Delete app.py

app.py

@@ -1,982 +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
-# =============================================================================
-
-async def run_stage2_analysis(progress=gr.Progress()):
-    """Run Agent 3 analysis on extracted tokens."""
-    
-    if not state.desktop_normalized or not state.mobile_normalized:
-        return "❌ Please complete Stage 1 first", "", None, None, None, None
-    
-    state.log("")
-    state.log("=" * 50)
-    state.log("🧠 STAGE 2: AI-POWERED ANALYSIS")
-    state.log("=" * 50)
-    
-    progress(0.1, desc="🤖 Starting AI analysis...")
-    
-    try:
-        advisor_mod = get_advisor()
-        
-        # Run analysis with logging
-        state.log("🔍 Analyzing design patterns...")
-        progress(0.3, desc="🔍 Analyzing patterns...")
-        
-        recommendations = await advisor_mod.analyze_design_system(
-            desktop_tokens=state.desktop_normalized,
-            mobile_tokens=state.mobile_normalized,
-            log_callback=state.log,
-        )
-        
-        state.upgrade_recommendations = recommendations
-        
-        progress(0.9, desc="📊 Preparing recommendations...")
-        
-        # Format for display
-        type_scale_options = format_type_scale_options(recommendations.typography_scales)
-        spacing_options = format_spacing_options(recommendations.spacing_systems)
-        color_ramp_preview = format_color_ramps(recommendations.color_ramps)
-        
-        state.log("✅ Analysis complete!")
-        progress(1.0, desc="✅ Complete!")
-        
-        status = f"""## 🧠 AI Analysis Complete!
-
-### Detected Patterns
-{chr(10).join(['• ' + p for p in recommendations.detected_patterns]) if recommendations.detected_patterns else '• No specific patterns detected'}
-
-### LLM Recommendation
-{recommendations.llm_rationale}
-
-### Options Ready
-- **{len(recommendations.typography_scales)}** type scale options
-- **{len(recommendations.spacing_systems)}** spacing system options
-- **{len(recommendations.color_ramps)}** color ramp options
-
-**Select your preferred options below, then apply upgrades.**
-"""
-        
-        return status, state.get_logs(), type_scale_options, spacing_options, color_ramp_preview, format_as_is_vs_to_be()
-        
-    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, None
-
-
-def format_type_scale_options(options) -> list:
-    """Format type scale options for radio buttons."""
-    if not options:
-        return []
-    
-    formatted = []
-    for opt in options:
-        label = f"{opt.name}"
-        if opt.recommended:
-            label += " ⭐"
-        label += f" — {opt.description}"
-        formatted.append(label)
-    
-    return formatted
-
-
-def format_spacing_options(options) -> list:
-    """Format spacing options for radio buttons."""
-    if not options:
-        return []
-    
-    formatted = []
-    for opt in options:
-        label = f"{opt.name}"
-        if opt.recommended:
-            label += " ⭐"
-        label += f" — {opt.description}"
-        formatted.append(label)
-    
-    return formatted
-
-
-def format_color_ramps(options) -> str:
-    """Format color ramp options for display."""
-    if not options:
-        return "No color ramps generated"
-    
-    lines = []
-    for opt in options:
-        ramp = opt.values.get("ramp", {})
-        base = opt.values.get("base_color", "")
-        role = opt.values.get("role", "unknown")
-        
-        lines.append(f"**{role.title()} Ramp** (from {base})")
-        shades = " → ".join([f"`{ramp.get(f'{role}.{s}', '?')}`" for s in ["50", "200", "500", "700", "900"]])
-        lines.append(shades)
-        lines.append("")
-    
-    return "\n".join(lines)
-
-
-def format_as_is_vs_to_be() -> str:
-    """Format As-Is vs To-Be comparison."""
-    if not state.upgrade_recommendations:
-        return ""
-    
-    rec = state.upgrade_recommendations
-    
-    # Get current values
-    desktop_typo = list(state.desktop_normalized.typography.values()) if state.desktop_normalized else []
-    desktop_spacing = list(state.desktop_normalized.spacing.values()) if state.desktop_normalized else []
-    desktop_colors = list(state.desktop_normalized.colors.values()) if state.desktop_normalized else []
-    
-    # Format current typography
-    current_sizes = sorted(set([t.font_size for t in desktop_typo[:10]]))
-    
-    # Get recommended type scale
-    rec_scale = None
-    for opt in rec.typography_scales:
-        if opt.recommended:
-            rec_scale = opt.values.get("scale", {})
-            break
-    
-    # Format comparison
-    lines = [
-        "## As-Is vs To-Be Comparison",
-        "",
-        "### Typography",
-        "| As-Is (Current) | To-Be (Upgraded) |",
-        "|-----------------|------------------|",
-    ]
-    
-    if rec_scale:
-        for name, size in list(rec_scale.items())[:6]:
-            lines.append(f"| {current_sizes[0] if current_sizes else '?'}px | {size}px ({name}) |")
-            if current_sizes:
-                current_sizes.pop(0)
-    
-    lines.extend([
-        "",
-        "### Spacing",
-        "| As-Is | To-Be |",
-        "|-------|-------|",
-    ])
-    
-    current_spacing = sorted([s.value_px for s in desktop_spacing[:8]])
-    
-    # Get recommended spacing
-    rec_spacing = None
-    for opt in rec.spacing_systems:
-        if opt.recommended:
-            rec_spacing = opt.values.get("scale", {})
-            break
-    
-    if rec_spacing:
-        to_be_spacing = list(rec_spacing.values())[:8]
-        for i, cs in enumerate(current_spacing[:6]):
-            tb = to_be_spacing[i] if i < len(to_be_spacing) else "—"
-            lines.append(f"| {cs}px | {tb} |")
-    
-    lines.extend([
-        "",
-        "### Colors",
-        f"**{len(desktop_colors)} colors detected** → Full ramps will be generated",
-    ])
-    
-    return "\n".join(lines)
-
-
-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."""
-    result = {
-        "metadata": {
-            "source_url": state.base_url,
-            "extracted_at": datetime.now().isoformat(),
-            "version": "v1-stage1-extracted",
-            "stage": "extraction",
-        },
-        "colors": {},  # Viewport-agnostic
-        "typography": {
-            "desktop": {},
-            "mobile": {},
-        },
-        "spacing": {
-            "desktop": {},
-            "mobile": {},
-        },
-        "radius": {},  # Viewport-agnostic
-    }
-    
-    # Colors (no viewport prefix - same across devices)
-    if state.desktop_normalized:
-        for name, c in state.desktop_normalized.colors.items():
-            result["colors"][c.suggested_name or c.value] = {
-                "value": c.value,
-                "frequency": c.frequency,
-                "confidence": c.confidence.value if c.confidence else "medium",
-                "contexts": c.contexts[:3],
-            }
-    
-    # Typography (viewport-specific)
-    if state.desktop_normalized:
-        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,
-            }
-    
-    if state.mobile_normalized:
-        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 (viewport-specific if different)
-    if state.desktop_normalized:
-        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,
-            }
-    
-    if state.mobile_normalized:
-        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:
-        for name, r in state.desktop_normalized.radius.items():
-            result["radius"][name] = {
-                "value": r.value,
-                "frequency": r.frequency,
-            }
-    
-    return json.dumps(result, indent=2, default=str)
-
-
-def export_tokens_json():
-    """Export tokens to JSON."""
-    result = {
-        "metadata": {
-            "source_url": state.base_url,
-            "extracted_at": datetime.now().isoformat(),
-            "version": "v1-extracted",
-        },
-        "desktop": None,
-        "mobile": None,
-    }
-    
-    if state.desktop_normalized:
-        result["desktop"] = {
-            "colors": [
-                {"value": c.value, "name": c.suggested_name, "frequency": c.frequency, 
-                 "confidence": c.confidence.value if c.confidence else "medium"}
-                for c in state.desktop_normalized.colors
-            ],
-            "typography": [
-                {"font_family": t.font_family, "font_size": t.font_size, 
-                 "font_weight": t.font_weight, "line_height": t.line_height,
-                 "name": t.suggested_name, "frequency": t.frequency}
-                for t in state.desktop_normalized.typography
-            ],
-            "spacing": [
-                {"value": s.value, "value_px": s.value_px, "name": s.suggested_name,
-                 "frequency": s.frequency, "fits_base_8": s.fits_base_8}
-                for s in state.desktop_normalized.spacing
-            ],
-        }
-    
-    if state.mobile_normalized:
-        result["mobile"] = {
-            "colors": [
-                {"value": c.value, "name": c.suggested_name, "frequency": c.frequency,
-                 "confidence": c.confidence.value if c.confidence else "medium"}
-                for c in state.mobile_normalized.colors
-            ],
-            "typography": [
-                {"font_family": t.font_family, "font_size": t.font_size,
-                 "font_weight": t.font_weight, "line_height": t.line_height,
-                 "name": t.suggested_name, "frequency": t.frequency}
-                for t in state.mobile_normalized.typography
-            ],
-            "spacing": [
-                {"value": s.value, "value_px": s.value_px, "name": s.suggested_name,
-                 "frequency": s.frequency, "fits_base_8": s.fits_base_8}
-                for s in state.mobile_normalized.spacing
-            ],
-        }
-    
-    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.")
-            
-            analyze_btn = gr.Button("🤖 Analyze Design System", variant="primary")
-            
-            with gr.Row():
-                stage2_log = gr.Textbox(label="📋 AI Analysis Log", lines=10, interactive=False)
-            
-            gr.Markdown("---")
-            
-            with gr.Row():
-                # Left column: Options
-                with gr.Column(scale=1):
-                    gr.Markdown("### 📐 Type Scale")
-                    type_scale_radio = gr.Radio(
-                        choices=[],
-                        label="Select type scale ratio",
-                        interactive=True,
-                    )
-                    
-                    gr.Markdown("### 📏 Spacing System")
-                    spacing_radio = gr.Radio(
-                        choices=[],
-                        label="Select spacing base",
-                        interactive=True,
-                    )
-                    
-                    gr.Markdown("### 🎨 Color Ramps")
-                    color_ramps_checkbox = gr.Checkbox(
-                        label="Generate color ramps (50-900 shades)",
-                        value=True,
-                    )
-                    color_ramps_preview = gr.Markdown("")
-                
-                # Right column: As-Is vs To-Be
-                with gr.Column(scale=1):
-                    gr.Markdown("### 🔄 As-Is vs To-Be Preview")
-                    comparison_display = gr.Markdown("")
-            
-            gr.Markdown("---")
-            
-            with gr.Row():
-                apply_upgrades_btn = gr.Button("✨ Apply Selected Upgrades", variant="primary")
-                reset_btn = gr.Button("↩️ Reset to Original", variant="secondary")
-            
-            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,
-            outputs=[stage2_status, stage2_log, type_scale_radio, spacing_radio, color_ramps_preview, comparison_display],
-        ).then(
-            fn=lambda opts: gr.update(choices=opts) if opts else gr.update(),
-            inputs=[type_scale_radio],
-            outputs=[type_scale_radio],
-        )
-        
-        # 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],
-        )
-        
-        # 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)