comprehensive_test.py

"""
Comprehensive test script for Gap Analyzer to validate all features and gap detection.
Tests the complete pipeline: text analysis → gap extraction → web search → validation → results
"""

import asyncio
import json
import time
from datetime import datetime
from app.services.gap_analyzer.orchestrator import GapAnalysisOrchestrator

# Multiple test papers with different characteristics to test various scenarios
TEST_PAPERS = {
    "computer_vision": {
        "title": "Computer Vision for Autonomous Vehicles",
        "text": """
        Title: Real-Time Object Detection in Autonomous Vehicles: Challenges and Solutions

        Abstract: Autonomous vehicles rely heavily on computer vision systems for real-time object detection and scene understanding. This paper presents a comprehensive analysis of current object detection methods and their performance in automotive scenarios. We evaluate state-of-the-art deep learning models including YOLO, SSD, and transformer-based detectors on automotive datasets.

        Introduction:
        Autonomous driving requires robust perception systems capable of detecting and classifying objects in real-time. Computer vision forms the backbone of these systems, enabling vehicles to understand their environment and make safe driving decisions. Current challenges include handling diverse weather conditions, varying lighting scenarios, and computational constraints.

        Methods:
        We implemented and evaluated several object detection architectures:
        - YOLOv5 and YOLOv8 for real-time detection
        - Faster R-CNN for high-accuracy scenarios
        - Vision Transformers (ViT) for enhanced feature extraction
        - Multi-modal fusion combining camera and LiDAR data

        Key Findings:
        1. Transformer-based models achieve 94.2% mAP on KITTI dataset
        2. Real-time performance requires specialized hardware acceleration
        3. Multi-modal approaches improve robustness by 15-20%
        4. Edge case handling remains a significant challenge

        Limitations:
        Despite promising results, several critical limitations persist:
        1. Poor performance in adverse weather conditions (fog, heavy rain, snow)
        2. Computational complexity limits real-time deployment on edge devices
        3. Limited generalization across different geographical regions and traffic patterns
        4. Difficulty detecting small or partially occluded objects
        5. Lack of robustness to adversarial attacks on vision systems
        6. Insufficient training data for rare but critical scenarios (emergency vehicles, road work)
        7. Integration challenges with existing automotive safety systems

        Future Work:
        Critical research directions for advancing autonomous vehicle perception:
        1. Development of weather-robust vision algorithms using synthetic data augmentation
        2. Efficient model compression techniques for real-time edge deployment
        3. Cross-domain adaptation methods for global deployment
        4. Advanced sensor fusion algorithms combining vision, radar, and LiDAR
        5. Adversarial robustness improvements for safety-critical applications
        6. Synthetic data generation for rare scenario training
        7. Standardized evaluation protocols for automotive vision systems

        Conclusion:
        While computer vision has made remarkable progress in autonomous vehicles, significant challenges remain in achieving the reliability and robustness required for full autonomy. Future research must address these limitations to enable safe deployment.
        """
    },
    
    "nlp_limitations": {
        "title": "Natural Language Processing with Limited Data",
        "text": """
        Title: Few-Shot Learning in Natural Language Processing: Current State and Open Challenges

        Abstract: Few-shot learning has emerged as a critical capability for NLP systems to quickly adapt to new tasks with minimal training data. This paper examines current approaches including meta-learning, prompt engineering, and transfer learning, highlighting their strengths and fundamental limitations.

        Introduction:
        The ability to learn from few examples is crucial for practical NLP deployment, especially for specialized domains or low-resource languages. While large language models show impressive few-shot capabilities, many challenges remain unsolved.

        Methods:
        We evaluated several few-shot learning approaches:
        - Meta-learning algorithms (MAML, Prototypical Networks)
        - In-context learning with GPT-3/4
        - Parameter-efficient fine-tuning (LoRA, Adapters)
        - Prompt engineering and chain-of-thought reasoning

        Results:
        Our analysis reveals significant performance gains but also exposes critical gaps:
        - GPT-4 achieves 78% accuracy on 5-shot text classification
        - Meta-learning approaches show 65% accuracy on average
        - Performance degrades significantly on out-of-domain examples

        Limitations:
        Current few-shot learning in NLP faces several unresolved challenges:
        1. Catastrophic forgetting when adapting to new tasks
        2. Poor calibration and overconfidence in uncertain predictions
        3. Inability to handle truly novel semantic concepts not seen during pretraining
        4. Lack of compositionality in understanding complex multi-step reasoning
        5. Brittleness to prompt variations and example selection bias
        6. Limited ability to learn new linguistic structures or syntax patterns
        7. Difficulty in maintaining consistency across longer dialogues or documents

        Future Work:
        Essential research directions for advancing few-shot NLP:
        1. Developing memory-augmented architectures that prevent catastrophic forgetting
        2. Improved uncertainty quantification and calibration methods
        3. Novel approaches for learning compositional representations
        4. Better prompt optimization and example selection strategies
        5. Continual learning frameworks that accumulate knowledge over time
        6. Cross-lingual few-shot learning for truly low-resource languages
        7. Integration of symbolic reasoning with neural few-shot learning

        Discussion:
        The gap between human-like few-shot learning and current AI systems remains substantial. Humans can learn new concepts from single examples and generalize broadly, while current systems require careful engineering and still fail on edge cases.

        Conclusion:
        Few-shot learning in NLP has made impressive progress but fundamental challenges in generalization, reasoning, and knowledge retention must be addressed for practical deployment in dynamic real-world scenarios.
        """
    },

    "robotics_gaps": {
        "title": "Robotics and Human-Robot Interaction",
        "text": """
        Title: Advances in Human-Robot Collaboration: From Industrial to Social Robotics

        Abstract: Human-robot collaboration has evolved from simple industrial automation to sophisticated social interactions. This survey examines recent advances in collaborative robotics, focusing on safety, communication, and shared autonomy. We identify key technological gaps that limit current deployment and propose research directions.

        Introduction:
        The future of robotics lies in seamless human-robot collaboration across diverse environments. From manufacturing floors to home assistance, robots must understand, predict, and respond to human behavior while ensuring safety and efficiency.

        Technical Contributions:
        1. Novel safety protocols for close human-robot interaction
        2. Multi-modal communication interfaces combining speech, gesture, and gaze
        3. Adaptive behavior algorithms that learn from human preferences
        4. Shared autonomy frameworks for complex manipulation tasks

        Methods:
        Our research employed several key technologies:
        - Computer vision for human pose and intention recognition
        - Natural language processing for verbal command understanding
        - Reinforcement learning for adaptive behavior
        - Force/torque sensing for safe physical interaction
        - Probabilistic models for human behavior prediction

        Experimental Results:
        Extensive experiments demonstrate significant improvements:
        - 89% success rate in collaborative assembly tasks
        - 67% reduction in task completion time with robot assistance
        - 94% user satisfaction in social interaction scenarios
        - Zero safety incidents in 500+ hours of testing

        Current Limitations:
        Despite progress, fundamental limitations constrain real-world deployment:
        1. Inability to understand complex human emotions and social cues
        2. Limited dexterity for fine manipulation tasks requiring human-level precision
        3. Poor adaptation to unstructured and dynamically changing environments
        4. Lack of common sense reasoning for everyday task planning
        5. Insufficient robustness to unexpected human behavior or system failures
        6. Limited long-term memory and relationship building capabilities
        7. High computational requirements limiting real-time responsiveness
        8. Difficulty in learning from demonstration for complex multi-step tasks

        Open Research Challenges:
        Critical areas requiring breakthrough research:
        1. Development of artificial empathy and emotional intelligence for robots
        2. Advanced manipulation skills approaching human dexterity levels
        3. Robust perception and navigation in cluttered, dynamic environments
        4. Integration of common sense knowledge for intuitive task execution
        5. Fail-safe mechanisms and graceful degradation under uncertainty
        6. Personalized learning and long-term relationship adaptation
        7. Edge computing solutions for real-time collaborative decision making
        8. Standardized evaluation frameworks for human-robot interaction quality

        Safety and Ethical Considerations:
        The deployment of collaborative robots raises important questions about responsibility, trust, and the changing nature of work. Future systems must address these concerns proactively.

        Conclusion:
        Human-robot collaboration shows immense promise but requires fundamental advances in perception, reasoning, and social intelligence. Addressing these challenges will unlock transformative applications across numerous domains.
        """
    }
}

class ComprehensiveGapTester:
    def __init__(self):
        self.results = {
            "test_summary": {},
            "detailed_results": {},
            "performance_metrics": {},
            "gap_analysis": {}
        }
        
    async def run_all_tests(self):
        """Run comprehensive tests on all paper types"""
        print("🧪 COMPREHENSIVE GAP ANALYZER TEST SUITE")
        print("=" * 80)
        print(f"📅 Test Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        print(f"📝 Testing {len(TEST_PAPERS)} different paper types")
        print("=" * 80)
        
        overall_start = time.time()
        
        for paper_type, paper_data in TEST_PAPERS.items():
            print(f"\n🔬 TESTING: {paper_type.upper()}")
            print("-" * 60)
            
            try:
                result = await self.test_single_paper(paper_type, paper_data)
                self.results["detailed_results"][paper_type] = result
                
                # Print immediate results
                self.print_test_results(paper_type, result)
                
            except Exception as e:
                print(f"❌ ERROR in {paper_type}: {str(e)}")
                self.results["detailed_results"][paper_type] = {"error": str(e)}
                import traceback
                traceback.print_exc()
            
            print("-" * 60)
        
        # Generate comprehensive summary
        total_time = time.time() - overall_start
        await self.generate_final_report(total_time)
        
    async def test_single_paper(self, paper_type, paper_data):
        """Test gap analysis on a single paper"""
        start_time = time.time()
        
        # Initialize orchestrator
        orchestrator = GapAnalysisOrchestrator()
        
        # Run gap analysis with minimal scope for fast testing
        result = await orchestrator.analyze_research_gaps_from_text(
            paper_text=paper_data["text"],
            paper_id=f"{paper_type}_test",
            max_papers=3,  # Very small for fast testing
            validation_threshold=1  # Fast validation
        )
        
        processing_time = time.time() - start_time
        
        # Analyze results
        analysis = {
            "execution_time": processing_time,
            "success": len(result.validated_gaps) > 0,
            "gaps_found": len(result.validated_gaps),
            "gaps_discovered": result.process_metadata.gaps_discovered,
            "gaps_eliminated": result.process_metadata.gaps_eliminated,
            "papers_analyzed": result.process_metadata.total_papers_analyzed,
            "search_queries": result.process_metadata.search_queries_executed,
            "validation_attempts": result.process_metadata.validation_attempts,
            "gap_details": [],
            "full_result": result
        }
        
        # Extract gap details
        for gap in result.validated_gaps:
            gap_detail = {
                "title": gap.gap_title,
                "category": gap.category,
                "description_length": len(gap.description),
                "has_approaches": len(gap.suggested_approaches) > 0,
                "has_impact": len(gap.potential_impact) > 10
            }
            analysis["gap_details"].append(gap_detail)
        
        return analysis
    
    def print_test_results(self, paper_type, result):
        """Print results for a single test"""
        if "error" in result:
            print(f"❌ FAILED: {result['error']}")
            return
            
        print(f"✅ SUCCESS: {result['success']}")
        print(f"⏱️  Execution Time: {result['execution_time']:.2f} seconds")
        print(f"📄 Papers Analyzed: {result['papers_analyzed']}")
        print(f"🔍 Gaps Discovered: {result['gaps_discovered']}")
        print(f"✅ Gaps Validated: {result['gaps_found']}")
        print(f"❌ Gaps Eliminated: {result['gaps_eliminated']}")
        print(f"🔎 Search Queries: {result['search_queries']}")
        print(f"🧪 Validation Attempts: {result['validation_attempts']}")
        
        if result["gaps_found"] > 0:
            print(f"\n🎯 DISCOVERED GAPS:")
            for i, gap in enumerate(result["gap_details"], 1):
                print(f"   {i}. {gap['title']} [{gap['category']}]")
                print(f"      📝 Description: {gap['description_length']} chars")
                print(f"      🔬 Has Approaches: {gap['has_approaches']}")
                print(f"      🎯 Has Impact: {gap['has_impact']}")
        else:
            print("⚠️  No validated gaps found")
    
    async def generate_final_report(self, total_time):
        """Generate comprehensive final report"""
        print("\n" + "=" * 80)
        print("📊 FINAL TEST REPORT")
        print("=" * 80)
        
        # Overall statistics
        successful_tests = sum(1 for r in self.results["detailed_results"].values() 
                              if "error" not in r and r.get("success", False))
        total_tests = len(self.results["detailed_results"])
        total_gaps_found = sum(r.get("gaps_found", 0) for r in self.results["detailed_results"].values() 
                              if "error" not in r)
        total_papers_analyzed = sum(r.get("papers_analyzed", 0) for r in self.results["detailed_results"].values() 
                                   if "error" not in r)
        
        print(f"🧪 Tests Run: {total_tests}")
        print(f"✅ Tests Passed: {successful_tests}")
        print(f"❌ Tests Failed: {total_tests - successful_tests}")
        print(f"📈 Success Rate: {(successful_tests/total_tests)*100:.1f}%")
        print(f"⏱️  Total Time: {total_time:.2f} seconds")
        print(f"🎯 Total Gaps Found: {total_gaps_found}")
        print(f"📄 Total Papers Analyzed: {total_papers_analyzed}")
        
        # Feature validation
        print(f"\n🔧 FEATURE VALIDATION:")
        features = {
            "Text Analysis": all(r.get("papers_analyzed", 0) > 0 for r in self.results["detailed_results"].values() if "error" not in r),
            "Gap Extraction": all(r.get("gaps_discovered", 0) > 0 for r in self.results["detailed_results"].values() if "error" not in r),
            "Web Search": all(r.get("search_queries", 0) > 0 for r in self.results["detailed_results"].values() if "error" not in r),
            "Gap Validation": all(r.get("validation_attempts", 0) > 0 for r in self.results["detailed_results"].values() if "error" not in r),
            "Gap Discovery": total_gaps_found > 0
        }
        
        for feature, working in features.items():
            status = "✅" if working else "❌"
            print(f"   {status} {feature}")
        
        # Detailed breakdown by paper type
        print(f"\n📋 DETAILED BREAKDOWN:")
        for paper_type, result in self.results["detailed_results"].items():
            if "error" in result:
                print(f"   ❌ {paper_type}: FAILED - {result['error']}")
            else:
                print(f"   ✅ {paper_type}: {result['gaps_found']} gaps, {result['execution_time']:.1f}s")
        
        # Save detailed results
        with open("comprehensive_test_results.json", "w") as f:
            # Convert result objects to dicts for JSON serialization
            serializable_results = {}
            for paper_type, result in self.results["detailed_results"].items():
                if "error" not in result and "full_result" in result:
                    serializable_result = result.copy()
                    serializable_result["full_result"] = result["full_result"].dict()
                    serializable_results[paper_type] = serializable_result
                else:
                    serializable_results[paper_type] = result
            
            json.dump({
                "test_summary": {
                    "total_tests": total_tests,
                    "successful_tests": successful_tests,
                    "success_rate": (successful_tests/total_tests)*100,
                    "total_time": total_time,
                    "total_gaps_found": total_gaps_found,
                    "features_working": features
                },
                "detailed_results": serializable_results
            }, f, indent=2, default=str)
        
        print(f"\n💾 Detailed results saved to: comprehensive_test_results.json")
        
        # Final assessment
        print(f"\n🎯 ASSESSMENT:")
        if successful_tests == total_tests and total_gaps_found > 0:
            print("   🌟 EXCELLENT: All tests passed and gaps were discovered!")
            print("   📈 The Gap Analyzer is working correctly across all scenarios.")
        elif successful_tests > 0 and total_gaps_found > 0:
            print("   ✅ GOOD: Most tests passed and gaps were discovered.")
            print("   🔧 Some issues may need attention.")
        elif successful_tests > 0:
            print("   ⚠️  PARTIAL: Tests ran but few/no gaps discovered.")
            print("   🔍 Gap detection logic may need tuning.")
        else:
            print("   ❌ CRITICAL: Major issues detected.")
            print("   🚨 System requires debugging before deployment.")
        
        print("=" * 80)

async def main():
    """Run the comprehensive test suite"""
    tester = ComprehensiveGapTester()
    await tester.run_all_tests()

if __name__ == "__main__":
    # Set up proper event loop for testing
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\n⚠️  Test interrupted by user")
    except Exception as e:
        print(f"\n❌ Test failed with error: {str(e)}")
        import traceback
        traceback.print_exc()