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src/agent/insights_generator.py

@@ -1,62 +1,56 @@
 """
-Insight Generation Module
+Restaurant Insights Generator - EXPANDED VERSION
+Generates role-specific insights for Chef and Manager personas.
 
-Generates actionable, role-specific insights from restaurant review analysis.
-
-Stakeholder-specific outputs:
-- Chef: Food quality, menu items, presentation, taste
-- Manager: Service, operations, staffing, customer experience
+UPDATED: Now uses TOP 20 items/aspects instead of TOP 10 for more comprehensive insights.
 """
 
-from typing import Dict, Any
-from anthropic import Anthropic
 import json
 import re
+from typing import Any, Dict, Optional
 
 
 class InsightsGenerator:
     """
-    Generates role-specific insights from analysis results.
+    Generates actionable insights for different restaurant roles.
+    
+    UPDATED: Expanded to use top 20 menu items and aspects for better recommendations.
     """
     
-    def __init__(self, client: Anthropic, model: str):
+    def __init__(self, client, model: str = "claude-sonnet-4-20250514"):
         """
         Initialize the insights generator.
         
         Args:
             client: Anthropic client instance
-            model: Claude model to use
+            model: Model to use for generation
         """
         self.client = client
         self.model = model
     
     def generate_insights(
-        self,
+        self, 
         analysis_data: Dict[str, Any],
         role: str = 'chef',
-        restaurant_name: str = 'the restaurant'
+        restaurant_name: str = "the restaurant"
     ) -> Dict[str, Any]:
         """
-        Generate role-specific insights.
+        Generate role-specific insights from analysis data.
         
         Args:
-            analysis_data: Results from analysis
-            role: Target role ('chef' or 'manager')
+            analysis_data: Complete analysis including menu and aspects
+            role: Either 'chef' or 'manager'
             restaurant_name: Name of the restaurant
         
         Returns:
-            Dictionary with summary, strengths, concerns, recommendations
+            Dict with summary, strengths, concerns, and recommendations
         """
-        # Build the prompt based on role
-        if role.lower() == 'chef':
-            prompt = self._build_chef_prompt(analysis_data, restaurant_name)
-        elif role.lower() == 'manager':
-            prompt = self._build_manager_prompt(analysis_data, restaurant_name)
-        else:
-            raise ValueError(f"Unknown role: {role}. Must be 'chef' or 'manager'")
-        
-        # Call Claude to generate insights
         try:
+            if role == 'chef':
+                prompt = self._build_chef_prompt(analysis_data, restaurant_name)
+            else:
+                prompt = self._build_manager_prompt(analysis_data, restaurant_name)
+            
             response = self.client.messages.create(
                 model=self.model,
                 max_tokens=2000,
@@ -64,48 +58,52 @@ class InsightsGenerator:
                 messages=[{"role": "user", "content": prompt}]
             )
             
-            # Extract insights
-            insights_text = response.content[0].text
-            
-            # Clean up response
-            insights_text = insights_text.replace('```json', '').replace('```', '').strip()
-            
-            # Remove any trailing commas before closing braces/brackets
-            insights_text = re.sub(r',(\s*[}\]])', r'\1', insights_text)
+            response_text = response.content[0].text.strip()
             
-            # Parse JSON response
-            insights = json.loads(insights_text)
+            # Parse JSON from response
+            insights = self._parse_json_response(response_text)
             
-            # Validate structure
-            if not all(key in insights for key in ['summary', 'strengths', 'concerns', 'recommendations']):
-                print(f"⚠️  Incomplete insights structure, using fallback")
+            if insights:
+                return insights
+            else:
                 return self._get_fallback_insights(role)
-            
-            return insights
-            
-        except json.JSONDecodeError as e:
-            print(f"❌ Failed to parse insights as JSON: {e}")
-            print(f"Raw response: {insights_text[:200]}...")
-            return self._get_fallback_insights(role)
+                
         except Exception as e:
-            print(f"❌ Error generating insights: {e}")
+            print(f"[INSIGHTS] Error generating {role} insights: {e}")
             return self._get_fallback_insights(role)
     
+    def _parse_json_response(self, text: str) -> Optional[Dict]:
+        """Parse JSON from response, handling markdown fences."""
+        # Remove markdown code fences
+        text = re.sub(r'```json\s*', '', text)
+        text = re.sub(r'```\s*', '', text)
+        text = text.strip()
+        
+        try:
+            return json.loads(text)
+        except json.JSONDecodeError:
+            # Try to find JSON object in text
+            match = re.search(r'\{[\s\S]*\}', text)
+            if match:
+                try:
+                    return json.loads(match.group())
+                except:
+                    pass
+            return None
+    
     def _build_chef_prompt(
         self,
         analysis_data: Dict[str, Any],
         restaurant_name: str
     ) -> str:
-        """
-        Build prompt for chef-focused insights.
-        """
-        # Prepare summary of analysis data
-        menu_summary = self._summarize_menu_data(analysis_data)
-        aspect_summary = self._summarize_aspect_data(analysis_data, focus='food')
+        """Build prompt for chef-focused insights - EXPANDED to 20 items."""
+        # Get EXPANDED menu summary (20 items instead of 10)
+        menu_summary = self._summarize_menu_data(analysis_data, max_food=20, max_drinks=10)
+        aspect_summary = self._summarize_aspect_data(analysis_data, focus='food', max_aspects=15)
         
         prompt = f"""You are an expert culinary consultant analyzing customer feedback for {restaurant_name}.
 
-MENU PERFORMANCE:
+MENU PERFORMANCE (Top items by customer mentions):
 {menu_summary}
 
 FOOD-RELATED ASPECTS:
@@ -125,24 +123,62 @@ CRITICAL RULES:
 1. Focus ONLY on food/kitchen topics
 2. Be specific with evidence from reviews
 3. Make recommendations actionable
-4. Output ONLY valid JSON, no other text
+4. Reference specific menu items by name
+5. Output ONLY valid JSON, no other text
 
 OUTPUT FORMAT (JSON):
 {{
-  "summary": "2-3 sentence executive summary",
-  "strengths": ["Specific strength 1", "Specific strength 2", "Specific strength 3"],
-  "concerns": ["Specific concern 1", "Specific concern 2"],
+  "summary": "2-3 sentence executive summary covering overall kitchen performance",
+  "strengths": [
+    "Specific strength 1 with menu item example",
+    "Specific strength 2 with menu item example",
+    "Specific strength 3 with menu item example",
+    "Specific strength 4 with menu item example",
+    "Specific strength 5 with menu item example"
+  ],
+  "concerns": [
+    "Specific concern 1 with evidence",
+    "Specific concern 2 with evidence",
+    "Specific concern 3 with evidence"
+  ],
   "recommendations": [
     {{
       "priority": "high",
       "action": "Specific action to take",
+      "reason": "Why this matters based on review data",
+      "evidence": "Supporting data from reviews"
+    }},
+    {{
+      "priority": "high",
+      "action": "Another high priority action",
+      "reason": "Why this matters",
+      "evidence": "Supporting data"
+    }},
+    {{
+      "priority": "medium",
+      "action": "Medium priority action",
+      "reason": "Why this matters",
+      "evidence": "Supporting data"
+    }},
+    {{
+      "priority": "medium",
+      "action": "Another medium priority action",
+      "reason": "Why this matters",
+      "evidence": "Supporting data"
+    }},
+    {{
+      "priority": "low",
+      "action": "Lower priority improvement",
       "reason": "Why this matters",
       "evidence": "Supporting data"
     }}
   ]
 }}
 
-IMPORTANT: Ensure all JSON is properly formatted with no trailing commas.
+IMPORTANT: 
+- Provide at least 5 strengths and 5 recommendations
+- Reference actual menu items from the data above
+- Ensure all JSON is properly formatted with no trailing commas
 
 Generate chef insights:"""
         
@@ -153,17 +189,21 @@ Generate chef insights:"""
         analysis_data: Dict[str, Any],
         restaurant_name: str
     ) -> str:
-        """
-        Build prompt for manager-focused insights.
-        """
-        # Prepare summary of analysis data
-        aspect_summary = self._summarize_aspect_data(analysis_data, focus='operations')
+        """Build prompt for manager-focused insights - EXPANDED to 20 aspects."""
+        # Get EXPANDED aspect summary (20 aspects instead of 10)
+        aspect_summary = self._summarize_aspect_data(analysis_data, focus='operations', max_aspects=20)
+        
+        # Also include menu overview for context
+        menu_summary = self._summarize_menu_data(analysis_data, max_food=10, max_drinks=5)
         
         prompt = f"""You are an expert restaurant operations consultant analyzing customer feedback for {restaurant_name}.
 
-OPERATIONAL ASPECTS:
+OPERATIONAL ASPECTS (All discovered from reviews):
 {aspect_summary}
 
+MENU OVERVIEW (for context):
+{menu_summary}
+
 YOUR TASK:
 Generate actionable insights specifically for the RESTAURANT MANAGER. Focus on:
 - Service quality and speed
@@ -179,90 +219,162 @@ CRITICAL RULES:
 1. Focus ONLY on operations/service topics
 2. Be specific with evidence from reviews
 3. Make recommendations actionable
-4. Output ONLY valid JSON, no other text
+4. Reference specific aspects by name
+5. Output ONLY valid JSON, no other text
 
 OUTPUT FORMAT (JSON):
 {{
-  "summary": "2-3 sentence executive summary",
-  "strengths": ["Specific strength 1", "Specific strength 2", "Specific strength 3"],
-  "concerns": ["Specific concern 1", "Specific concern 2"],
+  "summary": "2-3 sentence executive summary covering overall operations",
+  "strengths": [
+    "Specific operational strength 1 with evidence",
+    "Specific operational strength 2 with evidence",
+    "Specific operational strength 3 with evidence",
+    "Specific operational strength 4 with evidence",
+    "Specific operational strength 5 with evidence"
+  ],
+  "concerns": [
+    "Specific operational concern 1 with evidence",
+    "Specific operational concern 2 with evidence",
+    "Specific operational concern 3 with evidence"
+  ],
   "recommendations": [
     {{
       "priority": "high",
       "action": "Specific action to take",
+      "reason": "Why this matters based on review data",
+      "evidence": "Supporting data from reviews"
+    }},
+    {{
+      "priority": "high",
+      "action": "Another high priority action",
+      "reason": "Why this matters",
+      "evidence": "Supporting data"
+    }},
+    {{
+      "priority": "medium",
+      "action": "Medium priority action",
+      "reason": "Why this matters",
+      "evidence": "Supporting data"
+    }},
+    {{
+      "priority": "medium",
+      "action": "Another medium priority action",
+      "reason": "Why this matters",
+      "evidence": "Supporting data"
+    }},
+    {{
+      "priority": "low",
+      "action": "Lower priority improvement",
       "reason": "Why this matters",
       "evidence": "Supporting data"
     }}
   ]
 }}
 
-IMPORTANT: Ensure all JSON is properly formatted with no trailing commas.
+IMPORTANT: 
+- Provide at least 5 strengths and 5 recommendations
+- Reference actual aspects from the data above
+- Ensure all JSON is properly formatted with no trailing commas
 
 Generate manager insights:"""
         
         return prompt
     
-    def _summarize_menu_data(self, analysis_data: Dict[str, Any]) -> str:
-        """Summarize menu analysis for prompts."""
+    def _summarize_menu_data(
+        self, 
+        analysis_data: Dict[str, Any],
+        max_food: int = 20,
+        max_drinks: int = 10
+    ) -> str:
+        """
+        Summarize menu analysis for prompts.
+        
+        EXPANDED: Now uses top 20 food items and top 10 drinks (was 10/5).
+        """
         menu_data = analysis_data.get('menu_analysis', {})
-        food_items = menu_data.get('food_items', [])[:10]  # Top 10
-        drinks = menu_data.get('drinks', [])[:5]  # Top 5
+        food_items = menu_data.get('food_items', [])[:max_food]
+        drinks = menu_data.get('drinks', [])[:max_drinks]
         
         summary = []
         
         if food_items:
-            summary.append("TOP FOOD ITEMS:")
+            summary.append(f"TOP {len(food_items)} FOOD ITEMS:")
             for item in food_items:
                 sentiment = item.get('sentiment', 0)
                 mentions = item.get('mention_count', 0)
-                summary.append(f"  - {item.get('name', 'unknown')}: sentiment {sentiment:+.2f}, {mentions} mentions")
+                # Add sentiment indicator
+                indicator = "🟢" if sentiment > 0.3 else "🟡" if sentiment > -0.3 else "🔴"
+                summary.append(f"  {indicator} {item.get('name', 'unknown')}: sentiment {sentiment:+.2f}, {mentions} mentions")
         
         if drinks:
-            summary.append("\nTOP DRINKS:")
+            summary.append(f"\nTOP {len(drinks)} DRINKS:")
             for drink in drinks:
                 sentiment = drink.get('sentiment', 0)
                 mentions = drink.get('mention_count', 0)
-                summary.append(f"  - {drink.get('name', 'unknown')}: sentiment {sentiment:+.2f}, {mentions} mentions")
+                indicator = "🟢" if sentiment > 0.3 else "🟡" if sentiment > -0.3 else "🔴"
+                summary.append(f"  {indicator} {drink.get('name', 'unknown')}: sentiment {sentiment:+.2f}, {mentions} mentions")
+        
+        # Add overall stats
+        total_food = len(menu_data.get('food_items', []))
+        total_drinks = len(menu_data.get('drinks', []))
+        summary.append(f"\n(Total: {total_food} food items, {total_drinks} drinks discovered)")
         
         return '\n'.join(summary) if summary else "No menu data available"
     
-    def _summarize_aspect_data(self, analysis_data: Dict[str, Any], focus: str = 'all') -> str:
-        """Summarize aspect analysis for prompts."""
+    def _summarize_aspect_data(
+        self, 
+        analysis_data: Dict[str, Any], 
+        focus: str = 'all',
+        max_aspects: int = 20
+    ) -> str:
+        """
+        Summarize aspect analysis for prompts.
+        
+        EXPANDED: Now uses top 20 aspects (was 10).
+        """
         aspect_data = analysis_data.get('aspect_analysis', {})
         aspects = aspect_data.get('aspects', [])
         
         # Filter aspects based on focus
         if focus == 'food':
-            food_keywords = ['food', 'taste', 'flavor', 'quality', 'presentation', 'freshness', 'portion']
+            food_keywords = ['food', 'taste', 'flavor', 'quality', 'presentation', 'freshness', 
+                           'portion', 'dish', 'menu', 'ingredient', 'cook', 'season', 'texture']
             aspects = [a for a in aspects if any(kw in a.get('name', '').lower() for kw in food_keywords)]
         elif focus == 'operations':
-            ops_keywords = ['service', 'staff', 'wait', 'ambience', 'atmosphere', 'value', 'price', 'clean']
+            ops_keywords = ['service', 'staff', 'wait', 'ambience', 'atmosphere', 'value', 'price', 
+                          'clean', 'reservation', 'host', 'server', 'attentive', 'friendly', 
+                          'noise', 'music', 'parking', 'location', 'decor', 'vibe']
             aspects = [a for a in aspects if any(kw in a.get('name', '').lower() for kw in ops_keywords)]
         
-        aspects = aspects[:10]  # Top 10
+        aspects = aspects[:max_aspects]
         
         summary = []
+        summary.append(f"ASPECTS ({len(aspects)} found):")
+        
         for aspect in aspects:
             sentiment = aspect.get('sentiment', 0)
             mentions = aspect.get('mention_count', 0)
-            summary.append(f"  - {aspect.get('name', 'unknown')}: sentiment {sentiment:+.2f}, {mentions} mentions")
+            indicator = "🟢" if sentiment > 0.3 else "🟡" if sentiment > -0.3 else "🔴"
+            summary.append(f"  {indicator} {aspect.get('name', 'unknown')}: sentiment {sentiment:+.2f}, {mentions} mentions")
+        
+        # Add total count
+        total_aspects = len(aspect_data.get('aspects', []))
+        summary.append(f"\n(Total: {total_aspects} aspects discovered from reviews)")
         
         return '\n'.join(summary) if summary else "No aspect data available"
     
     def _get_fallback_insights(self, role: str) -> Dict[str, Any]:
-        """
-        Return fallback insights if generation fails.
-        """
+        """Return fallback insights if generation fails."""
         return {
-            "summary": f"Unable to generate {role} insights at this time.",
-            "strengths": ["Analysis data available for review"],
-            "concerns": ["Insight generation encountered an error"],
+            "summary": f"Unable to generate {role} insights at this time. Please try again.",
+            "strengths": ["Analysis in progress"],
+            "concerns": ["No data available"],
             "recommendations": [
                 {
-                    "priority": "high",
-                    "action": "Retry insight generation",
-                    "reason": "Complete analysis requires insights",
-                    "evidence": "System error"
+                    "priority": "medium",
+                    "action": "Re-run analysis with more reviews",
+                    "reason": "Insufficient data for detailed insights",
+                    "evidence": "N/A"
                 }
             ]
         }

src/ui/gradio_app.py

@@ -416,7 +416,7 @@ def translate_aspect_performance(aspects: dict, restaurant_name: str) -> str:
 
 
 def generate_chart(items: list, title: str) -> Optional[str]:
-    """Generate sentiment chart - top 10 by mentions."""
+    """Generate sentiment chart - top 10 by mentions, highest at TOP."""
     if not items:
         return None
     
@@ -432,7 +432,10 @@ def generate_chart(items: list, title: str) -> Optional[str]:
         NEUTRAL = '#f59e0b'
         NEGATIVE = '#ef4444'
         
+        # Sort by mention_count descending, then REVERSE for display
+        # (so highest mentions appear at TOP of horizontal bar chart)
         sorted_items = sorted(items, key=lambda x: x.get('mention_count', 0), reverse=True)[:10]
+        sorted_items = sorted_items[::-1]  # Reverse so highest is at top
         
         names = [f"{item.get('name', '?')[:18]} ({item.get('mention_count', 0)})" for item in sorted_items]
         sentiments = [item.get('sentiment', 0) for item in sorted_items]
@@ -619,7 +622,10 @@ def get_aspect_detail(aspect_name: str, state: dict) -> str:
 # ============================================================================
 
 def generate_pdf_report(state: dict) -> Optional[str]:
-    """Generate PDF report from analysis state - FIXED VERSION."""
+    """
+    Generate professional PDF report from analysis state.
+    Uses ReportLab with custom styling for a polished output.
+    """
     if not state:
         print("[PDF] No state provided")
         return None
@@ -628,189 +634,422 @@ def generate_pdf_report(state: dict) -> Optional[str]:
         from reportlab.lib.pagesizes import letter
         from reportlab.lib.units import inch
         from reportlab.lib import colors
-        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
+        from reportlab.lib.colors import HexColor
+        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak, HRFlowable
         from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-        from reportlab.lib.enums import TA_CENTER
-        
+        from reportlab.lib.enums import TA_CENTER, TA_LEFT
+        
+        # Color scheme
+        PRIMARY = HexColor('#2563eb')
+        PRIMARY_LIGHT = HexColor('#dbeafe')
+        POSITIVE = HexColor('#10b981')
+        POSITIVE_LIGHT = HexColor('#d1fae5')
+        WARNING = HexColor('#f59e0b')
+        WARNING_LIGHT = HexColor('#fef3c7')
+        NEGATIVE = HexColor('#ef4444')
+        NEGATIVE_LIGHT = HexColor('#fee2e2')
+        TEXT_DARK = HexColor('#1f2937')
+        TEXT_LIGHT = HexColor('#6b7280')
+        BACKGROUND = HexColor('#f9fafb')
+        BORDER = HexColor('#e5e7eb')
+        
+        # Extract data
         restaurant_name = state.get('restaurant_name', 'Restaurant')
+        source = state.get('source', 'unknown').replace('_', ' ').title()
+        menu = state.get('menu_analysis', {})
+        aspects = state.get('aspect_analysis', {})
+        insights = state.get('insights', {})
+        raw_reviews = state.get('raw_reviews', [])
+        
+        food_items = menu.get('food_items', [])
+        drinks = menu.get('drinks', [])
+        all_menu = food_items + drinks
+        aspect_list = aspects.get('aspects', [])
+        
+        # Create file
         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
         safe_name = restaurant_name.lower().replace(" ", "_").replace("/", "_").replace("&", "and")[:30]
         output_path = os.path.join(tempfile.gettempdir(), f"{safe_name}_report_{timestamp}.pdf")
         
-        print(f"[PDF] Generating report for {restaurant_name} at {output_path}")
+        print(f"[PDF] Generating professional report for {restaurant_name}")
         
-        doc = SimpleDocTemplate(output_path, pagesize=letter,
-                               rightMargin=0.75*inch, leftMargin=0.75*inch,
-                               topMargin=0.75*inch, bottomMargin=0.75*inch)
+        doc = SimpleDocTemplate(
+            output_path,
+            pagesize=letter,
+            rightMargin=0.75*inch,
+            leftMargin=0.75*inch,
+            topMargin=0.75*inch,
+            bottomMargin=0.75*inch
+        )
         
         styles = getSampleStyleSheet()
         
         # Custom styles
-        title_style = ParagraphStyle('CustomTitle', parent=styles['Heading1'],
-                                    fontSize=24, textColor=colors.HexColor('#2196F3'),
-                                    alignment=TA_CENTER, spaceAfter=20)
+        styles.add(ParagraphStyle('CoverTitle', parent=styles['Heading1'],
+                                 fontSize=32, textColor=PRIMARY, alignment=TA_CENTER,
+                                 spaceAfter=10, fontName='Helvetica-Bold'))
         
-        heading_style = ParagraphStyle('CustomHeading', parent=styles['Heading2'],
-                                      fontSize=14, textColor=colors.HexColor('#1f2937'),
-                                      spaceBefore=15, spaceAfter=10)
+        styles.add(ParagraphStyle('CoverSubtitle', parent=styles['Normal'],
+                                 fontSize=16, textColor=TEXT_LIGHT, alignment=TA_CENTER,
+                                 spaceAfter=30, fontName='Helvetica'))
         
-        body_style = ParagraphStyle('CustomBody', parent=styles['Normal'],
-                                   fontSize=10, textColor=colors.HexColor('#374151'),
-                                   spaceAfter=8)
+        styles.add(ParagraphStyle('CoverRestaurant', parent=styles['Heading1'],
+                                 fontSize=24, textColor=TEXT_DARK, alignment=TA_CENTER,
+                                 spaceAfter=15, fontName='Helvetica-Bold'))
         
-        elements = []
+        styles.add(ParagraphStyle('SectionHeader', parent=styles['Heading1'],
+                                 fontSize=18, textColor=PRIMARY, spaceBefore=20,
+                                 spaceAfter=12, fontName='Helvetica-Bold'))
         
-        # Title
-        elements.append(Paragraph("🍽️ Restaurant Intelligence Report", title_style))
-        elements.append(Paragraph(restaurant_name, ParagraphStyle('RestName', parent=styles['Heading2'],
-                                                                  fontSize=18, alignment=TA_CENTER)))
-        elements.append(Paragraph(f"Generated: {datetime.now().strftime('%B %d, %Y at %I:%M %p')}", 
-                                 ParagraphStyle('Date', parent=styles['Normal'], alignment=TA_CENTER,
-                                               textColor=colors.grey)))
-        elements.append(Spacer(1, 30))
+        styles.add(ParagraphStyle('SubHeader', parent=styles['Heading2'],
+                                 fontSize=14, textColor=TEXT_DARK, spaceBefore=15,
+                                 spaceAfter=8, fontName='Helvetica-Bold'))
         
-        # Stats
-        menu = state.get('menu_analysis', {})
-        aspects = state.get('aspect_analysis', {})
-        raw_reviews = state.get('raw_reviews', [])
-        source = state.get('source', 'unknown')
+        styles.add(ParagraphStyle('BodyText', parent=styles['Normal'],
+                                 fontSize=10, textColor=TEXT_DARK, spaceAfter=8,
+                                 leading=14, fontName='Helvetica'))
         
-        food_items = menu.get('food_items', [])
-        drinks = menu.get('drinks', [])
-        aspect_list = aspects.get('aspects', [])
+        styles.add(ParagraphStyle('Bullet', parent=styles['Normal'],
+                                 fontSize=10, textColor=TEXT_DARK, leftIndent=20,
+                                 spaceAfter=5, fontName='Helvetica'))
         
-        elements.append(Paragraph("📊 Executive Summary", heading_style))
+        styles.add(ParagraphStyle('Quote', parent=styles['Normal'],
+                                 fontSize=10, textColor=TEXT_LIGHT, leftIndent=20,
+                                 rightIndent=20, spaceAfter=10, fontName='Helvetica-Oblique'))
         
-        stats_data = [
-            ['Metric', 'Value'],
-            ['Source Platform', source.replace('_', ' ').title()],
-            ['Reviews Analyzed', str(len(raw_reviews))],
-            ['Menu Items Found', f"{len(food_items) + len(drinks)} ({len(food_items)} food, {len(drinks)} drinks)"],
-            ['Aspects Analyzed', str(len(aspect_list))],
-        ]
+        styles.add(ParagraphStyle('Footer', parent=styles['Normal'],
+                                 fontSize=8, textColor=TEXT_LIGHT, alignment=TA_CENTER))
+        
+        styles.add(ParagraphStyle('PriorityHigh', parent=styles['Normal'],
+                                 fontSize=10, textColor=NEGATIVE, leftIndent=20,
+                                 spaceAfter=5, fontName='Helvetica-Bold'))
+        
+        styles.add(ParagraphStyle('PriorityMedium', parent=styles['Normal'],
+                                 fontSize=10, textColor=WARNING, leftIndent=20,
+                                 spaceAfter=5, fontName='Helvetica-Bold'))
         
-        stats_table = Table(stats_data, colWidths=[2.5*inch, 3*inch])
+        styles.add(ParagraphStyle('PriorityLow', parent=styles['Normal'],
+                                 fontSize=10, textColor=POSITIVE, leftIndent=20,
+                                 spaceAfter=5, fontName='Helvetica-Bold'))
+        
+        elements = []
+        
+        # ==================== COVER PAGE ====================
+        elements.append(Spacer(1, 1.5*inch))
+        elements.append(Paragraph("RESTAURANT", styles['CoverTitle']))
+        elements.append(Paragraph("INTELLIGENCE REPORT", styles['CoverTitle']))
+        elements.append(Spacer(1, 0.3*inch))
+        elements.append(Paragraph("AI-Powered Customer Review Analysis", styles['CoverSubtitle']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=10, spaceAfter=10))
+        elements.append(Spacer(1, 0.5*inch))
+        elements.append(Paragraph(restaurant_name, styles['CoverRestaurant']))
+        elements.append(Spacer(1, 0.3*inch))
+        elements.append(Paragraph(f"Data Source: {source}", styles['Footer']))
+        elements.append(Spacer(1, 0.5*inch))
+        
+        # Stats boxes
+        stats_data = [[
+            str(len(raw_reviews)), str(len(all_menu)), str(len(aspect_list))
+        ], [
+            "Reviews", "Menu Items", "Aspects"
+        ]]
+        stats_table = Table(stats_data, colWidths=[2*inch, 2*inch, 2*inch])
         stats_table.setStyle(TableStyle([
-            ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#2196F3')),
+            ('BACKGROUND', (0, 0), (-1, -1), BACKGROUND),
+            ('TEXTCOLOR', (0, 0), (-1, 0), PRIMARY),
+            ('TEXTCOLOR', (0, 1), (-1, 1), TEXT_LIGHT),
+            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+            ('FONTSIZE', (0, 0), (-1, 0), 24),
+            ('FONTSIZE', (0, 1), (-1, 1), 10),
+            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+            ('TOPPADDING', (0, 0), (-1, -1), 15),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 15),
+            ('BOX', (0, 0), (-1, -1), 1, BORDER),
+        ]))
+        elements.append(stats_table)
+        elements.append(Spacer(1, 1*inch))
+        elements.append(Paragraph(f"Generated: {datetime.now().strftime('%B %d, %Y at %I:%M %p')}", styles['Footer']))
+        elements.append(Paragraph("Powered by Claude AI • Restaurant Intelligence Agent", styles['Footer']))
+        elements.append(PageBreak())
+        
+        # ==================== EXECUTIVE SUMMARY ====================
+        elements.append(Paragraph("Executive Summary", styles['SectionHeader']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=5, spaceAfter=15))
+        
+        # Calculate sentiment
+        all_sentiments = [item.get('sentiment', 0) for item in all_menu]
+        avg_sentiment = sum(all_sentiments) / len(all_sentiments) if all_sentiments else 0
+        
+        sent_label = "Excellent" if avg_sentiment > 0.5 else "Good" if avg_sentiment > 0.3 else "Positive" if avg_sentiment > 0 else "Mixed" if avg_sentiment > -0.3 else "Needs Attention"
+        sent_color = POSITIVE if avg_sentiment > 0.3 else WARNING if avg_sentiment > -0.3 else NEGATIVE
+        sent_bg = POSITIVE_LIGHT if avg_sentiment > 0.3 else WARNING_LIGHT if avg_sentiment > -0.3 else NEGATIVE_LIGHT
+        
+        # Sentiment box
+        sent_data = [[f"Overall Sentiment: {avg_sentiment:+.2f}", sent_label]]
+        sent_table = Table(sent_data, colWidths=[3.5*inch, 2*inch])
+        sent_table.setStyle(TableStyle([
+            ('BACKGROUND', (0, 0), (-1, -1), sent_bg),
+            ('TEXTCOLOR', (0, 0), (-1, -1), sent_color),
+            ('FONTNAME', (0, 0), (-1, -1), 'Helvetica-Bold'),
+            ('FONTSIZE', (0, 0), (-1, -1), 14),
+            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+            ('TOPPADDING', (0, 0), (-1, -1), 15),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 15),
+            ('BOX', (0, 0), (-1, -1), 2, sent_color),
+        ]))
+        elements.append(sent_table)
+        elements.append(Spacer(1, 15))
+        
+        # Key highlights
+        elements.append(Paragraph("Key Highlights", styles['SubHeader']))
+        top_items = sorted(all_menu, key=lambda x: x.get('sentiment', 0), reverse=True)[:3]
+        if top_items:
+            elements.append(Paragraph("✅ <b>Top Performing Items:</b>", styles['BodyText']))
+            for item in top_items:
+                elements.append(Paragraph(f"    • {item.get('name', '?').title()} (sentiment: {item.get('sentiment', 0):+.2f})", styles['Bullet']))
+        
+        concern_items = [i for i in all_menu if i.get('sentiment', 0) < -0.2]
+        if concern_items:
+            elements.append(Spacer(1, 10))
+            elements.append(Paragraph("⚠️ <b>Items Needing Attention:</b>", styles['BodyText']))
+            for item in sorted(concern_items, key=lambda x: x.get('sentiment', 0))[:3]:
+                elements.append(Paragraph(f"    • {item.get('name', '?').title()} (sentiment: {item.get('sentiment', 0):+.2f})", styles['Bullet']))
+        
+        elements.append(Spacer(1, 15))
+        
+        # Summary stats
+        stars = len([i for i in all_menu if i.get('sentiment', 0) > 0.5])
+        good = len([i for i in all_menu if 0.2 < i.get('sentiment', 0) <= 0.5])
+        mixed = len([i for i in all_menu if -0.2 <= i.get('sentiment', 0) <= 0.2])
+        concerns = len([i for i in all_menu if i.get('sentiment', 0) < -0.2])
+        
+        summary_data = [
+            ['Metric', 'Value', 'Details'],
+            ['Reviews Analyzed', str(len(raw_reviews)), f'From {source}'],
+            ['Menu Items', str(len(all_menu)), f'{len(food_items)} food, {len(drinks)} drinks'],
+            ['Customer Favorites', str(stars), 'Sentiment > 0.5'],
+            ['Performing Well', str(good), 'Sentiment 0.2 - 0.5'],
+            ['Mixed Reviews', str(mixed), 'Sentiment -0.2 - 0.2'],
+            ['Needs Attention', str(concerns), 'Sentiment < -0.2'],
+        ]
+        summary_table = Table(summary_data, colWidths=[2*inch, 1.3*inch, 2.5*inch])
+        summary_table.setStyle(TableStyle([
+            ('BACKGROUND', (0, 0), (-1, 0), PRIMARY),
             ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
             ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
             ('FONTSIZE', (0, 0), (-1, -1), 10),
-            ('BOTTOMPADDING', (0, 0), (-1, 0), 10),
-            ('TOPPADDING', (0, 0), (-1, 0), 10),
-            ('BACKGROUND', (0, 1), (-1, -1), colors.HexColor('#f3f4f6')),
-            ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#e5e7eb')),
-            ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
-            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
-            ('LEFTPADDING', (0, 0), (-1, -1), 10),
-            ('RIGHTPADDING', (0, 0), (-1, -1), 10),
+            ('ALIGN', (1, 0), (1, -1), 'CENTER'),
+            ('TOPPADDING', (0, 0), (-1, -1), 8),
+            ('BOTTOMPADDING', (0, 0), (-1, -1), 8),
+            ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, BACKGROUND]),
+            ('GRID', (0, 0), (-1, -1), 0.5, BORDER),
         ]))
-        elements.append(stats_table)
-        elements.append(Spacer(1, 20))
-        
-        # Top Menu Items
-        if food_items or drinks:
-            elements.append(Paragraph("🍽️ Top Menu Items (by mentions)", heading_style))
-            all_menu = food_items + drinks
-            sorted_menu = sorted(all_menu, key=lambda x: x.get('mention_count', 0), reverse=True)[:10]
+        elements.append(summary_table)
+        elements.append(PageBreak())
+        
+        # ==================== MENU ANALYSIS ====================
+        elements.append(Paragraph("Menu Performance Analysis", styles['SectionHeader']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=5, spaceAfter=15))
+        
+        if all_menu:
+            elements.append(Paragraph(
+                f"Analysis of <b>{len(all_menu)}</b> menu items ({len(food_items)} food, {len(drinks)} drinks) based on {len(raw_reviews)} customer reviews.",
+                styles['BodyText']
+            ))
+            elements.append(Spacer(1, 10))
             
-            menu_data = [['Item', 'Sentiment', 'Mentions', 'Status']]
-            for item in sorted_menu:
+            sorted_menu = sorted(all_menu, key=lambda x: x.get('mention_count', 0), reverse=True)[:20]
+            menu_data = [['#', 'Item', 'Sentiment', 'Mentions', 'Status']]
+            for i, item in enumerate(sorted_menu, 1):
                 sentiment = item.get('sentiment', 0)
                 status = '✓ Positive' if sentiment > 0.3 else '~ Mixed' if sentiment > -0.3 else '✗ Negative'
-                menu_data.append([
-                    item.get('name', '?').title()[:25],
-                    f"{sentiment:+.2f}",
-                    str(item.get('mention_count', 0)),
-                    status
-                ])
+                menu_data.append([str(i), item.get('name', '?').title()[:22], f"{sentiment:+.2f}", str(item.get('mention_count', 0)), status])
             
-            menu_table = Table(menu_data, colWidths=[2*inch, 1*inch, 0.8*inch, 1.2*inch])
+            menu_table = Table(menu_data, colWidths=[0.4*inch, 2.2*inch, 1*inch, 0.9*inch, 1.1*inch])
             menu_table.setStyle(TableStyle([
-                ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#10b981')),
+                ('BACKGROUND', (0, 0), (-1, 0), POSITIVE),
                 ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
                 ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
                 ('FONTSIZE', (0, 0), (-1, -1), 9),
-                ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
+                ('ALIGN', (0, 0), (0, -1), 'CENTER'),
+                ('ALIGN', (2, 0), (3, -1), 'CENTER'),
                 ('TOPPADDING', (0, 0), (-1, -1), 6),
-                ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.HexColor('#f9fafb')]),
-                ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#e5e7eb')),
-                ('ALIGN', (1, 0), (-1, -1), 'CENTER'),
+                ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
+                ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, BACKGROUND]),
+                ('GRID', (0, 0), (-1, -1), 0.5, BORDER),
             ]))
             elements.append(menu_table)
-            elements.append(Spacer(1, 20))
+        elements.append(Spacer(1, 20))
+        
+        # ==================== ASPECT ANALYSIS ====================
+        elements.append(Paragraph("Customer Experience Aspects", styles['SectionHeader']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=5, spaceAfter=15))
         
-        # Aspects
         if aspect_list:
-            elements.append(Paragraph("📊 Customer Experience Aspects", heading_style))
-            sorted_aspects = sorted(aspect_list, key=lambda x: x.get('mention_count', 0), reverse=True)[:10]
-            
-            aspect_data = [['Aspect', 'Sentiment', 'Mentions', 'Status']]
-            for aspect in sorted_aspects:
+            sorted_aspects = sorted(aspect_list, key=lambda x: x.get('mention_count', 0), reverse=True)[:20]
+            aspect_data = [['#', 'Aspect', 'Sentiment', 'Mentions', 'Status']]
+            for i, aspect in enumerate(sorted_aspects, 1):
                 sentiment = aspect.get('sentiment', 0)
                 status = '✓ Strength' if sentiment > 0.3 else '~ Neutral' if sentiment > -0.3 else '✗ Weakness'
-                aspect_data.append([
-                    aspect.get('name', '?').title()[:25],
-                    f"{sentiment:+.2f}",
-                    str(aspect.get('mention_count', 0)),
-                    status
-                ])
+                aspect_data.append([str(i), aspect.get('name', '?').title()[:22], f"{sentiment:+.2f}", str(aspect.get('mention_count', 0)), status])
             
-            aspect_table = Table(aspect_data, colWidths=[2*inch, 1*inch, 0.8*inch, 1.2*inch])
+            aspect_table = Table(aspect_data, colWidths=[0.4*inch, 2.2*inch, 1*inch, 0.9*inch, 1.1*inch])
             aspect_table.setStyle(TableStyle([
-                ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#f59e0b')),
+                ('BACKGROUND', (0, 0), (-1, 0), WARNING),
                 ('TEXTCOLOR', (0, 0), (-1, 0), colors.white),
                 ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
                 ('FONTSIZE', (0, 0), (-1, -1), 9),
-                ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
+                ('ALIGN', (0, 0), (0, -1), 'CENTER'),
+                ('ALIGN', (2, 0), (3, -1), 'CENTER'),
                 ('TOPPADDING', (0, 0), (-1, -1), 6),
-                ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, colors.HexColor('#f9fafb')]),
-                ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#e5e7eb')),
-                ('ALIGN', (1, 0), (-1, -1), 'CENTER'),
+                ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
+                ('ROWBACKGROUNDS', (0, 1), (-1, -1), [colors.white, BACKGROUND]),
+                ('GRID', (0, 0), (-1, -1), 0.5, BORDER),
             ]))
             elements.append(aspect_table)
-            elements.append(Spacer(1, 20))
+        elements.append(PageBreak())
         
-        # Insights
-        insights = state.get('insights', {})
+        # ==================== CHEF INSIGHTS ====================
+        elements.append(Paragraph("🍳 Chef Insights", styles['SectionHeader']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=5, spaceAfter=15))
         
-        chef_insights = insights.get('chef', {})
-        if chef_insights:
-            elements.append(Paragraph("🍳 Chef Insights", heading_style))
-            if chef_insights.get('summary'):
-                elements.append(Paragraph(f"<b>Summary:</b> {chef_insights['summary']}", body_style))
-            if chef_insights.get('strengths'):
-                strengths = chef_insights['strengths']
+        chef_data = insights.get('chef', {})
+        if chef_data:
+            if chef_data.get('summary'):
+                elements.append(Paragraph("Summary", styles['SubHeader']))
+                elements.append(Paragraph(str(chef_data['summary']), styles['BodyText']))
+            
+            if chef_data.get('strengths'):
+                elements.append(Paragraph("✅ Strengths", styles['SubHeader']))
+                strengths = chef_data['strengths']
                 if isinstance(strengths, list):
-                    for s in strengths[:3]:
+                    for s in strengths[:8]:  # Show up to 8 strengths
                         text = s.get('action', str(s)) if isinstance(s, dict) else str(s)
-                        elements.append(Paragraph(f"✓ {text}", body_style))
-            elements.append(Spacer(1, 10))
+                        elements.append(Paragraph(f"• {text}", styles['Bullet']))
+            
+            if chef_data.get('concerns'):
+                elements.append(Paragraph("⚠️ Areas of Concern", styles['SubHeader']))
+                concerns = chef_data['concerns']
+                if isinstance(concerns, list):
+                    for c in concerns[:5]:  # Show up to 5 concerns
+                        text = c.get('action', str(c)) if isinstance(c, dict) else str(c)
+                        elements.append(Paragraph(f"• {text}", styles['Bullet']))
+            
+            if chef_data.get('recommendations'):
+                elements.append(Paragraph("💡 Recommendations", styles['SubHeader']))
+                recs = chef_data['recommendations']
+                if isinstance(recs, list):
+                    for r in recs[:8]:  # Show up to 8 recommendations
+                        if isinstance(r, dict):
+                            priority = r.get('priority', 'medium').lower()
+                            action = r.get('action', str(r))
+                            style_name = 'PriorityHigh' if priority == 'high' else 'PriorityMedium' if priority == 'medium' else 'PriorityLow'
+                            elements.append(Paragraph(f"[{priority.upper()}] {action}", styles[style_name]))
+                        else:
+                            elements.append(Paragraph(f"• {r}", styles['Bullet']))
+        else:
+            elements.append(Paragraph("Chef insights will be available after full analysis.", styles['BodyText']))
         
-        manager_insights = insights.get('manager', {})
-        if manager_insights:
-            elements.append(Paragraph("📊 Manager Insights", heading_style))
-            if manager_insights.get('summary'):
-                elements.append(Paragraph(f"<b>Summary:</b> {manager_insights['summary']}", body_style))
-            if manager_insights.get('recommendations'):
-                recs = manager_insights['recommendations']
+        elements.append(Spacer(1, 20))
+        
+        # ==================== MANAGER INSIGHTS ====================
+        elements.append(Paragraph("📊 Manager Insights", styles['SectionHeader']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=5, spaceAfter=15))
+        
+        manager_data = insights.get('manager', {})
+        if manager_data:
+            if manager_data.get('summary'):
+                elements.append(Paragraph("Summary", styles['SubHeader']))
+                elements.append(Paragraph(str(manager_data['summary']), styles['BodyText']))
+            
+            if manager_data.get('strengths'):
+                elements.append(Paragraph("✅ Operational Strengths", styles['SubHeader']))
+                strengths = manager_data['strengths']
+                if isinstance(strengths, list):
+                    for s in strengths[:8]:  # Show up to 8 strengths
+                        text = s.get('action', str(s)) if isinstance(s, dict) else str(s)
+                        elements.append(Paragraph(f"• {text}", styles['Bullet']))
+            
+            if manager_data.get('concerns'):
+                elements.append(Paragraph("⚠️ Operational Concerns", styles['SubHeader']))
+                concerns = manager_data['concerns']
+                if isinstance(concerns, list):
+                    for c in concerns[:5]:  # Show up to 5 concerns
+                        text = c.get('action', str(c)) if isinstance(c, dict) else str(c)
+                        elements.append(Paragraph(f"• {text}", styles['Bullet']))
+            
+            if manager_data.get('recommendations'):
+                elements.append(Paragraph("💡 Action Items", styles['SubHeader']))
+                recs = manager_data['recommendations']
                 if isinstance(recs, list):
-                    for r in recs[:3]:
+                    for r in recs[:8]:  # Show up to 8 recommendations
                         if isinstance(r, dict):
-                            priority = r.get('priority', '')
+                            priority = r.get('priority', 'medium').lower()
                             action = r.get('action', str(r))
-                            elements.append(Paragraph(f"[{priority.upper()}] {action}", body_style))
+                            style_name = 'PriorityHigh' if priority == 'high' else 'PriorityMedium' if priority == 'medium' else 'PriorityLow'
+                            elements.append(Paragraph(f"[{priority.upper()}] {action}", styles[style_name]))
                         else:
-                            elements.append(Paragraph(f"• {r}", body_style))
+                            elements.append(Paragraph(f"• {r}", styles['Bullet']))
+        else:
+            elements.append(Paragraph("Manager insights will be available after full analysis.", styles['BodyText']))
+        
+        elements.append(PageBreak())
         
-        # Footer
+        # ==================== CUSTOMER FEEDBACK HIGHLIGHTS ====================
+        elements.append(Paragraph("Customer Feedback Highlights", styles['SectionHeader']))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=5, spaceAfter=15))
+        
+        positive_reviews = []
+        negative_reviews = []
+        
+        for review in raw_reviews[:50]:
+            if isinstance(review, dict):
+                text = review.get('text', '') or review.get('review_text', '')
+            else:
+                text = str(review)
+            
+            if not text or len(text) < 30:
+                continue
+            
+            text_lower = text.lower()
+            pos_words = ['amazing', 'excellent', 'fantastic', 'great', 'awesome', 'delicious', 'perfect', 'loved', 'best']
+            neg_words = ['terrible', 'horrible', 'awful', 'bad', 'worst', 'disappointing', 'poor', 'rude']
+            
+            pos_count = sum(1 for w in pos_words if w in text_lower)
+            neg_count = sum(1 for w in neg_words if w in text_lower)
+            
+            if pos_count > neg_count and len(positive_reviews) < 3:
+                positive_reviews.append(text[:180])
+            elif neg_count > pos_count and len(negative_reviews) < 3:
+                negative_reviews.append(text[:180])
+        
+        elements.append(Paragraph("✅ Positive Feedback", styles['SubHeader']))
+        if positive_reviews:
+            for review in positive_reviews:
+                elements.append(Paragraph(f'"{review}..."', styles['Quote']))
+        else:
+            elements.append(Paragraph("Detailed positive feedback samples not available.", styles['BodyText']))
+        
+        elements.append(Spacer(1, 15))
+        
+        elements.append(Paragraph("⚠️ Critical Feedback", styles['SubHeader']))
+        if negative_reviews:
+            for review in negative_reviews:
+                elements.append(Paragraph(f'"{review}..."', styles['Quote']))
+        else:
+            elements.append(Paragraph("No significant negative feedback identified. Great job!", styles['BodyText']))
+        
+        # ==================== FOOTER ====================
         elements.append(Spacer(1, 30))
-        elements.append(Paragraph("Generated by Restaurant Intelligence Agent | Powered by Claude AI",
-                                 ParagraphStyle('Footer', parent=styles['Normal'],
-                                               fontSize=8, textColor=colors.grey, alignment=TA_CENTER)))
+        elements.append(HRFlowable(width="100%", thickness=1, color=BORDER, spaceBefore=10, spaceAfter=10))
+        elements.append(Paragraph(f"Report generated for {restaurant_name}", styles['Footer']))
+        elements.append(Paragraph(f"Generated on {datetime.now().strftime('%B %d, %Y at %I:%M %p')}", styles['Footer']))
+        elements.append(Paragraph("Restaurant Intelligence Agent • Powered by Claude AI", styles['Footer']))
+        elements.append(Paragraph("© 2025 - Built for Anthropic MCP Hackathon", styles['Footer']))
         
         # Build PDF
         doc.build(elements)
-        print(f"[PDF] Successfully generated: {output_path}")
+        print(f"[PDF] Successfully generated professional report: {output_path}")
         return output_path
         
     except Exception as e: