Delete app.py

app.py

@@ -1,1725 +0,0 @@
-"""
-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()
-
-
-# =============================================================================
-# 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 "❌ Please enter a valid URL", "", 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 = f"✅ Found {len(pages)} pages. Review and click 'Extract Tokens' to continue."
-        
-        return status, state.get_logs(), pages_data
-        
-    except Exception as e:
-        import traceback
-        state.log(f"❌ Error: {str(e)}")
-        return f"❌ Error: {str(e)}", 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 "❌ Please 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:
-                    # 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 "❌ No pages selected. Please select pages or rediscover.", 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("🖥️ DESKTOP EXTRACTION (1440px)")
-        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)
-        
-        state.log(f"   Raw: {len(state.desktop_raw.colors)} colors, {len(state.desktop_raw.typography)} typography, {len(state.desktop_raw.spacing)} spacing")
-        
-        # Normalize desktop
-        state.log("   Normalizing...")
-        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("📱 MOBILE EXTRACTION (375px)")
-        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)
-        
-        state.log(f"   Raw: {len(state.mobile_raw.colors)} colors, {len(state.mobile_raw.typography)} typography, {len(state.mobile_raw.spacing)} spacing")
-        
-        # Normalize mobile
-        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")
-        
-        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)
-        
-        state.log("")
-        state.log("=" * 50)
-        state.log("✅ EXTRACTION COMPLETE!")
-        state.log("=" * 50)
-        
-        progress(1.0, desc="✅ Complete!")
-        
-        status = f"""## ✅ Extraction Complete!
-
-| Viewport | Colors | Typography | Spacing |
-|----------|--------|------------|---------|
-| Desktop | {len(state.desktop_normalized.colors)} | {len(state.desktop_normalized.typography)} | {len(state.desktop_normalized.spacing)} |
-| Mobile | {len(state.mobile_normalized.colors)} | {len(state.mobile_normalized.typography)} | {len(state.mobile_normalized.spacing)} |
-
-**Next:** Review the tokens below. Accept or reject, then proceed to Stage 2.
-"""
-        
-        return status, state.get_logs(), desktop_data, mobile_data
-        
-    except Exception as e:
-        import traceback
-        state.log(f"❌ Error: {str(e)}")
-        state.log(traceback.format_exc())
-        return f"❌ Error: {str(e)}", 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",
-        ])
-    
-    return {
-        "colors": colors,
-        "typography": typography,
-        "spacing": spacing,
-    }
-
-
-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"]
-
-
-# =============================================================================
-# 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
-        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,
-        )
-        
-        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()
-        
-        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)
-        
-    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
-
-
-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:
-                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:
-            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_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:
-            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:
-            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 apply_selected_upgrades(type_choice: str, spacing_choice: str, apply_ramps: bool):
-    """Apply selected upgrade options."""
-    if not state.upgrade_recommendations:
-        return "❌ Run analysis first", ""
-    
-    state.log("✨ Applying selected upgrades...")
-    
-    # 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'}")
-    
-    state.log("✅ Upgrades applied! Proceed to Stage 3 for export.")
-    
-    return "✅ Upgrades applied! Proceed to Stage 3 to export.", state.get_logs()
-
-
-def export_stage1_json():
-    """Export Stage 1 tokens (as-is extraction) to JSON."""
-    if not state.desktop_normalized:
-        return json.dumps({"error": "No tokens extracted. Please run extraction first."}, indent=2)
-    
-    result = {
-        "metadata": {
-            "source_url": state.base_url,
-            "extracted_at": datetime.now().isoformat(),
-            "version": "v1-stage1-as-is",
-            "stage": "extraction",
-            "description": "Raw extracted tokens before upgrades",
-        },
-        "fonts": {},  # Detected font families
-        "colors": {},  # Viewport-agnostic
-        "typography": {
-            "desktop": {},
-            "mobile": {},
-        },
-        "spacing": {
-            "desktop": {},
-            "mobile": {},
-        },
-        "radius": {},  # Viewport-agnostic
-        "shadows": {},  # Viewport-agnostic
-    }
-    
-    # Font families detected
-    fonts_info = get_detected_fonts()
-    result["fonts"] = {
-        "primary": fonts_info.get("primary", "Unknown"),
-        "weights": fonts_info.get("weights", [400]),
-        "all_fonts": fonts_info.get("all_fonts", {}),
-    }
-    
-    # Colors (no viewport prefix - same across devices)
-    if state.desktop_normalized and state.desktop_normalized.colors:
-        for name, c in state.desktop_normalized.colors.items():
-            key = c.suggested_name or c.value
-            result["colors"][key] = {
-                "value": c.value,
-                "frequency": c.frequency,
-                "confidence": c.confidence.value if c.confidence else "medium",
-                "contrast_white": round(c.contrast_white, 2) if c.contrast_white else None,
-                "contrast_black": round(c.contrast_black, 2) if c.contrast_black else None,
-                "contexts": c.contexts[:3] if c.contexts else [],
-            }
-    
-    # Typography Desktop
-    if state.desktop_normalized and state.desktop_normalized.typography:
-        for name, t in state.desktop_normalized.typography.items():
-            key = t.suggested_name or f"{t.font_family}-{t.font_size}"
-            result["typography"]["desktop"][key] = {
-                "font_family": t.font_family,
-                "font_size": t.font_size,
-                "font_weight": t.font_weight,
-                "line_height": t.line_height,
-                "frequency": t.frequency,
-            }
-    
-    # Typography Mobile
-    if state.mobile_normalized and state.mobile_normalized.typography:
-        for name, t in state.mobile_normalized.typography.items():
-            key = t.suggested_name or f"{t.font_family}-{t.font_size}"
-            result["typography"]["mobile"][key] = {
-                "font_family": t.font_family,
-                "font_size": t.font_size,
-                "font_weight": t.font_weight,
-                "line_height": t.line_height,
-                "frequency": t.frequency,
-            }
-    
-    # Spacing Desktop
-    if state.desktop_normalized and state.desktop_normalized.spacing:
-        for name, s in state.desktop_normalized.spacing.items():
-            key = s.suggested_name or s.value
-            result["spacing"]["desktop"][key] = {
-                "value": s.value,
-                "value_px": s.value_px,
-                "fits_base_8": s.fits_base_8,
-                "frequency": s.frequency,
-            }
-    
-    # Spacing Mobile
-    if state.mobile_normalized and state.mobile_normalized.spacing:
-        for name, s in state.mobile_normalized.spacing.items():
-            key = s.suggested_name or s.value
-            result["spacing"]["mobile"][key] = {
-                "value": s.value,
-                "value_px": s.value_px,
-                "fits_base_8": s.fits_base_8,
-                "frequency": s.frequency,
-            }
-    
-    # Radius (no viewport prefix)
-    if state.desktop_normalized and state.desktop_normalized.radius:
-        for name, r in state.desktop_normalized.radius.items():
-            result["radius"][name] = {
-                "value": r.value,
-                "frequency": r.frequency,
-            }
-    
-    # Shadows (no viewport prefix)
-    if state.desktop_normalized and state.desktop_normalized.shadows:
-        for name, s in state.desktop_normalized.shadows.items():
-            result["shadows"][name] = {
-                "value": s.value,
-                "frequency": s.frequency,
-            }
-    
-    return json.dumps(result, indent=2, default=str)
-
-
-def export_tokens_json():
-    """Export final tokens with selected upgrades applied."""
-    if not state.desktop_normalized:
-        return json.dumps({"error": "No tokens extracted. Please run extraction first."}, indent=2)
-    
-    # Get selected upgrades
-    upgrades = getattr(state, 'selected_upgrades', {})
-    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
-    
-    result = {
-        "metadata": {
-            "source_url": state.base_url,
-            "extracted_at": datetime.now().isoformat(),
-            "version": "v2-upgraded",
-            "stage": "final",
-            "upgrades_applied": {
-                "type_scale": type_scale_choice,
-                "spacing": spacing_choice,
-                "color_ramps": apply_ramps,
-            },
-        },
-        "fonts": {},
-        "colors": {},
-        "typography": {
-            "desktop": {},
-            "mobile": {},
-        },
-        "spacing": {
-            "desktop": {},
-            "mobile": {},
-        },
-        "radius": {},
-        "shadows": {},
-    }
-    
-    # Font families
-    fonts_info = get_detected_fonts()
-    result["fonts"] = {
-        "primary": fonts_info.get("primary", "Unknown"),
-        "weights": fonts_info.get("weights", [400]),
-    }
-    
-    # Colors with optional ramps
-    if state.desktop_normalized and state.desktop_normalized.colors:
-        from core.color_utils import generate_color_ramp
-        
-        for name, c in state.desktop_normalized.colors.items():
-            base_key = c.suggested_name or c.value
-            
-            if apply_ramps:
-                # Generate full ramp
-                try:
-                    ramp = generate_color_ramp(c.value)
-                    shades = ["50", "100", "200", "300", "400", "500", "600", "700", "800", "900"]
-                    for i, shade in enumerate(shades):
-                        if i < len(ramp):
-                            result["colors"][f"{base_key}.{shade}"] = {
-                                "value": ramp[i],
-                                "source": "upgraded" if shade != "500" else "detected",
-                            }
-                except:
-                    result["colors"][base_key] = {"value": c.value, "source": "detected"}
-            else:
-                result["colors"][base_key] = {"value": c.value, "source": "detected"}
-    
-    # Typography with optional type scale
-    base_size = get_base_font_size()
-    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"
-    ]
-    
-    # Desktop typography
-    if state.desktop_normalized and state.desktop_normalized.typography:
-        if ratio:
-            # Apply type scale
-            scales = [int(round(base_size * (ratio ** (8-i)) / 2) * 2) for i in range(13)]
-            for i, token_name in enumerate(token_names):
-                result["typography"]["desktop"][token_name] = {
-                    "font_family": fonts_info.get("primary", "sans-serif"),
-                    "font_size": f"{scales[i]}px",
-                    "source": "upgraded",
-                }
-        else:
-            # Keep original
-            for name, t in state.desktop_normalized.typography.items():
-                key = t.suggested_name or f"{t.font_family}-{t.font_size}"
-                result["typography"]["desktop"][key] = {
-                    "font_family": t.font_family,
-                    "font_size": t.font_size,
-                    "font_weight": t.font_weight,
-                    "line_height": t.line_height,
-                    "source": "detected",
-                }
-    
-    # Mobile typography
-    if state.mobile_normalized and state.mobile_normalized.typography:
-        if ratio:
-            # Apply type scale with mobile factor
-            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):
-                result["typography"]["mobile"][token_name] = {
-                    "font_family": fonts_info.get("primary", "sans-serif"),
-                    "font_size": f"{scales[i]}px",
-                    "source": "upgraded",
-                }
-        else:
-            for name, t in state.mobile_normalized.typography.items():
-                key = t.suggested_name or f"{t.font_family}-{t.font_size}"
-                result["typography"]["mobile"][key] = {
-                    "font_family": t.font_family,
-                    "font_size": t.font_size,
-                    "font_weight": t.font_weight,
-                    "line_height": t.line_height,
-                    "source": "detected",
-                }
-    
-    # Spacing with optional grid alignment
-    spacing_tokens = ["space.1", "space.2", "space.3", "space.4", "space.5", 
-                      "space.6", "space.8", "space.10", "space.12", "space.16"]
-    
-    if state.desktop_normalized and state.desktop_normalized.spacing:
-        if spacing_base:
-            # Generate grid-aligned spacing
-            for i, token_name in enumerate(spacing_tokens):
-                value = spacing_base * (i + 1)
-                result["spacing"]["desktop"][token_name] = {
-                    "value": f"{value}px",
-                    "value_px": value,
-                    "source": "upgraded",
-                }
-        else:
-            for name, s in state.desktop_normalized.spacing.items():
-                key = s.suggested_name or s.value
-                result["spacing"]["desktop"][key] = {
-                    "value": s.value,
-                    "value_px": s.value_px,
-                    "source": "detected",
-                }
-    
-    if state.mobile_normalized and state.mobile_normalized.spacing:
-        if spacing_base:
-            for i, token_name in enumerate(spacing_tokens):
-                value = spacing_base * (i + 1)
-                result["spacing"]["mobile"][token_name] = {
-                    "value": f"{value}px",
-                    "value_px": value,
-                    "source": "upgraded",
-                }
-        else:
-            for name, s in state.mobile_normalized.spacing.items():
-                key = s.suggested_name or s.value
-                result["spacing"]["mobile"][key] = {
-                    "value": s.value,
-                    "value_px": s.value_px,
-                    "source": "detected",
-                }
-    
-    # Radius
-    if state.desktop_normalized and state.desktop_normalized.radius:
-        for name, r in state.desktop_normalized.radius.items():
-            result["radius"][name] = {
-                "value": r.value,
-                "source": "detected",
-            }
-    
-    # Shadows
-    if state.desktop_normalized and state.desktop_normalized.shadows:
-        for name, s in state.desktop_normalized.shadows.items():
-            result["shadows"][name] = {
-                "value": s.value,
-                "source": "detected",
-            }
-    
-    return json.dumps(result, indent=2, default=str)
-
-
-# =============================================================================
-# UI BUILDING
-# =============================================================================
-
-def create_ui():
-    """Create the Gradio interface."""
-    
-    with gr.Blocks(
-        title="Design System Extractor v2",
-        theme=gr.themes.Soft(),
-        css="""
-        .color-swatch { display: inline-block; width: 24px; height: 24px; border-radius: 4px; margin-right: 8px; vertical-align: middle; }
-        """
-    ) as app:
-        
-        gr.Markdown("""
-        # 🎨 Design System Extractor v2
-        
-        **Reverse-engineer design systems from live websites.**
-        
-        A semi-automated, human-in-the-loop system that extracts, normalizes, and upgrades design tokens.
-        
-        ---
-        """)
-        
-        # =================================================================
-        # CONFIGURATION
-        # =================================================================
-        
-        with gr.Accordion("⚙️ Configuration", open=not bool(HF_TOKEN_FROM_ENV)):
-            gr.Markdown("**HuggingFace Token** — Required for Stage 2 (AI upgrades)")
-            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!"
-                return "❌ Invalid token"
-            
-            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 your website URL to discover pages for extraction.")
-            
-            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)
-            
-            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,
-            )
-            
-            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 extracted tokens.** Toggle between Desktop and Mobile viewports.
-            Accept or reject tokens, then proceed to Stage 2 for AI-powered upgrades.
-            """)
-            
-            viewport_toggle = gr.Radio(
-                choices=["Desktop (1440px)", "Mobile (375px)"],
-                value="Desktop (1440px)",
-                label="Viewport",
-            )
-            
-            with gr.Tabs():
-                with gr.Tab("🎨 Colors"):
-                    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.Tab("📝 Typography"):
-                    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.Tab("📏 Spacing"):
-                    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.Tab("🔘 Radius"):
-                    radius_table = gr.Dataframe(
-                        headers=["Accept", "Value", "Frequency", "Context"],
-                        datatype=["bool", "str", "number", "str"],
-                        label="Border Radius",
-                        interactive=True,
-                    )
-            
-            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 Upgrades", open=False) as stage2_accordion:
-            
-            stage2_status = gr.Markdown("Click 'Analyze' to start AI-powered design system analysis.")
-            
-            # =============================================================
-            # LLM CONFIGURATION & COMPETITORS
-            # =============================================================
-            with gr.Accordion("⚙️ Analysis Configuration", open=False):
-                gr.Markdown("""
-                ### 🤖 LLM Models Used
-                
-                | Role | Model | Expertise |
-                |------|-------|-----------|
-                | **Typography Analyst** | meta-llama/Llama-3.1-70B | Type scale patterns, readability |
-                | **Color Analyst** | meta-llama/Llama-3.1-70B | Color theory, accessibility |
-                | **Spacing Analyst** | Rule-based | Grid alignment, consistency |
-                
-                *Analysis compares your design against industry leaders.*
-                """)
-                
-                gr.Markdown("### 🎯 Competitor Design Systems")
-                gr.Markdown("Enter design systems to compare against (comma-separated):")
-                competitors_input = gr.Textbox(
-                    value="Material Design 3, Apple HIG, Shopify Polaris, IBM Carbon, Atlassian",
-                    label="Competitors",
-                    placeholder="Material Design 3, Apple HIG, Shopify Polaris...",
-                )
-                gr.Markdown("*Suggestions: Ant Design, Chakra UI, Tailwind, Bootstrap, Salesforce Lightning*")
-            
-            analyze_btn = gr.Button("🤖 Analyze Design System", variant="primary", size="lg")
-            
-            with gr.Accordion("📋 AI Analysis Log", open=True):
-                stage2_log = gr.Textbox(label="Log", lines=18, interactive=False)
-            
-            # =============================================================
-            # BRAND COMPARISON (LLM Research)
-            # =============================================================
-            gr.Markdown("---")
-            brand_comparison = gr.Markdown("*Brand comparison will appear after analysis*")
-            
-            # =============================================================
-            # FONT FAMILIES DETECTED
-            # =============================================================
-            gr.Markdown("---")
-            gr.Markdown("## 🔤 Font Families Detected")
-            font_families_display = gr.Markdown("*Font information will appear after analysis*")
-            
-            # =============================================================
-            # TYPOGRAPHY SECTION - Desktop & Mobile
-            # =============================================================
-            gr.Markdown("---")
-            gr.Markdown("## 📐 Typography")
-            
-            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
-            # =============================================================
-            gr.Markdown("---")
-            gr.Markdown("## 🎨 Colors")
-            
-            base_colors_display = gr.Markdown("*Base colors will appear after analysis*")
-            
-            gr.Markdown("---")
-            
-            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)")
-            
-            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)")
-            
-            radius_display = gr.Markdown("*Radius tokens will appear after analysis*")
-            
-            # =============================================================
-            # SHADOWS SECTION
-            # =============================================================
-            gr.Markdown("---")
-            gr.Markdown("## 🌫️ Shadows (Rule-Based)")
-            
-            shadows_display = gr.Markdown("*Shadow tokens will appear after analysis*")
-            
-            # =============================================================
-            # APPLY SECTION
-            # =============================================================
-            gr.Markdown("---")
-            
-            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("")
-        
-        # =================================================================
-        # STAGE 3: EXPORT
-        # =================================================================
-        
-        with gr.Accordion("📦 Stage 3: Export", open=False):
-            gr.Markdown("""
-            Export your design tokens to JSON (compatible with Figma Tokens Studio).
-            
-            - **Stage 1 JSON**: Raw extracted tokens (as-is)
-            - **Final JSON**: Upgraded tokens with selected improvements
-            """)
-            
-            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")
-            
-            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],
-        ).then(
-            fn=lambda d: (d.get("colors", []), d.get("typography", []), d.get("spacing", [])),
-            inputs=[desktop_data],
-            outputs=[colors_table, typography_table, spacing_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],
-        )
-        
-        # Stage 2: Analyze
-        analyze_btn.click(
-            fn=run_stage2_analysis,
-            inputs=[competitors_input],
-            outputs=[stage2_status, stage2_log, brand_comparison, font_families_display,
-                     typography_desktop, typography_mobile, spacing_comparison, 
-                     base_colors_display, color_ramps_display, radius_display, shadows_display],
-        )
-        
-        # Stage 2: Apply upgrades
-        apply_upgrades_btn.click(
-            fn=apply_selected_upgrades,
-            inputs=[type_scale_radio, spacing_radio, color_ramps_checkbox],
-            outputs=[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 + Gradio + LangGraph + HuggingFace
-        
-        *A semi-automated co-pilot for design system recovery and modernization.*
-        """)
-    
-    return app
-
-
-# =============================================================================
-# MAIN
-# =============================================================================
-
-if __name__ == "__main__":
-    app = create_ui()
-    app.launch(server_name="0.0.0.0", server_port=7860)