feat: Initial commitHEADmain

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+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()
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