Deploy test_prompt_templates.py to backend/ directory

backend/test_prompt_templates.py

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+"""
+Unit Tests for Medical Prompt Templates
+Tests prompt generation logic without requiring ML model dependencies
+
+Author: MiniMax Agent
+Date: 2025-10-29
+"""
+
+import sys
+sys.path.insert(0, '/workspace/medical-ai-platform/backend')
+
+from medical_prompt_templates import PromptTemplateLibrary, SummaryType
+
+
+def create_sample_ecg_data():
+    """Sample ECG data for testing"""
+    return {
+        "metadata": {
+            "document_id": "ecg-test-001",
+            "facility": "Test Hospital",
+            "document_date": "2025-10-29"
+        },
+        "intervals": {
+            "pr_ms": 165.0,
+            "qrs_ms": 92.0,
+            "qt_ms": 390.0,
+            "qtc_ms": 425.0,
+            "rr_ms": 850.0
+        },
+        "rhythm_classification": {
+            "primary_rhythm": "Normal Sinus Rhythm",
+            "heart_rate_bpm": 71,
+            "heart_rate_regularity": "regular",
+            "arrhythmia_types": []
+        },
+        "arrhythmia_probabilities": {
+            "normal_rhythm": 0.92,
+            "atrial_fibrillation": 0.02
+        },
+        "derived_features": {
+            "st_elevation_mm": {},
+            "axis_deviation": "normal",
+            "t_wave_abnormalities": []
+        }
+    }
+
+
+def create_sample_model_outputs():
+    """Sample model outputs"""
+    return [
+        {
+            "model_name": "Bio_ClinicalBERT",
+            "domain": "clinical_notes",
+            "result": {"summary": "Analysis complete", "confidence": 0.87}
+        }
+    ]
+
+
+def test_ecg_clinician_prompt():
+    """Test ECG clinician prompt generation"""
+    print("\n" + "="*80)
+    print("TEST 1: ECG Clinician Prompt Generation")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    ecg_data = create_sample_ecg_data()
+    model_outputs = create_sample_model_outputs()
+    confidence = {"overall_confidence": 0.89}
+    
+    prompt = lib.get_clinician_summary_template(
+        modality="ECG",
+        structured_data=ecg_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    # Validate prompt contains key elements
+    assert "ECG" in prompt, "Prompt should mention ECG"
+    assert "Heart Rate: 71 bpm" in prompt, "Prompt should include heart rate"
+    assert "Normal Sinus Rhythm" in prompt, "Prompt should include rhythm"
+    assert "ANALYSIS CONFIDENCE: 89.0%" in prompt, "Prompt should include confidence"
+    assert "TECHNICAL SUMMARY" in prompt, "Prompt should have technical summary section"
+    assert "RECOMMENDATIONS" in prompt, "Prompt should have recommendations section"
+    
+    print(f"✓ Prompt generated: {len(prompt)} characters")
+    print(f"✓ Contains all required sections")
+    print(f"\nSample excerpt:\n{prompt[:300]}...\n")
+    
+    return True
+
+
+def test_ecg_patient_prompt():
+    """Test ECG patient-friendly prompt generation"""
+    print("\n" + "="*80)
+    print("TEST 2: ECG Patient Prompt Generation")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    ecg_data = create_sample_ecg_data()
+    model_outputs = create_sample_model_outputs()
+    confidence = {"overall_confidence": 0.89}
+    
+    prompt = lib.get_patient_summary_template(
+        modality="ECG",
+        structured_data=ecg_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    # Validate patient-friendly language
+    assert "YOUR ECG RESULTS" in prompt, "Should use patient-friendly heading"
+    assert "simple" in prompt.lower(), "Should mention simple language"
+    assert "WHAT WE FOUND" in prompt, "Should have clear sections"
+    assert "NEXT STEPS" in prompt, "Should include next steps"
+    
+    print(f"✓ Prompt generated: {len(prompt)} characters")
+    print(f"✓ Patient-friendly language detected")
+    print(f"\nSample excerpt:\n{prompt[:300]}...\n")
+    
+    return True
+
+
+def test_radiology_prompts():
+    """Test radiology prompt generation"""
+    print("\n" + "="*80)
+    print("TEST 3: Radiology Prompt Generation")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    rad_data = {
+        "metadata": {"document_id": "rad-001"},
+        "image_references": [
+            {"modality": "CT", "body_part": "Chest"}
+        ],
+        "findings": {
+            "findings_text": "Clear lungs bilaterally",
+            "impression_text": "No acute abnormality",
+            "critical_findings": [],
+            "incidental_findings": []
+        },
+        "metrics": {"organ_volumes": {}, "lesion_measurements": []}
+    }
+    model_outputs = create_sample_model_outputs()
+    confidence = {"overall_confidence": 0.85}
+    
+    # Test clinician prompt
+    clinician_prompt = lib.get_clinician_summary_template(
+        modality="radiology",
+        structured_data=rad_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    assert "IMAGING STUDY DETAILS" in clinician_prompt
+    assert "CT" in clinician_prompt
+    assert "Chest" in clinician_prompt
+    
+    # Test patient prompt
+    patient_prompt = lib.get_patient_summary_template(
+        modality="radiology",
+        structured_data=rad_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    assert "YOUR IMAGING STUDY" in patient_prompt
+    assert "Type of Scan" in patient_prompt
+    
+    print(f"✓ Clinician prompt: {len(clinician_prompt)} characters")
+    print(f"✓ Patient prompt: {len(patient_prompt)} characters")
+    
+    return True
+
+
+def test_laboratory_prompts():
+    """Test laboratory prompt generation"""
+    print("\n" + "="*80)
+    print("TEST 4: Laboratory Prompt Generation")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    lab_data = {
+        "metadata": {"document_id": "lab-001"},
+        "tests": [
+            {
+                "test_name": "Glucose",
+                "value": 105.0,
+                "unit": "mg/dL",
+                "reference_range_low": 70.0,
+                "reference_range_high": 99.0,
+                "flags": ["H"]
+            }
+        ],
+        "abnormal_count": 1,
+        "critical_values": [],
+        "panel_name": "Basic Metabolic Panel",
+        "collection_date": "2025-10-29"
+    }
+    model_outputs = create_sample_model_outputs()
+    confidence = {"overall_confidence": 0.92}
+    
+    # Test clinician prompt
+    clinician_prompt = lib.get_clinician_summary_template(
+        modality="laboratory",
+        structured_data=lab_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    assert "LABORATORY PANEL" in clinician_prompt
+    assert "Glucose" in clinician_prompt
+    assert "105.0" in clinician_prompt
+    assert "SUMMARY OF KEY FINDINGS" in clinician_prompt
+    
+    # Test patient prompt
+    patient_prompt = lib.get_patient_summary_template(
+        modality="laboratory",
+        structured_data=lab_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    assert "YOUR LAB RESULTS" in patient_prompt
+    assert "everyday language" in patient_prompt.lower()
+    
+    print(f"✓ Clinician prompt: {len(clinician_prompt)} characters")
+    print(f"✓ Patient prompt: {len(patient_prompt)} characters")
+    
+    return True
+
+
+def test_clinical_notes_prompts():
+    """Test clinical notes prompt generation"""
+    print("\n" + "="*80)
+    print("TEST 5: Clinical Notes Prompt Generation")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    notes_data = {
+        "metadata": {"document_id": "note-001"},
+        "note_type": "progress_note",
+        "sections": [
+            {
+                "section_type": "chief_complaint",
+                "content": "Patient presents with chest pain"
+            }
+        ],
+        "entities": [],
+        "diagnoses": ["Chest pain, unspecified"],
+        "medications": ["Aspirin 81mg daily"]
+    }
+    model_outputs = create_sample_model_outputs()
+    confidence = {"overall_confidence": 0.87}
+    
+    # Test clinician prompt
+    clinician_prompt = lib.get_clinician_summary_template(
+        modality="clinical_notes",
+        structured_data=notes_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    assert "CLINICAL SECTIONS" in clinician_prompt
+    assert "ASSESSMENT" in clinician_prompt
+    assert "chest pain" in clinician_prompt.lower()
+    
+    # Test patient prompt
+    patient_prompt = lib.get_patient_summary_template(
+        modality="clinical_notes",
+        structured_data=notes_data,
+        model_outputs=model_outputs,
+        confidence_scores=confidence
+    )
+    
+    assert "REASON FOR YOUR VISIT" in patient_prompt
+    assert "TREATMENT PLAN" in patient_prompt
+    
+    print(f"✓ Clinician prompt: {len(clinician_prompt)} characters")
+    print(f"✓ Patient prompt: {len(patient_prompt)} characters")
+    
+    return True
+
+
+def test_multi_modal_prompt():
+    """Test multi-modal synthesis prompt"""
+    print("\n" + "="*80)
+    print("TEST 6: Multi-Modal Synthesis Prompt")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    
+    modalities = ["ECG", "radiology", "laboratory"]
+    all_data = {
+        "ECG": create_sample_ecg_data(),
+        "radiology": {"metadata": {"document_id": "rad-001"}},
+        "laboratory": {"metadata": {"document_id": "lab-001"}}
+    }
+    confidence_scores = {
+        "ECG": 0.89,
+        "radiology": 0.85,
+        "laboratory": 0.92
+    }
+    
+    prompt = lib.get_multi_modal_synthesis_template(
+        modalities=modalities,
+        all_data=all_data,
+        confidence_scores=confidence_scores
+    )
+    
+    assert "multiple medical documents" in prompt.lower()
+    assert "ECG" in prompt
+    assert "INTEGRATED CLINICAL PICTURE" in prompt
+    assert "COORDINATED CARE PLAN" in prompt
+    
+    print(f"✓ Multi-modal prompt: {len(prompt)} characters")
+    print(f"✓ Includes all {len(modalities)} modalities")
+    
+    return True
+
+
+def test_confidence_explanation_prompt():
+    """Test confidence explanation prompt"""
+    print("\n" + "="*80)
+    print("TEST 7: Confidence Explanation Prompt")
+    print("="*80)
+    
+    lib = PromptTemplateLibrary()
+    
+    # Test high confidence
+    high_conf = {
+        "overall_confidence": 0.92,
+        "extraction_confidence": 0.94,
+        "model_confidence": 0.91,
+        "data_quality": 0.95
+    }
+    
+    prompt_high = lib.get_confidence_explanation_template(
+        confidence_scores=high_conf,
+        modality="ECG"
+    )
+    
+    assert "92.0%" in prompt_high
+    assert "AUTO-APPROVED" in prompt_high
+    
+    # Test low confidence
+    low_conf = {
+        "overall_confidence": 0.55,
+        "extraction_confidence": 0.55,
+        "model_confidence": 0.50,
+        "data_quality": 0.58
+    }
+    
+    prompt_low = lib.get_confidence_explanation_template(
+        confidence_scores=low_conf,
+        modality="ECG"
+    )
+    
+    assert "55.0%" in prompt_low
+    assert "MANUAL REVIEW REQUIRED" in prompt_low
+    
+    print(f"✓ High confidence prompt generated")
+    print(f"✓ Low confidence prompt generated")
+    print(f"✓ Threshold detection working correctly")
+    
+    return True
+
+
+def run_prompt_template_tests():
+    """Run all prompt template tests"""
+    print("\n" + "="*80)
+    print("MEDICAL PROMPT TEMPLATES - UNIT TEST SUITE")
+    print("Testing Prompt Generation Logic")
+    print("="*80)
+    
+    tests = [
+        ("ECG Clinician Prompt", test_ecg_clinician_prompt),
+        ("ECG Patient Prompt", test_ecg_patient_prompt),
+        ("Radiology Prompts", test_radiology_prompts),
+        ("Laboratory Prompts", test_laboratory_prompts),
+        ("Clinical Notes Prompts", test_clinical_notes_prompts),
+        ("Multi-Modal Prompt", test_multi_modal_prompt),
+        ("Confidence Explanation", test_confidence_explanation_prompt)
+    ]
+    
+    results = []
+    
+    for test_name, test_func in tests:
+        try:
+            success = test_func()
+            results.append((test_name, "PASS" if success else "FAIL"))
+            print(f"✓ {test_name}: PASS")
+        except AssertionError as e:
+            print(f"✗ {test_name}: FAIL - {str(e)}")
+            results.append((test_name, "FAIL"))
+        except Exception as e:
+            print(f"✗ {test_name}: ERROR - {str(e)}")
+            import traceback
+            traceback.print_exc()
+            results.append((test_name, "ERROR"))
+    
+    # Print summary
+    print("\n" + "="*80)
+    print("TEST SUMMARY")
+    print("="*80)
+    for test_name, status in results:
+        status_symbol = "✓" if status == "PASS" else "✗"
+        print(f"{status_symbol} {test_name}: {status}")
+    
+    passed = sum(1 for _, status in results if status == "PASS")
+    total = len(results)
+    print(f"\nTotal: {passed}/{total} tests passed ({passed/total*100:.1f}%)")
+    print("="*80)
+    
+    return passed == total
+
+
+if __name__ == "__main__":
+    success = run_prompt_template_tests()
+    exit(0 if success else 1)