stage2_log = gr.Textbox(
                    label="Log", 
                    lines=20, 
                    interactive=False,
                    elem_classes=["log-container"]
                )
            
            # =============================================================
            # SCORES DASHBOARD
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 📊 Analysis Results")
            gr.Markdown("*Overall scores for your design system across accessibility, consistency, brand alignment, and best practices. "
                        "Each score is out of 100 — aim for 70+ in all categories. Priority actions below show the highest-impact fixes.*",
                        elem_classes=["section-desc"])

            scores_dashboard = gr.HTML(
                value="<div class='placeholder-msg placeholder-lg'>Scores will appear after analysis...</div>",
                label="Scores"
            )
            
            # =============================================================
            # PRIORITY ACTIONS
            # =============================================================
            priority_actions_html = gr.HTML(
                value="<div class='placeholder-msg'>Priority actions will appear after analysis...</div>",
                label="Priority Actions"
            )
            
            # =============================================================
            # BENCHMARK COMPARISON
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 📊 Benchmark Comparison")
            gr.Markdown("*Your design tokens compared against industry-leading design systems (Material Design 3, Shopify Polaris, etc.). "
                        "Shows how closely your type scale, spacing grid, and color palette align with each benchmark. "
                        "Helps you decide which system to adopt or draw inspiration from.*",
                        elem_classes=["section-desc"])
            benchmark_comparison_md = gr.Markdown("*Benchmark comparison will appear after analysis*")
            
            # =============================================================
            # COLOR RECOMMENDATIONS
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 🎨 Color Recommendations")
            gr.Markdown("*AI-suggested color changes based on WCAG AA compliance, brand consistency, and industry best practices. "
                        "Each recommendation shows the current color, the issue found, and a suggested replacement. "
                        "Use the checkboxes to accept or reject individual changes before exporting.*",
                        elem_classes=["section-desc"])
            
            # =============================================================
            # TYPOGRAPHY SECTION
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 📐 Typography")
            gr.Markdown("*Your detected type scale compared against standard ratios (Minor Third 1.2, Major Third 1.25, Perfect Fourth 1.333). "
                        "The visual preview shows how text will look at each scale. Desktop and mobile sizes are shown separately — "
                        "choose a scale below to apply to your exported tokens.*",
                        elem_classes=["section-desc"])

            with gr.Accordion("👁️ Typography Visual Preview", open=True):
                stage2_typography_preview = gr.HTML(
                    value="<div class='placeholder-msg'>Typography preview will appear after analysis...</div>",
                    label="Typography Preview"
                )
            
            with gr.Row():
                with gr.Column(scale=2):
                    gr.Markdown("### 🖥️ Desktop (1440px)")
                    typography_desktop = gr.Dataframe(
                        headers=["Token", "Current", "Scale 1.2", "Scale 1.25 ⭐", "Scale 1.333", "Keep"],
                        datatype=["str", "str", "str", "str", "str", "str"],
                        label="Desktop Typography",
                        interactive=False,
                    )
                
                with gr.Column(scale=2):
                    gr.Markdown("### 📱 Mobile (375px)")
                    typography_mobile = gr.Dataframe(
                        headers=["Token", "Current", "Scale 1.2", "Scale 1.25 ⭐", "Scale 1.333", "Keep"],
                        datatype=["str", "str", "str", "str", "str", "str"],
                        label="Mobile Typography",
                        interactive=False,
                    )
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Select Type Scale Option")
                    type_scale_radio = gr.Radio(
                        choices=["Keep Current", "Scale 1.2 (Minor Third)", "Scale 1.25 (Major Third) ⭐", "Scale 1.333 (Perfect Fourth)"],
                        value="Scale 1.25 (Major Third) ⭐",
                        label="Type Scale",
                        interactive=True,
                    )
                    gr.Markdown("*Font family will be preserved. Sizes rounded to even numbers.*")
            
            # =============================================================
            # COLORS SECTION - Base Colors + Ramps + LLM Recommendations
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 🎨 Colors")
            gr.Markdown("*Complete color analysis: base colors extracted from your site, AI-generated semantic color ramps (50–950 shades), "
                        "and LLM-powered recommendations for accessibility fixes. The visual preview groups colors by semantic role "
                        "(brand, text, background, border, feedback).*",
                        elem_classes=["section-desc"])

            # LLM Recommendations Section (NEW)
            with gr.Accordion("🤖 LLM Color Recommendations", open=True):
                gr.Markdown("*Four AI agents analyzed your colors: **Brand Identifier** (detects primary/secondary brand colors), "
                            "**Benchmark Advisor** (compares to design system standards), **Best Practices Auditor** (WCAG, contrast, naming), "
                            "and **Head Synthesizer** (combines all findings into actionable suggestions). Use the table to accept or reject each change.*",
                            elem_classes=["section-desc"])
                
                llm_color_recommendations = gr.HTML(
                    value="<div class='placeholder-msg'>LLM recommendations will appear after analysis...</div>",
                    label="LLM Recommendations"
                )
                
                # Accept/Reject table for color recommendations
                color_recommendations_table = gr.Dataframe(
                    headers=["Accept", "Role", "Current", "Issue", "Suggested", "Contrast"],
                    datatype=["bool", "str", "str", "str", "str", "str"],
                    label="Color Recommendations",
                    interactive=True,
                    col_count=(6, "fixed"),
                )
            
            # Visual Preview
            with gr.Accordion("👁️ Color Ramps Visual Preview (Semantic Groups)", open=True):
                gr.Markdown("*AI-generated color ramps expanding each base color into a 50–950 shade scale (similar to Tailwind CSS). "
                            "Colors are grouped by semantic role. These ramps will be included in your final export if the checkbox below is enabled.*",
                            elem_classes=["section-desc"])
                stage2_color_ramps_preview = gr.HTML(
                    value="<div class='placeholder-msg'>Color ramps preview will appear after analysis...</div>",
                    label="Color Ramps Preview"
                )

            gr.Markdown("**Base Colors** — Primary colors extracted from your site, organized by frequency and semantic role:",
                        elem_classes=["section-desc"])
            base_colors_display = gr.Markdown("*Base colors will appear after analysis*")

            gr.Markdown("---")

            gr.Markdown("**Color Ramps** — Full shade tables (50–950) generated from each base color:",
                        elem_classes=["section-desc"])
            color_ramps_display = gr.Markdown("*Color ramps will appear after analysis*")
            
            color_ramps_checkbox = gr.Checkbox(
                label="✓ Generate color ramps (keeps base colors, adds 50-950 shades)",
                value=True,
            )
            
            # =============================================================
            # SPACING SECTION
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 📏 Spacing (Rule-Based)")
            gr.Markdown("*Your detected spacing values compared against standard 8px and 4px grid systems. "
                        "Consistent spacing creates visual rhythm and alignment. The 8px grid (8, 16, 24, 32...) is the industry standard — "
                        "select your preferred system below to normalize spacing in the export.*",
                        elem_classes=["section-desc"])

            with gr.Row():
                with gr.Column(scale=2):
                    spacing_comparison = gr.Dataframe(
                        headers=["Current", "8px Grid", "4px Grid"],
                        datatype=["str", "str", "str"],
                        label="Spacing Comparison",
                        interactive=False,
                    )
                
                with gr.Column(scale=1):
                    spacing_radio = gr.Radio(
                        choices=["Keep Current", "8px Base Grid ⭐", "4px Base Grid"],
                        value="8px Base Grid ⭐",
                        label="Spacing System",
                        interactive=True,
                    )
            
            # =============================================================
            # RADIUS SECTION
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 🔘 Border Radius (Rule-Based)")
            gr.Markdown("*Border radius values detected from your site, mapped to standard design tokens (radius.none → radius.full). "
                        "Consistent radius tokens ensure buttons, cards, and modals share a cohesive visual language. "
                        "Values are sorted from sharp corners to fully rounded.*",
                        elem_classes=["section-desc"])

            radius_display = gr.Markdown("*Radius tokens will appear after analysis*")
            
            # =============================================================
            # SHADOWS SECTION
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("## 🌫️ Shadows (Rule-Based)")
            gr.Markdown("*Box shadow values detected from your site, organized into elevation tokens (shadow.xs → shadow.2xl). "
                        "A well-defined shadow scale creates depth hierarchy — subtle shadows for cards, deeper shadows for modals and popovers. "
                        "Exported tokens are ready for Figma elevation styles.*",
                        elem_classes=["section-desc"])

            shadows_display = gr.Markdown("*Shadow tokens will appear after analysis*")
            
            # =============================================================
            # APPLY SECTION
            # =============================================================
            gr.Markdown("---")
            gr.Markdown("**Apply** saves your chosen type scale, spacing grid, color ramp, and LLM recommendation selections. "
                        "These choices will be baked into your Stage 3 export. **Reset** reverts all selections back to the original extracted values.",
                        elem_classes=["section-desc"])

            with gr.Row():
                apply_upgrades_btn = gr.Button("✨ Apply Selected Upgrades", variant="primary", scale=2)
                reset_btn = gr.Button("↩️ Reset to Original", variant="secondary", scale=1)
            
            apply_status = gr.Markdown("", elem_classes=["apply-status-box"])
        
        # =================================================================
        # STAGE 3: EXPORT
        # =================================================================
        
        with gr.Accordion("📦 Stage 3: Export", open=False) as stage3_accordion:
            gr.Markdown("Export your finalized design tokens as JSON, compatible with **Figma Tokens Studio**.",
                        elem_classes=["section-desc"])
            gr.Markdown("""
- **Stage 1 JSON (As-Is):** Raw extracted tokens with no modifications — useful for archival or baseline comparison. Includes desktop and mobile viewport variants.
- **Final JSON (Upgraded):** Tokens with your selected improvements applied (type scale, spacing grid, color ramps, and accepted LLM recommendations). **This is the recommended export.**

Copy the JSON output below or save it as a `.json` file for import into Figma.
            """, elem_classes=["section-desc"])

            with gr.Row():
                export_stage1_btn = gr.Button("📥 Export Stage 1 (As-Is)", variant="secondary")
                export_final_btn = gr.Button("📥 Export Final (Upgraded)", variant="primary")

            gr.Markdown("*The generated JSON uses a flat token structure compatible with Figma Tokens Studio. "
                        "Copy the contents or save as a `.json` file.*",
                        elem_classes=["section-desc"])
            export_output = gr.Code(label="Tokens JSON", language="json", lines=25)
            
            export_stage1_btn.click(export_stage1_json, outputs=[export_output])
            export_final_btn.click(export_tokens_json, outputs=[export_output])
        
        # =================================================================
        # EVENT HANDLERS
        # =================================================================
        
        # Store data for viewport toggle
        desktop_data = gr.State({})
        mobile_data = gr.State({})
        
        # Discover pages
        discover_btn.click(
            fn=discover_pages,
            inputs=[url_input],
            outputs=[discover_status, log_output, pages_table],
        ).then(
            fn=lambda: (gr.update(visible=True), gr.update(visible=True)),
            outputs=[pages_table, extract_btn],
        )
        
        # Extract tokens
        extract_btn.click(
            fn=extract_tokens,
            inputs=[pages_table],
            outputs=[extraction_status, log_output, desktop_data, mobile_data, 
                     stage1_typography_preview, stage1_colors_preview,
                     stage1_semantic_preview,
                     stage1_spacing_preview, stage1_radius_preview, stage1_shadows_preview],
        ).then(
            fn=lambda d: (d.get("colors", []), d.get("typography", []), d.get("spacing", []), d.get("radius", [])),
            inputs=[desktop_data],
            outputs=[colors_table, typography_table, spacing_table, radius_table],
        ).then(
            fn=lambda: gr.update(open=True),
            outputs=[stage1_accordion],
        )
        
        # Viewport toggle
        viewport_toggle.change(
            fn=switch_viewport,
            inputs=[viewport_toggle],
            outputs=[colors_table, typography_table, spacing_table, radius_table],
        )
        
        # Stage 2: NEW Architecture Analyze
        analyze_btn_v2.click(
            fn=run_stage2_analysis_v2,
            inputs=[benchmark_checkboxes],
            outputs=[
                stage2_status,
                stage2_log,
                benchmark_comparison_md,
                scores_dashboard,
                priority_actions_html,
                color_recommendations_table,
                typography_desktop,
                typography_mobile,
                stage2_typography_preview,
                stage2_color_ramps_preview,
                llm_color_recommendations,
                spacing_comparison,
                base_colors_display,
                color_ramps_display,
                radius_display,
                shadows_display,
            ],
        )
        
        # Stage 2: Legacy Analyze (keep for backward compatibility)
        analyze_btn_legacy.click(
            fn=run_stage2_analysis,
            inputs=[],
            outputs=[stage2_status, stage2_log, benchmark_comparison_md, scores_dashboard,
                     typography_desktop, typography_mobile, spacing_comparison, 
                     base_colors_display, color_ramps_display, radius_display, shadows_display,
                     stage2_typography_preview, stage2_color_ramps_preview,
                     llm_color_recommendations, color_recommendations_table],
        )
        
        # Stage 2: Apply upgrades
        apply_upgrades_btn.click(
            fn=apply_selected_upgrades,
            inputs=[type_scale_radio, spacing_radio, color_ramps_checkbox, color_recommendations_table],
            outputs=[apply_status, stage2_log],
        ).then(
            fn=lambda: gr.update(open=True),
            outputs=[stage3_accordion],
        )

        # Stage 2: Reset to original
        reset_btn.click(
            fn=reset_to_original,
            outputs=[type_scale_radio, spacing_radio, color_ramps_checkbox, apply_status, stage2_log],
        )
        
        # Stage 1: Download JSON
        download_stage1_btn.click(
            fn=export_stage1_json,
            outputs=[export_output],
        )
        
        # Proceed to Stage 2 button
        proceed_stage2_btn.click(
            fn=lambda: gr.update(open=True),
            outputs=[stage2_accordion],
        )
        
        # =================================================================
        # FOOTER
        # =================================================================
        
        gr.Markdown("""
        ---
        **Design System Extractor v2** | Built with Playwright + Firecrawl + LangGraph + HuggingFace
        
        *A semi-automated co-pilot for design system recovery and modernization.*
        
        **New Architecture:** Rule Engine (FREE) + Benchmark Research (Firecrawl) + LLM Agents
        """)
    
    return app


# =============================================================================
# MAIN
# =============================================================================

if __name__ == "__main__":
    app = create_ui()
    app.launch(server_name="0.0.0.0", server_port=7860)