import json
from artifact_optimizer import ArtifactOptimizer

def run_demo():
    """Demonstrate the artifact optimizer with different priority scenarios"""
    
    # Initialize optimizer
    optimizer = ArtifactOptimizer()
    optimizer.load_data('data.json', 'characters.json')
    
    print("="*80)
    print("🎮 GENSHIN ARTIFACT OPTIMIZER DEMO")
    print("="*80)
    print(f"📦 Total artifacts available: {len(optimizer.artifacts)}")
    print(f"👥 Characters available: {', '.join(optimizer.character_data.keys())}")
    
    # Define different priority scenarios
    scenarios = [
        {
            "name": "DPS Priority",
            "description": "Prioritize main DPS characters first",
            "priority": ["Fischl", "Chiori", "Furina", "Escoffier"]
        },
        {
            "name": "Support Priority", 
            "description": "Prioritize support characters first",
            "priority": ["Furina", "Escoffier", "Fischl", "Chiori"]
        },
        {
            "name": "ER Demanding First",
            "description": "Prioritize characters with high ER requirements",
            "priority": ["Escoffier", "Furina", "Fischl", "Chiori"]
        },
        {
            "name": "Reverse Order",
            "description": "Completely reverse priority order",
            "priority": ["Chiori", "Fischl", "Escoffier", "Furina"]
        }
    ]
    
    # Store results for comparison
    all_results = {}
    
    # Run each scenario
    for i, scenario in enumerate(scenarios, 1):
        print(f"\n{'='*60}")
        print(f"SCENARIO {i}: {scenario['name']}")
        print(f"{'='*60}")
        print(f"📝 {scenario['description']}")
        print(f"🎯 Priority: {' -> '.join(scenario['priority'])}")
        
        # Run optimization
        results = optimizer.optimize_builds(scenario['priority'])
        all_results[scenario['name']] = results
        
        # Quick summary
        successful = sum(1 for data in results.values() if data['build'] is not None)
        if successful > 0:
            avg_score = sum(data['average_score'] for data in results.values() if data['build']) / successful
            er_compliant = sum(1 for data in results.values() 
                             if data['build'] and data['er_info']['meets_requirement'])
            
            print(f"✅ Successful builds: {successful}/4")
            print(f"📊 Average quality: {avg_score:.1f}/100")
            print(f"⚡ ER compliant: {er_compliant}/4")
            
            # Show individual scores
            print("\n📋 Individual Scores:")
            for char, data in results.items():
                if data['build']:
                    er_status = "✅" if data['er_info']['meets_requirement'] else "⚠️"
                    print(f"   {char}: {data['average_score']:.1f}/100 {er_status}")
                else:
                    print(f"   {char}: No build found ❌")
        else:
            print("❌ No successful builds found!")
    
    # Comparison analysis
    print(f"\n{'='*80}")
    print("📊 SCENARIO COMPARISON")
    print(f"{'='*80}")
    
    comparison_data = []
    for scenario_name, results in all_results.items():
        successful = sum(1 for data in results.values() if data['build'] is not None)
        if successful > 0:
            avg_score = sum(data['average_score'] for data in results.values() if data['build']) / successful
            er_compliant = sum(1 for data in results.values() 
                             if data['build'] and data['er_info']['meets_requirement'])
            
            comparison_data.append({
                'scenario': scenario_name,
                'successful_builds': successful,
                'average_score': avg_score,
                'er_compliant': er_compliant,
                'total_score': successful * avg_score  # Weighted score
            })
    
    # Sort by total weighted score
    comparison_data.sort(key=lambda x: x['total_score'], reverse=True)
    
    print("🏆 Ranking (by total weighted score):")
    for i, data in enumerate(comparison_data, 1):
        print(f"   {i}. {data['scenario']}")
        print(f"      Builds: {data['successful_builds']}/4 | "
              f"Avg Score: {data['average_score']:.1f} | "
              f"ER Compliant: {data['er_compliant']}/4 | "
              f"Total: {data['total_score']:.1f}")
    
    # Character-specific analysis
    print(f"\n📈 CHARACTER PERFORMANCE ACROSS SCENARIOS")
    print(f"{'='*60}")
    
    characters = list(optimizer.character_data.keys())
    for char in characters:
        print(f"\n🔍 {char}:")
        char_scores = []
        for scenario_name, results in all_results.items():
            if results[char]['build']:
                score = results[char]['average_score']
                er_ok = results[char]['er_info']['meets_requirement']
                char_scores.append(score)
                status = "✅" if er_ok else "⚠️"
                print(f"   {scenario_name}: {score:.1f}/100 {status}")
            else:
                print(f"   {scenario_name}: No build ❌")
        
        if char_scores:
            print(f"   📊 Score range: {min(char_scores):.1f} - {max(char_scores):.1f}")
            print(f"   📊 Average: {sum(char_scores)/len(char_scores):.1f}")
    
    # Priority impact analysis
    print(f"\n🎯 PRIORITY IMPACT ANALYSIS")
    print(f"{'='*60}")
    
    print("Key Findings:")
    
    # Find which characters benefit most from high priority
    char_variance = {}
    for char in characters:
        scores = []
        for results in all_results.values():
            if results[char]['build']:
                scores.append(results[char]['average_score'])
        
        if len(scores) > 1:
            variance = max(scores) - min(scores)
            char_variance[char] = variance
    
    if char_variance:
        most_affected = max(char_variance.items(), key=lambda x: x[1])
        least_affected = min(char_variance.items(), key=lambda x: x[1])
        
        print(f"• Most affected by priority: {most_affected[0]} (variance: {most_affected[1]:.1f})")
        print(f"• Least affected by priority: {least_affected[0]} (variance: {least_affected[1]:.1f})")
    
    # Save detailed results
    with open('demo_results.json', 'w') as f:
        json.dump(all_results, f, indent=2, default=str)
    
    print(f"\n💾 Detailed results saved to 'demo_results.json'")
    print(f"\n🎉 Demo completed! Try different scenarios to see how priority affects builds.")

if __name__ == "__main__":
    run_demo()