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#!/usr/bin/env python3
"""
🚀 FhirFlame Real Workflow Demo
Testing CodeLlama 13B + Langfuse monitoring with real medical document processing
"""
import asyncio
import sys
import os
import time
from datetime import datetime
# Add src to path (from tests directory)
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
from src.codellama_processor import CodeLlamaProcessor
from src.monitoring import monitor
from src.fhir_validator import FhirValidator
async def test_real_medical_workflow():
"""Demonstrate complete real medical AI workflow"""
print("🔥 FhirFlame Real Workflow Demo")
print("=" * 50)
# Sample medical documents for testing
medical_documents = [
{
"filename": "patient_smith.txt",
"content": """
MEDICAL RECORD - CONFIDENTIAL
Patient: John Smith
DOB: 1975-03-15
MRN: MR789123
CHIEF COMPLAINT: Chest pain and shortness of breath
HISTORY OF PRESENT ILLNESS:
45-year-old male presents with acute onset chest pain radiating to left arm.
Associated with diaphoresis and nausea. No prior cardiac history.
VITAL SIGNS:
- Blood Pressure: 145/95 mmHg
- Heart Rate: 102 bpm
- Temperature: 98.6°F
- Oxygen Saturation: 96% on room air
ASSESSMENT AND PLAN:
1. Acute coronary syndrome - rule out myocardial infarction
2. Hypertension - new diagnosis
3. Start aspirin 325mg daily
4. Lisinopril 10mg daily for blood pressure control
5. Atorvastatin 40mg daily
MEDICATIONS PRESCRIBED:
- Aspirin 325mg daily
- Lisinopril 10mg daily
- Atorvastatin 40mg daily
- Nitroglycerin 0.4mg sublingual PRN chest pain
"""
},
{
"filename": "diabetes_follow_up.txt",
"content": """
ENDOCRINOLOGY FOLLOW-UP NOTE
Patient: Maria Rodriguez
DOB: 1962-08-22
MRN: MR456789
DIAGNOSIS: Type 2 Diabetes Mellitus, well controlled
CURRENT MEDICATIONS:
- Metformin 1000mg twice daily
- Glipizide 5mg daily
- Insulin glargine 20 units at bedtime
LABORATORY RESULTS:
- HbA1c: 6.8% (target <7%)
- Fasting glucose: 126 mg/dL
- Creatinine: 1.0 mg/dL (normal kidney function)
VITAL SIGNS:
- Blood Pressure: 128/78 mmHg
- Weight: 165 lbs (stable)
- BMI: 28.5
ASSESSMENT:
Diabetes well controlled. Continue current regimen.
Recommend annual eye exam and podiatry follow-up.
"""
}
]
# Initialize processor with real Ollama
print("\n🤖 Initializing CodeLlama processor...")
processor = CodeLlamaProcessor()
# Initialize FHIR validator
print("📋 Initializing FHIR validator...")
fhir_validator = FhirValidator()
# Process each document
results = []
for i, doc in enumerate(medical_documents, 1):
print(f"\n📄 Processing Document {i}/{len(medical_documents)}: {doc['filename']}")
print("-" * 40)
start_time = time.time()
try:
# Process with real CodeLlama
print("🔍 Analyzing with CodeLlama 13B-instruct...")
result = await processor.process_document(
medical_text=doc['content'],
document_type="clinical_note",
extract_entities=True,
generate_fhir=True
)
processing_time = time.time() - start_time
# Display results
print(f"✅ Processing completed in {processing_time:.2f}s")
print(f"📊 Processing mode: {result['metadata']['model_used']}")
print(f"🎯 Entities found: {result['extraction_results']['entities_found']}")
print(f"📈 Quality score: {result['extraction_results']['quality_score']:.2f}")
# Extract and display medical entities
if 'extracted_data' in result:
import json
extracted = json.loads(result['extracted_data'])
print("\n🏥 Extracted Medical Information:")
print(f" Patient: {extracted.get('patient', 'N/A')}")
print(f" Conditions: {', '.join(extracted.get('conditions', []))}")
print(f" Medications: {', '.join(extracted.get('medications', []))}")
print(f" Confidence: {extracted.get('confidence_score', 0):.1%}")
# Validate FHIR bundle if generated
if 'fhir_bundle' in result:
print("\n📋 Validating FHIR bundle...")
fhir_validation = fhir_validator.validate_fhir_bundle(result['fhir_bundle'])
print(f" FHIR R4 Valid: {fhir_validation['is_valid']}")
print(f" Compliance Score: {fhir_validation['compliance_score']:.1%}")
print(f" Validation Level: {fhir_validation['validation_level']}")
results.append({
'filename': doc['filename'],
'processing_time': processing_time,
'success': True,
'result': result
})
except Exception as e:
print(f"❌ Error processing {doc['filename']}: {e}")
results.append({
'filename': doc['filename'],
'success': False,
'error': str(e)
})
# Summary
print("\n🎯 WORKFLOW SUMMARY")
print("=" * 50)
successful = sum(1 for r in results if r['success'])
total_time = sum(r.get('processing_time', 0) for r in results if r['success'])
print(f"Documents processed: {successful}/{len(medical_documents)}")
print(f"Total processing time: {total_time:.2f}s")
print(f"Average time per document: {total_time/successful:.2f}s" if successful > 0 else "N/A")
# Langfuse monitoring summary
print(f"\n🔍 Langfuse Monitoring: {'✅ Active' if monitor.langfuse else '❌ Disabled'}")
if monitor.langfuse:
print(f" Session ID: {monitor.session_id}")
print(f" Host: {os.getenv('LANGFUSE_HOST', 'cloud.langfuse.com')}")
return results
async def main():
"""Main workflow execution"""
from src.monitoring import monitor
print(f"🕐 Starting at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
# Set environment for real processing
os.environ['USE_REAL_OLLAMA'] = 'true'
try:
results = await test_real_medical_workflow()
# Log comprehensive workflow summary using centralized monitoring
successful = sum(1 for r in results if r['success'])
total_time = sum(r.get('processing_time', 0) for r in results if r['success'])
monitor.log_workflow_summary(
documents_processed=len(results),
successful_documents=successful,
total_time=total_time,
average_time=total_time/successful if successful > 0 else 0,
monitoring_active=monitor.langfuse is not None
)
print("\n🎉 Real workflow demonstration completed successfully!")
return 0
except Exception as e:
print(f"\n💥 Workflow failed: {e}")
return 1
if __name__ == "__main__":
exit_code = asyncio.run(main())
sys.exit(exit_code) |