quick_test.py

"""
Quick Test Script - Direct Pipeline Usage Without Server
For development testing, no OpenAI API Key required
"
import sys
import os

# Add Backend to path
sys.path.append(os.path.join(os.path.dirname(__file__), 'Backend'))

from pipeline.pain_assessment_pipeline import PainAssessmentPipeline

def test_quick():
    """Quick test using mocked LLM output"""
    
    print("="*70)
    print("🧪 Quick Test (No OpenAI API Required)")
    print("="*70)
    
    # Initialize pipeline
    pipeline = PainAssessmentPipeline(verbose=True)
    
    # Test cases
    test_cases = [
        {
            "name": "中文 - 慢性神经病理性疼痛",
            "text": "我有火辣辣的疼痛，已经好几个月了，腰部很难受",
            "llm_entities": {
                "pain_descriptors": ["火辣辣的疼痛"],
                "location": "腰部",
                "duration_phrase": "好几个月",
                "emotion_keywords": ["难受"],
                "functional_impact": None,
                "intensity": "Moderate to severe"
            }
        },
        {
            "name": "韩语 - 刺痛",
            "text": "허리가 따끔거리다",
            "llm_entities": {
                "pain_descriptors": ["따끔거리다"],
                "location": "허리",
                "duration_phrase": None,
                "emotion_keywords": [],
                "functional_impact": None,
                "intensity": "Moderate"
            }
        },
        {
            "name": "西班牙语 - 急性疼痛",
            "text": "Tengo un dolor agudo y punzante en la espalda",
            "llm_entities": {
                "pain_descriptors": ["agudo", "punzante"],
                "location": "la espalda",
                "duration_phrase": None,
                "emotion_keywords": [],
                "functional_impact": None,
                "intensity": "Severe"
            }
        },
        {
            "name": "苗族语 - 灼烧痛",
            "text": "Kuv mob Kub Heev heev",
            "llm_entities": {
                "pain_descriptors": ["Kub Heev"],
                "location": None,
                "duration_phrase": None,
                "emotion_keywords": [],
                "functional_impact": None,
                "intensity": "Severe"
            }
        }
    ]
    
    passed = 0
    failed = 0
    
    for test in test_cases:
        print("\n" + "="*70)
        print(f"Test: {test['name']}")
        print("-"*70)
        print(f"Input: {test['text']}")
        
        try:
            # Execute pipeline with mocked LLM output
            report = pipeline.execute(
                test['text'],
                test['llm_entities']
            )
            
            print(f"\n✅ Test Passed")
            print(f"\n📋 Analysis Results:")
            print(f"  Pain Type: {report.structured_data.pain_type}")
            print(f"  Location: {report.structured_data.location}")
            print(f"  Temporal Pattern: {report.structured_data.temporal_pattern}")
            
            if report.ontology_mapping_trace:
                print(f"\n🔄 Ontology Mappings ({len(report.ontology_mapping_trace)} items):")
                for mapping in report.ontology_mapping_trace:
                    lang = mapping.get('detected_language', '?')
                    print(f"  [{lang}] {mapping['original_term']} → "
                          f"{mapping['mapped_english']} ({mapping.get('pain_type', 'N/A')})")
            
            if report.clinical_recommendations:
                print(f"\n💊 Clinical Recommendations ({len(report.clinical_recommendations)} items):")
                for i, rec in enumerate(report.clinical_recommendations, 1):
                    print(f"\n  {i}. {rec.recommendation[:80]}...")
                    print(f"     Rule: {rec.triggered_by_rule}")
            else:
                print(f"\n💊 Clinical Recommendations: Standard assessment")
            
            passed += 1
            
        except Exception as e:
            print(f"\n❌ Test Failed: {e}")
            import traceback
            traceback.print_exc()
            failed += 1
    
    # Summary
    print("="*70)
    print("📊 Test Summary")
    print("="*70)
    print(f"Passed: {passed}/{passed+failed}")
    print(f"Failed: {failed}/{passed+failed}")
    
    if failed == 0:
        print("\n✅ All tests passed!")
    else:
        print(f"\n⚠️  {failed} test(s) failed")
    
    return failed == 0


def show_system_info():
    """Display system information"""
    print("\n" + "="*70)
    print("📊 System Information")
    print("="*70)
    
    pipeline = PainAssessmentPipeline(verbose=False)
    info = pipeline.get_pipeline_info()
    
    print(f"Pipeline Version: {info['pipeline_version']}")
    print(f"Architecture: {info['architecture']}")
    print(f"Supported Languages: {', '.join(info['supported_languages'])}")
    print(f"\nOntology Coverage:")
    print(f"  Total: {info['ontology_coverage']['total_descriptors']} terms")
    print(f"  Chinese: {info['ontology_coverage']['chinese_terms']}")
    print(f"  Korean: {info['ontology_coverage']['korean_terms']}")
    print(f"  Spanish: {info['ontology_coverage']['spanish_terms']}")
    print(f"  Hmong: {info['ontology_coverage']['hmong_terms']}")
    print(f"\nClinical Rules: {info['rule_count']}")
    print(f"  {', '.join(info['active_rules'])}")


if __name__ == "__main__":
    print("="*70)
    print("🚀 Multilingual Pain Assessment - Quick Test")
    print("="*70)
    print("\n💡 Note: This test does not require OpenAI API Key")
    print("   Uses predefined LLM output to simulate complete pipeline")
    
    # Display system information
    show_system_info()
    
    # Run tests
    success = test_quick()
    
    if success:
        print("\n" + "="*70)
        print("🎉 System working properly!")
        print("="*70)
        print("\nNext Steps:")
        print("  1. Set OPENAI_API_KEY environment variable")
        print("  2. Start server: python Backend/main.py")
        print("  3. Test full API: python test_api.py")
        print("\nDetailed docs: HOW_TO_START.md")
    
    sys.exit(0 if success else 1)