Delete app.py

app.py

@@ -1,479 +0,0 @@
-"""
-Design System Extractor v2 — Main Application
-==============================================
-
-A semi-automated, human-in-the-loop agentic system that reverse-engineers
-design systems from live websites.
-
-Usage:
-    python app.py
-"""
-
-import os
-import asyncio
-import gradio as gr
-from datetime import datetime
-
-# Get HF token from environment if available
-HF_TOKEN_FROM_ENV = os.getenv("HF_TOKEN", "")
-
-# =============================================================================
-# GLOBAL STATE
-# =============================================================================
-
-current_extraction: dict = {}
-user_hf_token: str = ""
-
-
-# =============================================================================
-# HF TOKEN MANAGEMENT
-# =============================================================================
-
-def set_hf_token(token: str) -> str:
-    """Set the HF token globally."""
-    global user_hf_token
-    
-    if not token or len(token) < 10:
-        return "❌ Please enter a valid HuggingFace token"
-    
-    user_hf_token = token.strip()
-    os.environ["HF_TOKEN"] = user_hf_token
-    
-    return "✅ Token saved! You can now use the extractor."
-
-
-# =============================================================================
-# LAZY IMPORTS (avoid circular imports at startup)
-# =============================================================================
-
-_crawler_module = None
-_extractor_module = None
-_schema_module = None
-
-def get_crawler():
-    global _crawler_module
-    if _crawler_module is None:
-        from agents import crawler as _crawler_module
-    return _crawler_module
-
-def get_extractor():
-    global _extractor_module
-    if _extractor_module is None:
-        from agents import extractor as _extractor_module
-    return _extractor_module
-
-def get_schema():
-    global _schema_module
-    if _schema_module is None:
-        from core import token_schema as _schema_module
-    return _schema_module
-
-
-# =============================================================================
-# STAGE 1: URL INPUT & PAGE DISCOVERY
-# =============================================================================
-
-async def discover_site_pages(url: str, progress=gr.Progress()) -> tuple:
-    """
-    Discover pages from a website URL.
-    
-    Returns tuple of (status_message, pages_dataframe, pages_json)
-    """
-    if not url or not url.startswith(("http://", "https://")):
-        return "❌ Please enter a valid URL starting with http:// or https://", None, None
-    
-    progress(0, desc="🚀 Initializing browser...")
-    
-    try:
-        crawler = get_crawler()
-        discoverer = crawler.PageDiscoverer()
-        
-        def update_progress(p):
-            progress(p, desc=f"🔍 Discovering pages... ({int(p*100)}%)")
-        
-        pages = await discoverer.discover(url, progress_callback=update_progress)
-        
-        progress(1.0, desc="✅ Discovery complete!")
-        
-        # Format for display - ensure we return simple values, not objects
-        pages_data = []
-        for page in pages:
-            pages_data.append([
-                page.selected,                              # Select (bool)
-                page.url,                                   # URL (str)
-                page.title if page.title else "(No title)", # Title (str)
-                page.page_type.value,                       # Type (str)
-                "✓" if not page.error else f"⚠ {page.error}" # Status (str)
-            ])
-        
-        # Store for later use
-        current_extraction["discovered_pages"] = pages
-        current_extraction["base_url"] = url
-        
-        status = f"✅ Found {len(pages)} pages. Select the pages you want to extract tokens from."
-        
-        return status, pages_data
-        
-    except Exception as e:
-        import traceback
-        return f"❌ Error: {str(e)}\n\n{traceback.format_exc()}", None
-
-
-async def start_extraction(pages_selection, viewport_choice: str, progress=gr.Progress()) -> tuple:
-    """
-    Start token extraction from selected pages.
-    
-    Returns tuple of (status, colors_data, typography_data, spacing_data)
-    """
-    if pages_selection is None or len(pages_selection) == 0:
-        return "❌ Please discover pages first", None, None, None
-    
-    progress(0, desc="🔄 Preparing extraction...")
-    
-    # Get selected URLs from the dataframe
-    selected_urls = []
-    
-    # Handle both list of lists and list of dicts formats
-    for row in pages_selection:
-        if isinstance(row, (list, tuple)):
-            # Format: [Select, URL, Title, Type, Status]
-            if len(row) >= 2 and row[0]:  # row[0] is Select checkbox
-                selected_urls.append(row[1])  # row[1] is URL
-        elif isinstance(row, dict):
-            if row.get("Select", False):
-                selected_urls.append(row.get("URL", ""))
-    
-    if not selected_urls:
-        return "❌ Please select at least one page using the checkboxes", None, None, None
-    
-    progress(0.05, desc=f"📋 Selected {len(selected_urls)} pages for extraction...")
-    
-    # Determine viewport
-    schema = get_schema()
-    viewport = schema.Viewport.DESKTOP if viewport_choice == "Desktop (1440px)" else schema.Viewport.MOBILE
-    
-    try:
-        extractor_mod = get_extractor()
-        extractor = extractor_mod.TokenExtractor(viewport=viewport)
-        
-        def update_progress(p):
-            # Scale progress from 0.1 to 0.9
-            scaled = 0.1 + (p * 0.8)
-            progress(scaled, desc=f"🔬 Extracting tokens... ({int(p*100)}%)")
-        
-        progress(0.1, desc=f"🌐 Starting {viewport.value} extraction...")
-        
-        result = await extractor.extract(selected_urls, progress_callback=update_progress)
-        
-        progress(0.9, desc="📊 Processing results...")
-        
-        # Store result
-        current_extraction[f"{viewport.value}_tokens"] = result
-        
-        # Format colors for display - use list of lists for Gradio
-        colors_data = []
-        for color in sorted(result.colors, key=lambda c: -c.frequency)[:50]:
-            colors_data.append([
-                True,                                           # Accept
-                color.value,                                    # Color
-                color.frequency,                                # Frequency
-                ", ".join(color.contexts[:3]) if color.contexts else "",  # Context
-                f"{color.contrast_white:.1f}:1",               # Contrast
-                "✓" if color.wcag_aa_small_text else "✗",      # AA Text
-                color.confidence.value if color.confidence else "low"  # Confidence
-            ])
-        
-        progress(0.93, desc="📝 Processing typography...")
-        
-        # Format typography for display
-        typography_data = []
-        for typo in sorted(result.typography, key=lambda t: -t.frequency)[:30]:
-            typography_data.append([
-                True,                                           # Accept
-                typo.font_family,                               # Font
-                typo.font_size,                                 # Size
-                typo.font_weight,                               # Weight
-                typo.line_height if typo.line_height else "",   # Line Height
-                ", ".join(typo.elements[:3]) if typo.elements else "",  # Elements
-                typo.frequency                                  # Frequency
-            ])
-        
-        progress(0.96, desc="📏 Processing spacing...")
-        
-        # Format spacing for display
-        spacing_data = []
-        for space in sorted(result.spacing, key=lambda s: s.value_px)[:20]:
-            spacing_data.append([
-                True,                                           # Accept
-                space.value,                                    # Value
-                space.frequency,                                # Frequency
-                ", ".join(space.contexts[:2]) if space.contexts else "",  # Context
-                "✓" if space.fits_base_8 else "",              # Fits 8px
-                "⚠" if space.is_outlier else ""                # Outlier
-            ])
-        
-        progress(1.0, desc="✅ Extraction complete!")
-        
-        # Summary
-        status = f"""✅ **Extraction Complete** ({viewport.value})
-
-### 📊 Summary
-| Metric | Value |
-|--------|-------|
-| Pages crawled | {len(result.pages_crawled)} |
-| Colors found | {len(result.colors)} |
-| Typography styles | {len(result.typography)} |
-| Spacing values | {len(result.spacing)} |
-| Font families | {len(result.font_families)} |
-| Spacing base | {result.spacing_base or 'Unknown'}px |
-| Duration | {result.extraction_duration_ms}ms |
-"""
-        
-        if result.warnings:
-            status += f"\n⚠️ **Warnings:** {len(result.warnings)}"
-        if result.errors:
-            status += f"\n❌ **Errors:** {len(result.errors)}"
-            for err in result.errors[:3]:
-                status += f"\n- {err}"
-        
-        return status, colors_data, typography_data, spacing_data
-        
-    except Exception as e:
-        import traceback
-        return f"❌ Extraction failed: {str(e)}\n\n```\n{traceback.format_exc()}\n```", None, None, None
-
-
-def export_tokens_json():
-    """Export current tokens to JSON."""
-    import json
-    
-    result = {}
-    
-    if "desktop_tokens" in current_extraction:
-        desktop = current_extraction["desktop_tokens"]
-        result["desktop"] = {
-            "colors": [c.model_dump() for c in desktop.colors],
-            "typography": [t.model_dump() for t in desktop.typography],
-            "spacing": [s.model_dump() for s in desktop.spacing],
-            "metadata": desktop.summary(),
-        }
-    
-    if "mobile_tokens" in current_extraction:
-        mobile = current_extraction["mobile_tokens"]
-        result["mobile"] = {
-            "colors": [c.model_dump() for c in mobile.colors],
-            "typography": [t.model_dump() for t in mobile.typography],
-            "spacing": [s.model_dump() for s in mobile.spacing],
-            "metadata": mobile.summary(),
-        }
-    
-    if not result:
-        return '{"error": "No tokens extracted yet. Please run extraction first."}'
-    
-    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(),
-    ) as app:
-        
-        # Header
-        gr.Markdown("""
-        # 🎨 Design System Extractor v2
-        
-        **Reverse-engineer design systems from live websites.**
-        
-        Extract colors, typography, and spacing tokens from any website and export to Figma-compatible JSON.
-        
-        ---
-        """)
-        
-        # =================================================================
-        # CONFIGURATION SECTION
-        # =================================================================
-        
-        with gr.Accordion("⚙️ Configuration", open=not bool(HF_TOKEN_FROM_ENV)):
-            
-            gr.Markdown("""
-            **HuggingFace Token** is required for AI-powered features (Agent 2-4).
-            Get your token at: [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
-            
-            *Note: Basic extraction (Agent 1) works without a token.*
-            """)
-            
-            with gr.Row():
-                hf_token_input = gr.Textbox(
-                    label="HuggingFace Token",
-                    placeholder="hf_xxxxxxxxxxxxxxxxxxxx",
-                    type="password",
-                    scale=4,
-                    value=HF_TOKEN_FROM_ENV if HF_TOKEN_FROM_ENV else "",
-                )
-                save_token_btn = gr.Button("💾 Save Token", scale=1)
-            
-            token_status = gr.Markdown(
-                "✅ Token loaded from environment" if HF_TOKEN_FROM_ENV else "⏳ Enter your HF token to enable all features"
-            )
-            
-            save_token_btn.click(
-                fn=set_hf_token,
-                inputs=[hf_token_input],
-                outputs=[token_status],
-            )
-        
-        # =================================================================
-        # STAGE 1: URL Input & Discovery
-        # =================================================================
-        
-        with gr.Accordion("📍 Stage 1: Website Discovery", open=True):
-            
-            gr.Markdown("""
-            **Step 1:** Enter your website URL and discover pages.
-            The system will automatically find and classify 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)
-            
-            discovery_status = gr.Markdown("")
-            
-            pages_table = gr.Dataframe(
-                headers=["Select", "URL", "Title", "Type", "Status"],
-                datatype=["bool", "str", "str", "str", "str"],
-                interactive=True,
-                label="Discovered Pages",
-                visible=False,
-                col_count=(5, "fixed"),
-            )
-        
-        # =================================================================
-        # STAGE 2: Extraction
-        # =================================================================
-        
-        with gr.Accordion("🔬 Stage 2: Token Extraction", open=False):
-            
-            gr.Markdown("""
-            **Step 2:** Select pages and viewport, then extract design tokens.
-            """)
-            
-            with gr.Row():
-                viewport_radio = gr.Radio(
-                    choices=["Desktop (1440px)", "Mobile (375px)"],
-                    value="Desktop (1440px)",
-                    label="Viewport",
-                )
-                extract_btn = gr.Button("🚀 Extract Tokens", variant="primary")
-            
-            extraction_status = gr.Markdown("")
-            
-            with gr.Tabs():
-                with gr.Tab("🎨 Colors"):
-                    colors_table = gr.Dataframe(
-                        headers=["Accept", "Color", "Frequency", "Context", "Contrast (White)", "AA Text", "Confidence"],
-                        datatype=["bool", "str", "number", "str", "str", "str", "str"],
-                        interactive=True,
-                        label="Extracted Colors",
-                    )
-                
-                with gr.Tab("📝 Typography"):
-                    typography_table = gr.Dataframe(
-                        headers=["Accept", "Font", "Size", "Weight", "Line Height", "Elements", "Frequency"],
-                        datatype=["bool", "str", "str", "number", "str", "str", "number"],
-                        interactive=True,
-                        label="Extracted Typography",
-                    )
-                
-                with gr.Tab("📏 Spacing"):
-                    spacing_table = gr.Dataframe(
-                        headers=["Accept", "Value", "Frequency", "Context", "Fits 8px", "Outlier"],
-                        datatype=["bool", "str", "number", "str", "str", "str"],
-                        interactive=True,
-                        label="Extracted Spacing",
-                    )
-        
-        # =================================================================
-        # STAGE 3: Export
-        # =================================================================
-        
-        with gr.Accordion("📦 Stage 3: Export", open=False):
-            
-            gr.Markdown("""
-            **Step 3:** Review and export your design tokens.
-            """)
-            
-            with gr.Row():
-                export_btn = gr.Button("📥 Export JSON", variant="secondary")
-                
-            export_output = gr.Code(
-                label="Exported Tokens (JSON)",
-                language="json",
-                lines=20,
-            )
-        
-        # =================================================================
-        # EVENT HANDLERS
-        # =================================================================
-        
-        # Discovery
-        discover_btn.click(
-            fn=discover_site_pages,
-            inputs=[url_input],
-            outputs=[discovery_status, pages_table],
-        ).then(
-            fn=lambda: gr.update(visible=True),
-            outputs=[pages_table],
-        )
-        
-        # Extraction
-        extract_btn.click(
-            fn=start_extraction,
-            inputs=[pages_table, viewport_radio],
-            outputs=[extraction_status, colors_table, typography_table, spacing_table],
-        )
-        
-        # Export
-        export_btn.click(
-            fn=export_tokens_json,
-            outputs=[export_output],
-        )
-        
-        # =================================================================
-        # FOOTER
-        # =================================================================
-        
-        gr.Markdown("""
-        ---
-        
-        **Design System Extractor v2** | Built with LangGraph + Gradio + HuggingFace
-        
-        *A semi-automated co-pilot for design system recovery and modernization.*
-        
-        **Models:** Microsoft Phi (Normalizer) • Meta Llama (Advisor) • Mistral Codestral (Generator)
-        """)
-    
-    return app
-
-
-# =============================================================================
-# MAIN
-# =============================================================================
-
-if __name__ == "__main__":
-    app = create_ui()
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-    )