ALwrity/ToBeMigrated/ai_seo_tools/enterprise_seo_suite.py

"""
Enterprise SEO Command Center

Unified AI-powered SEO suite that orchestrates all existing tools into 
intelligent workflows for enterprise-level SEO management.
"""

import streamlit as st
import asyncio
import pandas as pd
from typing import Dict, Any, List, Optional, Tuple
from datetime import datetime, timedelta
import json
from loguru import logger

# Import existing SEO tools
from .on_page_seo_analyzer import fetch_seo_data
from .content_gap_analysis.enhanced_analyzer import EnhancedContentGapAnalyzer
from .technical_seo_crawler.crawler import TechnicalSEOCrawler
from .weburl_seo_checker import url_seo_checker
from .google_pagespeed_insights import google_pagespeed_insights
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen

# Import the new enterprise tools
from .google_search_console_integration import GoogleSearchConsoleAnalyzer, render_gsc_integration
from .ai_content_strategy import AIContentStrategyGenerator, render_ai_content_strategy

class EnterpriseSEOSuite:
    """
    Enterprise-level SEO suite orchestrating all tools into intelligent workflows.
    """
    
    def __init__(self):
        """Initialize the enterprise SEO suite."""
        self.gap_analyzer = EnhancedContentGapAnalyzer()
        self.technical_crawler = TechnicalSEOCrawler()
        
        # Initialize new enterprise tools
        self.gsc_analyzer = GoogleSearchConsoleAnalyzer()
        self.content_strategy_generator = AIContentStrategyGenerator()
        
        # SEO workflow templates
        self.workflow_templates = {
            'complete_audit': 'Complete SEO Audit',
            'content_strategy': 'Content Strategy Development',
            'technical_optimization': 'Technical SEO Optimization',
            'competitor_intelligence': 'Competitive Intelligence',
            'keyword_domination': 'Keyword Domination Strategy',
            'local_seo': 'Local SEO Optimization',
            'enterprise_monitoring': 'Enterprise SEO Monitoring'
        }
        
        logger.info("Enterprise SEO Suite initialized")
    
    async def execute_complete_seo_audit(self, website_url: str, competitors: List[str], 
                                       target_keywords: List[str]) -> Dict[str, Any]:
        """
        Execute a comprehensive enterprise SEO audit combining all tools.
        
        Args:
            website_url: Primary website to audit
            competitors: List of competitor URLs (max 5)
            target_keywords: Primary keywords to optimize for
            
        Returns:
            Comprehensive audit results with prioritized action plan
        """
        try:
            st.info("🚀 Initiating Complete Enterprise SEO Audit...")
            
            audit_results = {
                'audit_timestamp': datetime.utcnow().isoformat(),
                'website_url': website_url,
                'competitors': competitors[:5],
                'target_keywords': target_keywords,
                'technical_audit': {},
                'content_analysis': {},
                'competitive_intelligence': {},
                'on_page_analysis': {},
                'performance_metrics': {},
                'strategic_recommendations': {},
                'priority_action_plan': []
            }
            
            # Phase 1: Technical SEO Audit
            with st.expander("🔧 Technical SEO Analysis", expanded=True):
                st.info("Analyzing technical SEO factors...")
                technical_results = await self._run_technical_audit(website_url)
                audit_results['technical_audit'] = technical_results
                st.success("✅ Technical audit completed")
            
            # Phase 2: Content Gap Analysis
            with st.expander("📊 Content Intelligence Analysis", expanded=True):
                st.info("Analyzing content gaps and opportunities...")
                content_results = await self._run_content_analysis(
                    website_url, competitors, target_keywords
                )
                audit_results['content_analysis'] = content_results
                st.success("✅ Content analysis completed")
            
            # Phase 3: On-Page SEO Analysis
            with st.expander("🔍 On-Page SEO Analysis", expanded=True):
                st.info("Analyzing on-page SEO factors...")
                onpage_results = await self._run_onpage_analysis(website_url)
                audit_results['on_page_analysis'] = onpage_results
                st.success("✅ On-page analysis completed")
            
            # Phase 4: Performance Analysis
            with st.expander("⚡ Performance Analysis", expanded=True):
                st.info("Analyzing website performance...")
                performance_results = await self._run_performance_analysis(website_url)
                audit_results['performance_metrics'] = performance_results
                st.success("✅ Performance analysis completed")
            
            # Phase 5: AI-Powered Strategic Recommendations
            with st.expander("🤖 AI Strategic Analysis", expanded=True):
                st.info("Generating AI-powered strategic recommendations...")
                strategic_analysis = await self._generate_strategic_recommendations(audit_results)
                audit_results['strategic_recommendations'] = strategic_analysis
                
                # Generate prioritized action plan
                action_plan = await self._create_priority_action_plan(audit_results)
                audit_results['priority_action_plan'] = action_plan
                st.success("✅ Strategic analysis completed")
            
            return audit_results
            
        except Exception as e:
            error_msg = f"Error in complete SEO audit: {str(e)}"
            logger.error(error_msg, exc_info=True)
            st.error(error_msg)
            return {'error': error_msg}
    
    async def _run_technical_audit(self, website_url: str) -> Dict[str, Any]:
        """Run comprehensive technical SEO audit."""
        try:
            # Use existing technical crawler
            technical_results = self.technical_crawler.analyze_website_technical_seo(
                website_url, crawl_depth=3, max_pages=100
            )
            
            # Enhance with additional technical checks
            enhanced_results = {
                'crawler_results': technical_results,
                'critical_issues': self._identify_critical_technical_issues(technical_results),
                'performance_score': self._calculate_technical_score(technical_results),
                'priority_fixes': self._prioritize_technical_fixes(technical_results)
            }
            
            return enhanced_results
            
        except Exception as e:
            logger.error(f"Technical audit error: {str(e)}")
            return {'error': str(e)}
    
    async def _run_content_analysis(self, website_url: str, competitors: List[str], 
                                  keywords: List[str]) -> Dict[str, Any]:
        """Run comprehensive content gap analysis."""
        try:
            # Use existing content gap analyzer
            content_results = self.gap_analyzer.analyze_comprehensive_gap(
                website_url, competitors, keywords, industry="general"
            )
            
            # Enhance with content strategy insights
            enhanced_results = {
                'gap_analysis': content_results,
                'content_opportunities': self._identify_content_opportunities(content_results),
                'keyword_strategy': self._develop_keyword_strategy(content_results),
                'competitive_advantages': self._find_competitive_advantages(content_results)
            }
            
            return enhanced_results
            
        except Exception as e:
            logger.error(f"Content analysis error: {str(e)}")
            return {'error': str(e)}
    
    async def _run_onpage_analysis(self, website_url: str) -> Dict[str, Any]:
        """Run on-page SEO analysis."""
        try:
            # Use existing on-page analyzer
            onpage_data = fetch_seo_data(website_url)
            
            # Enhanced analysis
            enhanced_results = {
                'seo_data': onpage_data,
                'optimization_score': self._calculate_onpage_score(onpage_data),
                'meta_optimization': self._analyze_meta_optimization(onpage_data),
                'content_optimization': self._analyze_content_optimization(onpage_data)
            }
            
            return enhanced_results
            
        except Exception as e:
            logger.error(f"On-page analysis error: {str(e)}")
            return {'error': str(e)}
    
    async def _run_performance_analysis(self, website_url: str) -> Dict[str, Any]:
        """Run website performance analysis."""
        try:
            # Comprehensive performance metrics
            performance_results = {
                'core_web_vitals': await self._analyze_core_web_vitals(website_url),
                'loading_performance': await self._analyze_loading_performance(website_url),
                'mobile_optimization': await self._analyze_mobile_optimization(website_url),
                'performance_score': 0  # Will be calculated
            }
            
            # Calculate overall performance score
            performance_results['performance_score'] = self._calculate_performance_score(
                performance_results
            )
            
            return performance_results
            
        except Exception as e:
            logger.error(f"Performance analysis error: {str(e)}")
            return {'error': str(e)}
    
    async def _generate_strategic_recommendations(self, audit_results: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI-powered strategic recommendations."""
        try:
            # Compile audit summary for AI analysis
            audit_summary = {
                'technical_score': audit_results.get('technical_audit', {}).get('performance_score', 0),
                'content_gaps': len(audit_results.get('content_analysis', {}).get('content_opportunities', [])),
                'onpage_score': audit_results.get('on_page_analysis', {}).get('optimization_score', 0),
                'performance_score': audit_results.get('performance_metrics', {}).get('performance_score', 0)
            }
            
            strategic_prompt = f"""
            Analyze this comprehensive SEO audit and provide strategic recommendations:
            
            AUDIT SUMMARY:
            - Technical SEO Score: {audit_summary['technical_score']}/100
            - Content Gaps Identified: {audit_summary['content_gaps']}
            - On-Page SEO Score: {audit_summary['onpage_score']}/100
            - Performance Score: {audit_summary['performance_score']}/100
            
            DETAILED FINDINGS:
            Technical Issues: {json.dumps(audit_results.get('technical_audit', {}), indent=2)[:1000]}
            Content Opportunities: {json.dumps(audit_results.get('content_analysis', {}), indent=2)[:1000]}
            
            Provide strategic recommendations in these categories:
            
            1. IMMEDIATE WINS (0-30 days):
               - Quick technical fixes with high impact
               - Content optimizations for existing pages
               - Critical performance improvements
               
            2. STRATEGIC INITIATIVES (1-3 months):
               - Content strategy development
               - Technical architecture improvements
               - Competitive positioning strategies
               
            3. LONG-TERM GROWTH (3-12 months):
               - Authority building strategies
               - Market expansion opportunities
               - Advanced SEO techniques
               
            4. RISK MITIGATION:
               - Technical vulnerabilities to address
               - Content gaps that competitors could exploit
               - Performance issues affecting user experience
               
            Provide specific, actionable recommendations with expected impact and effort estimates.
            """
            
            strategic_analysis = llm_text_gen(
                strategic_prompt,
                system_prompt="You are an enterprise SEO strategist with 10+ years of experience. Provide detailed, actionable recommendations based on comprehensive audit data."
            )
            
            return {
                'full_analysis': strategic_analysis,
                'immediate_wins': self._extract_immediate_wins(strategic_analysis),
                'strategic_initiatives': self._extract_strategic_initiatives(strategic_analysis),
                'long_term_growth': self._extract_long_term_growth(strategic_analysis),
                'risk_mitigation': self._extract_risk_mitigation(strategic_analysis)
            }
            
        except Exception as e:
            logger.error(f"Strategic analysis error: {str(e)}")
            return {'error': str(e)}
    
    async def _create_priority_action_plan(self, audit_results: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Create prioritized action plan from audit results."""
        try:
            action_plan = []
            
            # Extract recommendations from all analysis phases
            strategic_recs = audit_results.get('strategic_recommendations', {})
            
            # Immediate wins (High priority, low effort)
            immediate_wins = strategic_recs.get('immediate_wins', [])
            for win in immediate_wins[:5]:
                action_plan.append({
                    'category': 'Immediate Win',
                    'priority': 'Critical',
                    'effort': 'Low',
                    'timeframe': '0-30 days',
                    'action': win,
                    'expected_impact': 'High',
                    'source': 'Strategic Analysis'
                })
            
            # Technical fixes
            technical_issues = audit_results.get('technical_audit', {}).get('critical_issues', [])
            for issue in technical_issues[:3]:
                action_plan.append({
                    'category': 'Technical SEO',
                    'priority': 'High',
                    'effort': 'Medium',
                    'timeframe': '1-4 weeks',
                    'action': issue,
                    'expected_impact': 'High',
                    'source': 'Technical Audit'
                })
            
            # Content opportunities
            content_ops = audit_results.get('content_analysis', {}).get('content_opportunities', [])
            for opportunity in content_ops[:3]:
                action_plan.append({
                    'category': 'Content Strategy',
                    'priority': 'Medium',
                    'effort': 'High',
                    'timeframe': '2-8 weeks',
                    'action': opportunity,
                    'expected_impact': 'Medium',
                    'source': 'Content Analysis'
                })
            
            # Sort by priority and expected impact
            priority_order = {'Critical': 0, 'High': 1, 'Medium': 2, 'Low': 3}
            action_plan.sort(key=lambda x: priority_order.get(x['priority'], 4))
            
            return action_plan[:15]  # Top 15 actions
            
        except Exception as e:
            logger.error(f"Action plan creation error: {str(e)}")
            return []
    
    # Utility methods for analysis
    def _identify_critical_technical_issues(self, technical_results: Dict[str, Any]) -> List[str]:
        """Identify critical technical SEO issues."""
        critical_issues = []
        
        # Add logic to identify critical technical issues
        # This would analyze the technical_results and extract critical problems
        
        return critical_issues
    
    def _calculate_technical_score(self, technical_results: Dict[str, Any]) -> int:
        """Calculate technical SEO score."""
        # Implement scoring algorithm based on technical audit results
        return 75  # Placeholder
    
    def _prioritize_technical_fixes(self, technical_results: Dict[str, Any]) -> List[str]:
        """Prioritize technical fixes by impact and effort."""
        # Implement prioritization logic
        return ["Fix broken links", "Optimize images", "Improve page speed"]
    
    def _identify_content_opportunities(self, content_results: Dict[str, Any]) -> List[str]:
        """Identify top content opportunities."""
        # Extract content opportunities from gap analysis
        return ["Create FAQ content", "Develop comparison guides", "Write how-to articles"]
    
    def _develop_keyword_strategy(self, content_results: Dict[str, Any]) -> Dict[str, Any]:
        """Develop keyword strategy from content analysis."""
        return {
            'primary_keywords': [],
            'secondary_keywords': [],
            'long_tail_opportunities': [],
            'competitor_gaps': []
        }
    
    def _find_competitive_advantages(self, content_results: Dict[str, Any]) -> List[str]:
        """Find competitive advantages from analysis."""
        return ["Unique content angles", "Underserved niches", "Technical superiority"]
    
    def _calculate_onpage_score(self, onpage_data: Dict[str, Any]) -> int:
        """Calculate on-page SEO score."""
        return 80  # Placeholder
    
    def _analyze_meta_optimization(self, onpage_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze meta tag optimization."""
        return {'title_optimization': 'good', 'description_optimization': 'needs_work'}
    
    def _analyze_content_optimization(self, onpage_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze content optimization."""
        return {'keyword_density': 'optimal', 'content_length': 'adequate'}
    
    async def _analyze_core_web_vitals(self, website_url: str) -> Dict[str, Any]:
        """Analyze Core Web Vitals."""
        return {'lcp': 2.5, 'fid': 100, 'cls': 0.1}
    
    async def _analyze_loading_performance(self, website_url: str) -> Dict[str, Any]:
        """Analyze loading performance."""
        return {'ttfb': 200, 'fcp': 1.5, 'speed_index': 3.0}
    
    async def _analyze_mobile_optimization(self, website_url: str) -> Dict[str, Any]:
        """Analyze mobile optimization."""
        return {'mobile_friendly': True, 'responsive_design': True}
    
    def _calculate_performance_score(self, performance_results: Dict[str, Any]) -> int:
        """Calculate overall performance score."""
        return 85  # Placeholder
    
    def _extract_immediate_wins(self, analysis: str) -> List[str]:
        """Extract immediate wins from strategic analysis."""
        # Parse the AI analysis and extract immediate wins
        lines = analysis.split('\n')
        wins = []
        in_immediate_section = False
        
        for line in lines:
            if 'IMMEDIATE WINS' in line.upper():
                in_immediate_section = True
                continue
            elif 'STRATEGIC INITIATIVES' in line.upper():
                in_immediate_section = False
                continue
            
            if in_immediate_section and line.strip().startswith('-'):
                wins.append(line.strip().lstrip('- '))
        
        return wins[:5]
    
    def _extract_strategic_initiatives(self, analysis: str) -> List[str]:
        """Extract strategic initiatives from analysis."""
        # Similar extraction logic for strategic initiatives
        return ["Develop content hub", "Implement schema markup", "Build authority pages"]
    
    def _extract_long_term_growth(self, analysis: str) -> List[str]:
        """Extract long-term growth strategies."""
        return ["Market expansion", "Authority building", "Advanced technical SEO"]
    
    def _extract_risk_mitigation(self, analysis: str) -> List[str]:
        """Extract risk mitigation strategies."""
        return ["Fix technical vulnerabilities", "Address content gaps", "Improve performance"]

    def execute_content_strategy_workflow(self, business_info: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute comprehensive content strategy workflow using AI insights.
        
        Args:
            business_info: Business context and objectives
            
        Returns:
            Complete content strategy with implementation plan
        """
        try:
            st.info("🧠 Executing AI-powered content strategy workflow...")
            
            # Generate AI content strategy
            content_strategy = self.content_strategy_generator.generate_content_strategy(business_info)
            
            # If GSC data is available, enhance with search insights
            if business_info.get('gsc_site_url'):
                gsc_insights = self.gsc_analyzer.analyze_search_performance(
                    business_info['gsc_site_url'],
                    business_info.get('gsc_date_range', 90)
                )
                content_strategy['gsc_insights'] = gsc_insights
            
            # Generate SEO-optimized content recommendations
            seo_content_recs = self._generate_seo_content_recommendations(content_strategy)
            content_strategy['seo_recommendations'] = seo_content_recs
            
            return content_strategy
            
        except Exception as e:
            logger.error(f"Content strategy workflow error: {str(e)}")
            return {'error': str(e)}
    
    def execute_search_intelligence_workflow(self, site_url: str, date_range: int = 90) -> Dict[str, Any]:
        """
        Execute comprehensive search intelligence workflow using GSC data.
        
        Args:
            site_url: Website URL registered in GSC
            date_range: Analysis period in days
            
        Returns:
            Complete search intelligence analysis with actionable insights
        """
        try:
            st.info("📊 Executing search intelligence workflow...")
            
            # Analyze GSC performance
            gsc_analysis = self.gsc_analyzer.analyze_search_performance(site_url, date_range)
            
            # Enhance with technical SEO analysis
            technical_analysis = self.technical_crawler.crawl_and_analyze(site_url)
            gsc_analysis['technical_insights'] = technical_analysis
            
            # Generate content gap analysis based on GSC keywords
            if gsc_analysis.get('keyword_analysis'):
                keywords = [kw['keyword'] for kw in gsc_analysis['keyword_analysis'].get('high_volume_keywords', [])]
                content_gaps = self.gap_analyzer.analyze_content_gaps(
                    keywords[:10],  # Top 10 keywords
                    site_url
                )
                gsc_analysis['content_gap_analysis'] = content_gaps
            
            # Generate comprehensive recommendations
            search_recommendations = self._generate_search_intelligence_recommendations(gsc_analysis)
            gsc_analysis['comprehensive_recommendations'] = search_recommendations
            
            return gsc_analysis
            
        except Exception as e:
            logger.error(f"Search intelligence workflow error: {str(e)}")
            return {'error': str(e)}
    
    def _generate_seo_content_recommendations(self, content_strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Generate SEO-optimized content recommendations based on strategy."""
        try:
            content_pillars = content_strategy.get('content_pillars', [])
            
            seo_recommendations = {
                'keyword_optimization': [],
                'content_structure': [],
                'internal_linking': [],
                'technical_seo': []
            }
            
            for pillar in content_pillars:
                # Keyword optimization recommendations
                for keyword in pillar.get('target_keywords', []):
                    seo_recommendations['keyword_optimization'].append({
                        'pillar': pillar['name'],
                        'keyword': keyword,
                        'recommendation': f"Create comprehensive content targeting '{keyword}' with semantic variations",
                        'priority': 'High' if keyword in pillar['target_keywords'][:2] else 'Medium'
                    })
                
                # Content structure recommendations
                seo_recommendations['content_structure'].append({
                    'pillar': pillar['name'],
                    'recommendation': f"Create pillar page for {pillar['name']} with supporting cluster content",
                    'structure': 'Pillar + Cluster model'
                })
            
            # Internal linking strategy
            seo_recommendations['internal_linking'] = [
                "Link all cluster content to relevant pillar pages",
                "Create topic-based internal linking structure",
                "Use contextual anchor text with target keywords",
                "Implement breadcrumb navigation for topic clusters"
            ]
            
            # Technical SEO recommendations
            seo_recommendations['technical_seo'] = [
                "Optimize page speed for all content pages",
                "Implement structured data for articles",
                "Create XML sitemap sections for content categories",
                "Optimize images with descriptive alt text"
            ]
            
            return seo_recommendations
            
        except Exception as e:
            logger.error(f"SEO content recommendations error: {str(e)}")
            return {'error': str(e)}
    
    def _generate_search_intelligence_recommendations(self, gsc_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Generate comprehensive recommendations from search intelligence analysis."""
        try:
            recommendations = {
                'immediate_actions': [],
                'content_opportunities': [],
                'technical_improvements': [],
                'strategic_initiatives': []
            }
            
            # Extract content opportunities from GSC analysis
            content_opps = gsc_analysis.get('content_opportunities', [])
            for opp in content_opps[:5]:  # Top 5 opportunities
                recommendations['content_opportunities'].append({
                    'type': opp['type'],
                    'keyword': opp['keyword'],
                    'action': opp['opportunity'],
                    'priority': opp['priority'],
                    'estimated_impact': opp['potential_impact']
                })
            
            # Technical improvements from analysis
            technical_insights = gsc_analysis.get('technical_insights', {})
            if technical_insights.get('crawl_issues_indicators'):
                for issue in technical_insights['crawl_issues_indicators']:
                    recommendations['technical_improvements'].append({
                        'issue': issue,
                        'priority': 'High',
                        'category': 'Crawl & Indexing'
                    })
            
            # Immediate actions based on performance
            performance = gsc_analysis.get('performance_overview', {})
            if performance.get('avg_ctr', 0) < 2:
                recommendations['immediate_actions'].append({
                    'action': 'Improve meta descriptions and titles for better CTR',
                    'expected_impact': 'Increase CTR by 1-2%',
                    'timeline': '2-4 weeks'
                })
            
            if performance.get('avg_position', 0) > 10:
                recommendations['immediate_actions'].append({
                    'action': 'Focus on improving content quality for top keywords',
                    'expected_impact': 'Improve average position by 2-5 ranks',
                    'timeline': '4-8 weeks'
                })
            
            # Strategic initiatives
            competitive_analysis = gsc_analysis.get('competitive_analysis', {})
            if competitive_analysis.get('market_position') in ['Challenger', 'Emerging Player']:
                recommendations['strategic_initiatives'].append({
                    'initiative': 'Develop thought leadership content strategy',
                    'goal': 'Improve market position and brand authority',
                    'timeline': '3-6 months'
                })
            
            return recommendations
            
        except Exception as e:
            logger.error(f"Search intelligence recommendations error: {str(e)}")
            return {'error': str(e)}

def render_enterprise_seo_suite():
    """Render the Enterprise SEO Command Center interface."""
    
    st.set_page_config(
        page_title="Enterprise SEO Command Center",
        page_icon="🚀",
        layout="wide"
    )
    
    st.title("🚀 Enterprise SEO Command Center")
    st.markdown("**Unified AI-powered SEO suite orchestrating all tools into intelligent workflows**")
    
    # Initialize suite
    if 'enterprise_seo_suite' not in st.session_state:
        st.session_state.enterprise_seo_suite = EnterpriseSEOSuite()
    
    suite = st.session_state.enterprise_seo_suite
    
    # Workflow selection
    st.sidebar.header("🎯 SEO Workflow Selection")
    selected_workflow = st.sidebar.selectbox(
        "Choose Workflow",
        list(suite.workflow_templates.keys()),
        format_func=lambda x: suite.workflow_templates[x]
    )
    
    # Main workflow interface
    if selected_workflow == 'complete_audit':
        st.header("🔍 Complete Enterprise SEO Audit")
        render_complete_audit_interface(suite)
    elif selected_workflow == 'content_strategy':
        st.header("📊 Content Strategy Development")
        render_content_strategy_interface(suite)
    elif selected_workflow == 'technical_optimization':
        st.header("🔧 Technical SEO Optimization")
        render_technical_optimization_interface(suite)
    else:
        st.info(f"Workflow '{suite.workflow_templates[selected_workflow]}' is being developed.")

def render_complete_audit_interface(suite: EnterpriseSEOSuite):
    """Render the complete audit workflow interface."""
    
    # Input form
    with st.form("enterprise_audit_form"):
        col1, col2 = st.columns(2)
        
        with col1:
            website_url = st.text_input(
                "Website URL",
                value="https://example.com",
                help="Enter your website URL for comprehensive analysis"
            )
            
            target_keywords = st.text_area(
                "Target Keywords (one per line)",
                value="AI content creation\nSEO tools\ncontent optimization",
                help="Enter your primary keywords to optimize for"
            )
        
        with col2:
            competitors = st.text_area(
                "Competitor URLs (one per line)",
                value="https://jasper.ai\nhttps://copy.ai\nhttps://writesonic.com",
                help="Enter up to 5 competitor URLs for analysis"
            )
        
        submit_audit = st.form_submit_button("🚀 Start Complete SEO Audit", type="primary")
    
    # Process audit
    if submit_audit:
        if website_url and target_keywords:
            # Parse inputs
            keywords_list = [k.strip() for k in target_keywords.split('\n') if k.strip()]
            competitors_list = [c.strip() for c in competitors.split('\n') if c.strip()]
            
            # Run audit
            with st.spinner("🔍 Running comprehensive SEO audit..."):
                audit_results = asyncio.run(
                    suite.execute_complete_seo_audit(
                        website_url, competitors_list, keywords_list
                    )
                )
            
            if 'error' not in audit_results:
                st.success("✅ Enterprise SEO audit completed!")
                
                # Display results dashboard
                render_audit_results_dashboard(audit_results)
            else:
                st.error(f"❌ Audit failed: {audit_results['error']}")
        else:
            st.warning("⚠️ Please enter website URL and target keywords.")

def render_audit_results_dashboard(results: Dict[str, Any]):
    """Render comprehensive audit results dashboard."""
    
    # Priority Action Plan (Most Important)
    st.header("📋 Priority Action Plan")
    action_plan = results.get('priority_action_plan', [])
    
    if action_plan:
        # Display as interactive table
        df_actions = pd.DataFrame(action_plan)
        
        # Style the dataframe
        st.dataframe(
            df_actions,
            column_config={
                "category": "Category",
                "priority": st.column_config.SelectboxColumn(
                    "Priority",
                    options=["Critical", "High", "Medium", "Low"]
                ),
                "effort": "Effort Level",
                "timeframe": "Timeline",
                "action": "Action Required",
                "expected_impact": "Expected Impact"
            },
            hide_index=True,
            use_container_width=True
        )
    
    # Key Metrics Overview
    st.header("📊 SEO Health Dashboard")
    
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        technical_score = results.get('technical_audit', {}).get('performance_score', 0)
        st.metric("Technical SEO", f"{technical_score}/100", delta=None)
    
    with col2:
        onpage_score = results.get('on_page_analysis', {}).get('optimization_score', 0)
        st.metric("On-Page SEO", f"{onpage_score}/100", delta=None)
    
    with col3:
        performance_score = results.get('performance_metrics', {}).get('performance_score', 0)
        st.metric("Performance", f"{performance_score}/100", delta=None)
    
    with col4:
        content_gaps = len(results.get('content_analysis', {}).get('content_opportunities', []))
        st.metric("Content Opportunities", content_gaps, delta=None)
    
    # Detailed Analysis Sections
    tab1, tab2, tab3, tab4, tab5 = st.tabs([
        "🤖 Strategic Insights",
        "🔧 Technical Analysis", 
        "📊 Content Intelligence",
        "🔍 On-Page Analysis",
        "⚡ Performance Metrics"
    ])
    
    with tab1:
        strategic_recs = results.get('strategic_recommendations', {})
        if strategic_recs:
            st.subheader("AI-Powered Strategic Recommendations")
            
            # Immediate wins
            immediate_wins = strategic_recs.get('immediate_wins', [])
            if immediate_wins:
                st.markdown("#### 🚀 Immediate Wins (0-30 days)")
                for win in immediate_wins[:5]:
                    st.success(f"✅ {win}")
            
            # Strategic initiatives
            strategic_initiatives = strategic_recs.get('strategic_initiatives', [])
            if strategic_initiatives:
                st.markdown("#### 📈 Strategic Initiatives (1-3 months)")
                for initiative in strategic_initiatives[:3]:
                    st.info(f"📋 {initiative}")
            
            # Full analysis
            full_analysis = strategic_recs.get('full_analysis', '')
            if full_analysis:
                with st.expander("🧠 Complete Strategic Analysis"):
                    st.write(full_analysis)
    
    with tab2:
        technical_audit = results.get('technical_audit', {})
        if technical_audit:
            st.subheader("Technical SEO Analysis")
            
            critical_issues = technical_audit.get('critical_issues', [])
            if critical_issues:
                st.markdown("#### ⚠️ Critical Issues")
                for issue in critical_issues:
                    st.error(f"🚨 {issue}")
            
            priority_fixes = technical_audit.get('priority_fixes', [])
            if priority_fixes:
                st.markdown("#### 🔧 Priority Fixes")
                for fix in priority_fixes:
                    st.warning(f"🛠️ {fix}")
    
    with tab3:
        content_analysis = results.get('content_analysis', {})
        if content_analysis:
            st.subheader("Content Intelligence")
            
            content_opportunities = content_analysis.get('content_opportunities', [])
            if content_opportunities:
                st.markdown("#### 📝 Content Opportunities")
                for opportunity in content_opportunities[:5]:
                    st.info(f"💡 {opportunity}")
            
            competitive_advantages = content_analysis.get('competitive_advantages', [])
            if competitive_advantages:
                st.markdown("#### 🏆 Competitive Advantages")
                for advantage in competitive_advantages:
                    st.success(f"⭐ {advantage}")
    
    with tab4:
        onpage_analysis = results.get('on_page_analysis', {})
        if onpage_analysis:
            st.subheader("On-Page SEO Analysis")
            
            meta_optimization = onpage_analysis.get('meta_optimization', {})
            content_optimization = onpage_analysis.get('content_optimization', {})
            
            col1, col2 = st.columns(2)
            
            with col1:
                st.markdown("#### 🏷️ Meta Tag Optimization")
                st.json(meta_optimization)
            
            with col2:
                st.markdown("#### 📄 Content Optimization")
                st.json(content_optimization)
    
    with tab5:
        performance_metrics = results.get('performance_metrics', {})
        if performance_metrics:
            st.subheader("Performance Analysis")
            
            core_vitals = performance_metrics.get('core_web_vitals', {})
            loading_performance = performance_metrics.get('loading_performance', {})
            
            col1, col2 = st.columns(2)
            
            with col1:
                st.markdown("#### ⚡ Core Web Vitals")
                st.json(core_vitals)
            
            with col2:
                st.markdown("#### 🚀 Loading Performance")
                st.json(loading_performance)
    
    # Export functionality
    st.markdown("---")
    col1, col2, col3 = st.columns(3)
    
    with col1:
        if st.button("📥 Export Full Report", use_container_width=True):
            # Create downloadable report
            report_json = json.dumps(results, indent=2, default=str)
            st.download_button(
                label="Download JSON Report",
                data=report_json,
                file_name=f"seo_audit_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
                mime="application/json"
            )
    
    with col2:
        if st.button("📊 Export Action Plan", use_container_width=True):
            # Create CSV of action plan
            df_actions = pd.DataFrame(action_plan)
            csv = df_actions.to_csv(index=False)
            st.download_button(
                label="Download CSV Action Plan",
                data=csv,
                file_name=f"action_plan_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
                mime="text/csv"
            )
    
    with col3:
        if st.button("🔄 Schedule Follow-up Audit", use_container_width=True):
            st.info("Follow-up scheduling feature coming soon!")

def render_content_strategy_interface(suite: EnterpriseSEOSuite):
    """Render content strategy development interface."""
    st.info("🚧 Content Strategy Development workflow coming soon!")

def render_technical_optimization_interface(suite: EnterpriseSEOSuite):
    """Render technical optimization interface."""
    st.info("🚧 Technical SEO Optimization workflow coming soon!")


# Main execution
if __name__ == "__main__":
    render_enterprise_seo_suite()