test_gap_analyzer.py

"""
Simple test script for the Gap Analyzer - tests with sample paper text
to validate the entire workflow without needing URL extraction.
"""

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

# Sample paper text to test with
SAMPLE_PAPER_TEXT = """
Title: Deep Learning for Natural Language Processing: A Comprehensive Survey

Abstract: Natural Language Processing (NLP) has witnessed tremendous progress with the advent of deep learning techniques. This survey provides a comprehensive overview of deep learning approaches in NLP, covering major architectures, applications, and recent advances. We examine the evolution from traditional methods to transformer-based models and their impact on various NLP tasks.

Introduction: 
Deep learning has revolutionized Natural Language Processing by enabling end-to-end learning and achieving state-of-the-art results across numerous tasks. From early word embeddings to modern transformer architectures, the field has evolved rapidly. This paper surveys the landscape of deep learning in NLP, providing insights into current methodologies and future directions.

Key Contributions:
1. Comprehensive analysis of deep learning architectures for NLP
2. Evaluation of transformer models across multiple tasks
3. Investigation of transfer learning techniques
4. Analysis of computational efficiency in large language models

Methods:
We employ various deep learning architectures including:
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks
- Convolutional Neural Networks (CNNs) for text classification
- Transformer architectures including BERT, GPT, and T5
- Transfer learning and fine-tuning techniques
- Multi-task learning approaches

Results:
Our experiments demonstrate significant improvements in:
- Text classification accuracy (95.2% on benchmark datasets)
- Named Entity Recognition performance (F1 score of 0.94)
- Machine Translation quality (BLEU score improvements of 15-20%)
- Question Answering systems (exact match accuracy of 87.3%)

The transformer-based models consistently outperform traditional approaches across all evaluated tasks.

Limitations:
Despite the remarkable progress, several challenges remain:
1. Computational complexity and resource requirements for large models
2. Limited interpretability of deep learning models in NLP
3. Data efficiency issues requiring large amounts of labeled data
4. Domain adaptation challenges when transferring across different text types
5. Bias and fairness concerns in pre-trained language models
6. Difficulty in handling low-resource languages
7. Lack of robust evaluation metrics for generation tasks

Future Work:
Several promising research directions emerge:
1. Development of more efficient architectures to reduce computational costs
2. Improved techniques for model interpretability and explainability
3. Few-shot and zero-shot learning capabilities for low-resource scenarios
4. Better methods for handling multimodal data (text + images/audio)
5. Advanced techniques for bias mitigation and fairness
6. Cross-lingual transfer learning improvements
7. Integration of symbolic reasoning with neural approaches
8. Development of more comprehensive evaluation frameworks

Discussion:
The rapid advancement in deep learning for NLP has opened new possibilities but also introduced new challenges. While current models achieve impressive performance, questions about sustainability, interpretability, and ethical considerations remain paramount.

Conclusion:
Deep learning has fundamentally transformed Natural Language Processing, enabling unprecedented performance across diverse tasks. However, addressing computational efficiency, interpretability, and ethical concerns will be crucial for the field's continued advancement. Future research should focus on developing more efficient, interpretable, and fair NLP systems.

References: [List of 150+ references...]
"""

async def test_gap_analyzer():
    """Test the gap analyzer with sample paper text"""
    
    print("🚀 Starting Gap Analysis Test...")
    print("=" * 80)
    
    try:
        # Initialize the orchestrator
        orchestrator = GapAnalysisOrchestrator()
        
        # Run gap analysis from text (this does REAL web searches!)
        result = await orchestrator.analyze_research_gaps_from_text(
            paper_text=SAMPLE_PAPER_TEXT,
            paper_id="sample_nlp_survey",
            max_papers=8,  # Increased for better gap discovery
            validation_threshold=3  # More reasonable - gaps need 3 validation attempts to be eliminated
        )
        
        print(f"✅ Analysis completed successfully!")
        print(f"📊 Request ID: {result.request_id}")
        print(f"⏱️  Processing Time: {result.process_metadata.processing_time_seconds:.2f} seconds")
        print(f"📄 Papers Analyzed: {result.process_metadata.total_papers_analyzed}")
        print(f"🔍 Gaps Discovered: {result.process_metadata.gaps_discovered}")
        print(f"✅ Gaps Validated: {result.process_metadata.gaps_validated}")
        print(f"❌ Gaps Eliminated: {result.process_metadata.gaps_eliminated}")
        print(f"🔎 Search Queries: {result.process_metadata.search_queries_executed}")
        print()
        
        print("🎯 VALIDATED RESEARCH GAPS:")
        print("=" * 80)
        
        if result.validated_gaps:
            for i, gap in enumerate(result.validated_gaps, 1):
                print(f"\n{i}. {gap.gap_title}")
                print(f"   📝 Description: {gap.description}")
                print(f"   🏷️  Category: {gap.category}")
                print(f"   📚 Source: {gap.source_paper_title}")
                print(f"   ✅ Evidence: {gap.validation_evidence}")
                print(f"   🎯 Impact: {gap.potential_impact}")
                print(f"   🔬 Approaches: {', '.join(gap.suggested_approaches[:2])}")
                print("-" * 60)
        else:
            print("❌ No validated gaps found. This could mean:")
            print("   • All identified gaps were invalidated by found literature")
            print("   • The analysis needs more validation attempts")
            print("   • The paper doesn't contain clear research gaps")
        
        print("\n" + "=" * 80)
        print("🎉 Test completed successfully!")
        
        # Save full result to file for inspection
        with open("gap_analysis_result.json", "w") as f:
            json.dump(result.dict(), f, indent=2, default=str)
        print("💾 Full results saved to gap_analysis_result.json")
        
    except Exception as e:
        print(f"❌ Test failed: {str(e)}")
        import traceback
        traceback.print_exc()

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