backend/test_server_monitoring.py

"""
Simplified Test Server for Monitoring Load Testing
Includes only monitoring infrastructure without heavy dependencies
"""

from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from typing import Dict, Any
from datetime import datetime
import uuid
import logging

# Import monitoring modules
from monitoring_service import get_monitoring_service
from model_versioning import get_versioning_system
from production_logging import get_medical_logger
from compliance_reporting import get_compliance_system
from admin_endpoints import admin_router

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize FastAPI app
app = FastAPI(
    title="Medical AI Platform - Monitoring Test Server",
    description="Simplified server for monitoring infrastructure load testing",
    version="2.0.0"
)

# CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize monitoring and infrastructure services
monitoring_service = get_monitoring_service()
versioning_system = get_versioning_system()
medical_logger = get_medical_logger("medical_ai_test")
compliance_system = get_compliance_system()

logger.info("Monitoring test server initialized")

# In-memory job tracking for testing
job_tracker: Dict[str, Dict[str, Any]] = {}

# Add monitoring middleware
@app.middleware("http")
async def monitoring_middleware(request: Request, call_next):
    """Monitoring middleware for request tracking"""
    start_time = datetime.utcnow()
    request_id = str(uuid.uuid4())
    
    medical_logger.info("Request received", {
        "request_id": request_id,
        "method": request.method,
        "path": request.url.path,
        "client": request.client.host if request.client else "unknown"
    })
    
    try:
        response = await call_next(request)
        end_time = datetime.utcnow()
        latency_ms = (end_time - start_time).total_seconds() * 1000
        
        monitoring_service.track_request(
            endpoint=request.url.path,
            latency_ms=latency_ms,
            status_code=response.status_code
        )
        
        medical_logger.info("Request completed", {
            "request_id": request_id,
            "method": request.method,
            "path": request.url.path,
            "status_code": response.status_code,
            "latency_ms": round(latency_ms, 2)
        })
        
        return response
        
    except Exception as e:
        end_time = datetime.utcnow()
        latency_ms = (end_time - start_time).total_seconds() * 1000
        
        monitoring_service.track_error(
            endpoint=request.url.path,
            error_type=type(e).__name__,
            error_message=str(e)
        )
        
        medical_logger.error("Request failed", {
            "request_id": request_id,
            "method": request.method,
            "path": request.url.path,
            "error": str(e),
            "error_type": type(e).__name__,
            "latency_ms": round(latency_ms, 2)
        })
        
        raise

# Startup event handler
@app.on_event("startup")
async def startup_event():
    """Initialize all services on startup"""
    
    medical_logger.info("Starting monitoring test server initialization", {
        "version": "2.0.0",
        "timestamp": datetime.utcnow().isoformat()
    })
    
    # Initialize monitoring service
    monitoring_service.start_monitoring()
    medical_logger.info("Monitoring service initialized", {
        "cache_enabled": True,
        "alert_threshold": 0.05
    })
    
    # Register test model versions
    model_versions = [
        {"model_id": "bio_clinical_bert", "version": "1.0.0", "source": "HuggingFace"},
        {"model_id": "biogpt", "version": "1.0.0", "source": "HuggingFace"},
        {"model_id": "pubmed_bert", "version": "1.0.0", "source": "HuggingFace"},
        {"model_id": "hubert_ecg", "version": "1.0.0", "source": "HuggingFace"},
        {"model_id": "monai_unetr", "version": "1.0.0", "source": "HuggingFace"},
        {"model_id": "medgemma_2b", "version": "1.0.0", "source": "HuggingFace"}
    ]
    
    for model_config in model_versions:
        versioning_system.register_model_version(
            model_id=model_config["model_id"],
            version=model_config["version"],
            metadata={"source": model_config["source"]}
        )
    
    medical_logger.info("Model versioning initialized", {
        "total_models": len(model_versions)
    })
    
    # Test health check
    try:
        health_status = monitoring_service.get_system_health()
        medical_logger.info("Health check successful", {
            "status": health_status["status"],
            "components_ready": True
        })
    except Exception as e:
        medical_logger.error("Health check failed during startup", {
            "error": str(e)
        })
    
    medical_logger.info("Monitoring test server startup complete", {
        "status": "ready",
        "timestamp": datetime.utcnow().isoformat()
    })

# Include admin router
app.include_router(admin_router)

@app.get("/health")
async def health_check():
    """Basic health check endpoint"""
    system_health = monitoring_service.get_system_health()
    
    return {
        "status": system_health["status"],
        "components": {
            "monitoring": "active",
            "versioning": "active",
            "logging": "active",
            "compliance": "active"
        },
        "monitoring": {
            "uptime_seconds": system_health["uptime_seconds"],
            "error_rate": system_health["error_rate"],
            "active_alerts": system_health["active_alerts"],
            "critical_alerts": system_health["critical_alerts"]
        },
        "timestamp": datetime.utcnow().isoformat()
    }

@app.get("/health/dashboard")
async def get_health_dashboard():
    """Comprehensive health dashboard endpoint"""
    try:
        system_health = monitoring_service.get_system_health()
        cache_stats = monitoring_service.get_cache_statistics()
        recent_alerts = monitoring_service.get_recent_alerts(limit=10)
        
        # Get model performance metrics
        model_metrics = {}
        try:
            active_models = versioning_system.list_model_versions()
            for model_info in active_models[:10]:
                model_id = model_info.get("model_id")
                if model_id:
                    perf = versioning_system.get_model_performance(model_id)
                    if perf:
                        model_metrics[model_id] = {
                            "version": model_info.get("version", "unknown"),
                            "total_inferences": perf.get("total_inferences", 0),
                            "avg_latency_ms": perf.get("avg_latency_ms", 0),
                            "error_rate": perf.get("error_rate", 0.0),
                            "last_used": perf.get("last_used", "never")
                        }
        except Exception as e:
            medical_logger.warning("Failed to get model metrics", {"error": str(e)})
        
        # Pipeline statistics
        pipeline_stats = {
            "total_jobs_processed": len(job_tracker),
            "completed_jobs": sum(1 for job in job_tracker.values() if job.get("status") == "completed"),
            "failed_jobs": sum(1 for job in job_tracker.values() if job.get("status") == "failed"),
            "processing_jobs": sum(1 for job in job_tracker.values() if job.get("status") == "processing"),
            "success_rate": 0.0
        }
        
        if pipeline_stats["total_jobs_processed"] > 0:
            pipeline_stats["success_rate"] = (
                pipeline_stats["completed_jobs"] / pipeline_stats["total_jobs_processed"]
            )
        
        # Synthesis statistics (mock for testing)
        synthesis_stats = {
            "total_syntheses": 0,
            "avg_confidence": 0.0,
            "requiring_review": 0,
            "avg_processing_time_ms": 0
        }
        
        # Compliance overview
        compliance_overview = {
            "hipaa_compliant": True,
            "gdpr_compliant": True,
            "audit_logging_active": True,
            "phi_removal_active": True,
            "encryption_enabled": True
        }
        
        dashboard = {
            "status": "operational" if system_health["status"] == "operational" else "degraded",
            "timestamp": datetime.utcnow().isoformat(),
            
            "system": {
                "uptime_seconds": system_health["uptime_seconds"],
                "uptime_human": f"{system_health['uptime_seconds'] // 3600}h {(system_health['uptime_seconds'] % 3600) // 60}m",
                "error_rate": system_health["error_rate"],
                "total_requests": system_health["total_requests"],
                "error_threshold": 0.05,
                "status": system_health["status"]
            },
            
            "pipeline": pipeline_stats,
            
            "models": {
                "total_registered": len(model_metrics),
                "performance": model_metrics
            },
            
            "synthesis": synthesis_stats,
            
            "cache": cache_stats,
            
            "alerts": {
                "active_count": system_health["active_alerts"],
                "critical_count": system_health["critical_alerts"],
                "recent": recent_alerts
            },
            
            "compliance": compliance_overview,
            
            "components": {
                "monitoring_system": "operational",
                "versioning_system": "operational",
                "logging_system": "operational",
                "compliance_reporting": "operational",
                "cache_service": "operational"
            }
        }
        
        return dashboard
        
    except Exception as e:
        medical_logger.error("Dashboard generation failed", {
            "error": str(e),
            "timestamp": datetime.utcnow().isoformat()
        })
        
        return {
            "status": "error",
            "timestamp": datetime.utcnow().isoformat(),
            "error": "Failed to generate complete dashboard",
            "message": str(e)
        }

@app.get("/")
async def root():
    """Root endpoint"""
    return {
        "message": "Medical AI Platform - Monitoring Test Server",
        "version": "2.0.0",
        "endpoints": {
            "health": "/health",
            "dashboard": "/health/dashboard",
            "admin": "/admin/*"
        }
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)