#!/usr/bin/env python3
"""
Focused Accuracy Test - Test first 10 questions for complete baseline
"""

import asyncio
import sys
from pathlib import Path
from datetime import datetime
import json

# Add parent directory to path for imports
sys.path.append(str(Path(__file__).parent.parent))

from tests.async_batch_processor import BatchQuestionProcessor
from gaia_web_loader import GAIAQuestionLoaderWeb


async def run_focused_accuracy_test():
    """Run focused accuracy test on first 10 questions"""
    
    print("🎯 FOCUSED GAIA ACCURACY TEST (First 10 Questions)")
    print("=" * 70)
    print(f"🕐 Start Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
    print()
    
    try:
        # Load questions
        print("📋 Loading GAIA questions...")
        loader = GAIAQuestionLoaderWeb()
        all_questions = loader.questions
        
        # Use first 10 questions for focused testing
        test_questions = all_questions[:10]
        
        print(f"✅ Selected {len(test_questions)} questions for focused testing")
        
        # Show question preview
        print(f"\n📋 Test Questions:")
        for i, q in enumerate(test_questions):
            task_id = q.get('task_id', 'unknown')
            question_preview = q.get('question', '')[:50] + "..."
            level = q.get('Level', 'Unknown')
            has_file = "📎" if q.get('file_name') else "📝"
            print(f"  {i+1:2d}. {task_id[:8]}... | L{level} | {has_file} | {question_preview}")
        
        # Initialize processor with optimized settings for focused test
        print(f"\n🚀 Initializing focused batch processor...")
        processor = BatchQuestionProcessor(
            max_concurrent=2,  # Lower concurrency for stability  
            question_timeout=600,  # 10 minutes per question
            progress_interval=10   # Progress updates every 10 seconds
        )
        
        print(f"⚙️  Focused Test Configuration:")
        print(f"   - Questions: {len(test_questions)}")
        print(f"   - Max Concurrent: {processor.max_concurrent}")
        print(f"   - Question Timeout: {processor.question_timeout}s")
        print(f"   - Expected Duration: ~{len(test_questions) * 2} minutes")
        
        # Process questions
        print(f"\n🔄 Starting focused accuracy test...")
        start_time = datetime.now()
        results = await processor.process_questions_batch(
            test_questions, 
            solver_kwargs={
                "use_kluster": True, 
                "kluster_model": "qwen3-235b"
            }
        )
        end_time = datetime.now()
        
        # Analyze results
        print(f"\n" + "=" * 70)
        print(f"🏁 FOCUSED TEST RESULTS")
        print(f"=" * 70)
        
        duration = (end_time - start_time).total_seconds()
        accuracy = results["accuracy_metrics"]["accuracy_rate"]
        success = results["accuracy_metrics"]["success_rate"]
        
        print(f"⏱️  Total Duration: {int(duration // 60)}m {int(duration % 60)}s")
        print(f"✅ Accuracy: {accuracy:.1%} ({results['accuracy_metrics']['correct_answers']}/{results['completed_questions']})")
        print(f"🎯 Success Rate: {success:.1%}")
        print(f"⚡ Avg per Question: {results['performance_metrics']['average_duration']:.1f}s")
        
        # Detailed question-by-question results
        print(f"\n📊 QUESTION-BY-QUESTION RESULTS:")
        for i, result in enumerate(results["detailed_results"]):
            status_icon = "✅" if result.status == "CORRECT" else "🟡" if result.status == "PARTIAL" else "❌"
            task_id = result.task_id[:8]
            classification = result.classification
            duration = result.total_duration
            accuracy_score = result.accuracy_score
            
            print(f"  {i+1:2d}. {status_icon} {task_id}... | {classification:12} | {accuracy_score:.0%} | {duration:5.1f}s")
            
            if result.status != "CORRECT":
                print(f"      Expected: {result.expected_answer}")
                print(f"      Got:      {result.our_answer}")
                if result.error_type:
                    print(f"      Error:    {result.error_type}")
        
        # Classification analysis
        print(f"\n🎯 CLASSIFICATION PERFORMANCE:")
        classification_stats = {}
        
        for result in results["detailed_results"]:
            classification = result.classification
            if classification not in classification_stats:
                classification_stats[classification] = {
                    'total': 0, 'correct': 0, 'partial': 0, 'durations': []
                }
            
            classification_stats[classification]['total'] += 1
            classification_stats[classification]['durations'].append(result.total_duration)
            
            if result.status == 'CORRECT':
                classification_stats[classification]['correct'] += 1
            elif result.status == 'PARTIAL':
                classification_stats[classification]['partial'] += 1
        
        for classification, stats in sorted(classification_stats.items()):
            total = stats['total']
            correct = stats['correct']
            partial = stats['partial']
            accuracy_rate = correct / total if total > 0 else 0
            success_rate = (correct + partial) / total if total > 0 else 0
            avg_duration = sum(stats['durations']) / len(stats['durations']) if stats['durations'] else 0
            
            print(f"  {classification:15} | {accuracy_rate:.1%} acc | {success_rate:.1%} success | {total:2d} questions | {avg_duration:5.1f}s avg")
        
        # Assessment and recommendations
        print(f"\n🔧 ASSESSMENT:")
        if accuracy >= 0.9:
            print(f"  🏆 EXCELLENT: {accuracy:.1%} accuracy! System performing very well.")
        elif accuracy >= 0.7:
            print(f"  ✅ TARGET MET: {accuracy:.1%} accuracy achieves 70%+ goal!")
        elif accuracy >= 0.5:
            print(f"  🔧 GOOD PROGRESS: {accuracy:.1%} accuracy, approaching target.")
        else:
            print(f"  🚨 NEEDS IMPROVEMENT: {accuracy:.1%} accuracy requires attention.")
        
        # Save results
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        results_file = f"logs/focused_accuracy_test_{timestamp}.json"
        
        with open(results_file, 'w') as f:
            json.dump({
                'test_metadata': {
                    'timestamp': timestamp,
                    'test_type': 'focused_10_questions',
                    'duration_seconds': duration,
                    'questions_tested': len(test_questions),
                    'configuration': {
                        'max_concurrent': processor.max_concurrent,
                        'question_timeout': processor.question_timeout,
                        'model': 'qwen3-235b'
                    }
                },
                'results': {
                    'accuracy_rate': accuracy,
                    'success_rate': success,
                    'classification_stats': classification_stats,
                    'detailed_results': [
                        {
                            'question_number': i+1,
                            'task_id': r.task_id,
                            'classification': r.classification,
                            'status': r.status,
                            'accuracy_score': r.accuracy_score,
                            'our_answer': r.our_answer,
                            'expected_answer': r.expected_answer,
                            'duration': r.total_duration,
                            'error_type': r.error_type
                        } for i, r in enumerate(results['detailed_results'])
                    ]
                }
            }, f, indent=2)
        
        print(f"\n📁 Results saved to: {results_file}")
        
        return results
        
    except Exception as e:
        print(f"❌ Focused test failed: {e}")
        import traceback
        traceback.print_exc()
        return None


async def main():
    """Run the focused accuracy test"""
    results = await run_focused_accuracy_test()
    
    if results:
        accuracy = results["accuracy_metrics"]["accuracy_rate"]
        print(f"\n🎉 Focused accuracy test completed!")
        print(f"📊 Final Accuracy: {accuracy:.1%}")
        
        if accuracy >= 0.7:
            print(f"🎯 TARGET ACHIEVED: 70%+ accuracy reached!")
            print(f"🚀 Ready for comprehensive full-scale testing!")
        else:
            gap = 0.7 - accuracy
            print(f"🔧 GAP TO TARGET: {gap:.1%} improvement needed")


if __name__ == "__main__":
    asyncio.run(main())