"""
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.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_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 3: EXPORT
# =============================================================================

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,
                    )
            
            proceed_stage2_btn = gr.Button("➡️ Proceed to Stage 2: AI Upgrades", variant="primary")
        
        # =================================================================
        # STAGE 2: AI UPGRADES (Placeholder)
        # =================================================================
        
        with gr.Accordion("🧠 Stage 2: AI-Powered Upgrades (Coming Soon)", open=False):
            gr.Markdown("""
            **Agent 3 (Design System Advisor)** will analyze your tokens and propose:
            
            - **Type Scale Options:** Choose from A/B/C (1.25, 1.333, 1.414 ratios)
            - **Color Ramp Generation:** AA-compliant tints and shades
            - **Spacing System:** Aligned to 8px base grid
            - **Naming Conventions:** Semantic token names
            
            Each option will show a **live preview** so you can see the changes before accepting.
            
            *Requires HuggingFace token for LLM inference.*
            """)
        
        # =================================================================
        # STAGE 3: EXPORT
        # =================================================================
        
        with gr.Accordion("📦 Stage 3: Export", open=False):
            gr.Markdown("Export your design tokens to JSON (compatible with Figma Tokens Studio).")
            
            export_btn = gr.Button("📥 Export JSON", variant="secondary")
            export_output = gr.Code(label="Tokens JSON", language="json", lines=20)
            
            export_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],
        )
        
        # =================================================================
        # 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)