medical-report-analyzer / admin_endpoints.py
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Deploy backend with monitoring infrastructure - Complete Medical AI Platform
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"""
Admin UI Backend Endpoints
Administrative controls for system oversight and review management
Features:
- Review queue management
- System configuration
- User management (placeholder)
- Performance monitoring dashboard
- Compliance reporting interface
- Model versioning controls
Author: MiniMax Agent
Date: 2025-10-29
Version: 1.0.0
"""
from fastapi import APIRouter, HTTPException, Depends
from typing import Dict, List, Any, Optional
from datetime import datetime, timedelta
from pydantic import BaseModel
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
# Create admin router
admin_router = APIRouter(prefix="/admin", tags=["admin"])
# ================================
# REQUEST/RESPONSE MODELS
# ================================
class ReviewQueueItem(BaseModel):
"""Review queue item"""
item_id: str
document_id: str
document_type: str
confidence_score: float
risk_level: str
created_at: str
assigned_to: Optional[str] = None
priority: str # "critical", "high", "medium", "low"
class ReviewAction(BaseModel):
"""Review action request"""
item_id: str
reviewer_id: str
action: str # "approve", "reject", "escalate"
comments: Optional[str] = None
class SystemConfiguration(BaseModel):
"""System configuration"""
error_threshold: float = 0.05
cache_size_mb: int = 1000
cache_ttl_hours: int = 24
alert_email: Optional[str] = None
class ModelDeployment(BaseModel):
"""Model deployment request"""
model_id: str
version: str
set_active: bool = False
# ================================
# REVIEW QUEUE ENDPOINTS
# ================================
# In-memory review queue (in production, use database)
review_queue: List[ReviewQueueItem] = []
@admin_router.get("/review-queue")
async def get_review_queue(
priority: Optional[str] = None,
status: Optional[str] = None
) -> Dict[str, Any]:
"""Get current review queue"""
filtered_queue = review_queue
if priority:
filtered_queue = [item for item in filtered_queue if item.priority == priority]
return {
"total_items": len(review_queue),
"filtered_items": len(filtered_queue),
"queue": [item.dict() for item in filtered_queue],
"summary": {
"critical": len([i for i in review_queue if i.priority == "critical"]),
"high": len([i for i in review_queue if i.priority == "high"]),
"medium": len([i for i in review_queue if i.priority == "medium"]),
"low": len([i for i in review_queue if i.priority == "low"])
}
}
@admin_router.post("/review-queue/action")
async def submit_review_action(action: ReviewAction) -> Dict[str, Any]:
"""Submit review action (approve/reject/escalate)"""
# Find item in queue
item = next((i for i in review_queue if i.item_id == action.item_id), None)
if not item:
raise HTTPException(status_code=404, detail="Review item not found")
# Log review action
logger = get_medical_logger()
logger.info(
f"Review action: {action.action} on {action.item_id}",
user_id=action.reviewer_id,
document_id=item.document_id,
details={"action": action.action, "comments": action.comments}
)
# Log to compliance system
compliance = get_compliance_system()
compliance.log_audit_event(
user_id=action.reviewer_id,
event_type="REVIEW",
resource=f"document:{item.document_id}",
action=action.action.upper(),
ip_address="internal",
details={"item_id": action.item_id, "comments": action.comments}
)
# Remove from queue if approved or rejected
if action.action in ["approve", "reject"]:
review_queue.remove(item)
return {
"success": True,
"action": action.action,
"item_id": action.item_id,
"message": f"Review {action.action}d successfully"
}
@admin_router.post("/review-queue/assign")
async def assign_review(
item_id: str,
reviewer_id: str
) -> Dict[str, Any]:
"""Assign review to a reviewer"""
item = next((i for i in review_queue if i.item_id == item_id), None)
if not item:
raise HTTPException(status_code=404, detail="Review item not found")
item.assigned_to = reviewer_id
return {
"success": True,
"item_id": item_id,
"assigned_to": reviewer_id
}
# ================================
# MONITORING DASHBOARD ENDPOINTS
# ================================
@admin_router.get("/dashboard")
async def get_admin_dashboard() -> Dict[str, Any]:
"""Get comprehensive admin dashboard data"""
monitoring = get_monitoring_service()
versioning = get_versioning_system()
compliance = get_compliance_system()
return {
"timestamp": datetime.utcnow().isoformat(),
"system_health": monitoring.get_system_health(),
"performance_dashboard": monitoring.get_performance_dashboard(),
"model_inventory": versioning.get_system_status(),
"compliance_dashboard": compliance.get_compliance_dashboard(),
"review_queue_summary": {
"total_items": len(review_queue),
"critical_items": len([i for i in review_queue if i.priority == "critical"]),
"unassigned_items": len([i for i in review_queue if not i.assigned_to])
}
}
@admin_router.get("/metrics/performance")
async def get_performance_metrics(
window_minutes: int = 60
) -> Dict[str, Any]:
"""Get detailed performance metrics"""
monitoring = get_monitoring_service()
# Get statistics for key stages
stages = ["pdf_processing", "classification", "model_routing", "synthesis"]
performance_data = {}
for stage in stages:
stats = monitoring.latency_tracker.get_stage_statistics(stage, window_minutes)
performance_data[stage] = stats
error_summary = monitoring.error_monitor.get_error_summary()
return {
"window_minutes": window_minutes,
"latency_by_stage": performance_data,
"error_summary": error_summary,
"timestamp": datetime.utcnow().isoformat()
}
@admin_router.get("/metrics/cache")
async def get_cache_metrics() -> Dict[str, Any]:
"""Get cache performance metrics"""
versioning = get_versioning_system()
cache_stats = versioning.input_cache.get_statistics()
return {
"cache_statistics": cache_stats,
"recommendations": _generate_cache_recommendations(cache_stats),
"timestamp": datetime.utcnow().isoformat()
}
# ================================
# MODEL MANAGEMENT ENDPOINTS
# ================================
@admin_router.get("/models/inventory")
async def get_model_inventory() -> Dict[str, Any]:
"""Get complete model inventory"""
versioning = get_versioning_system()
inventory = versioning.model_registry.get_model_inventory()
return {
"inventory": inventory,
"summary": {
"total_models": len(inventory),
"total_versions": sum(data["total_versions"] for data in inventory.values())
},
"timestamp": datetime.utcnow().isoformat()
}
@admin_router.post("/models/deploy")
async def deploy_model_version(deployment: ModelDeployment) -> Dict[str, Any]:
"""Deploy a model version"""
versioning = get_versioning_system()
try:
if deployment.set_active:
versioning.model_registry.set_active_version(
deployment.model_id,
deployment.version
)
# Invalidate cache for this model
versioning.input_cache.invalidate_model_version(deployment.version)
return {
"success": True,
"model_id": deployment.model_id,
"version": deployment.version,
"active": deployment.set_active,
"message": f"Model {deployment.model_id} v{deployment.version} deployed"
}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@admin_router.post("/models/rollback")
async def rollback_model(
model_id: str,
version: str
) -> Dict[str, Any]:
"""Rollback to a previous model version"""
versioning = get_versioning_system()
success = versioning.model_registry.rollback_to_version(model_id, version)
if not success:
raise HTTPException(status_code=404, detail="Model version not found")
# Invalidate cache
versioning.input_cache.invalidate_model_version(version)
return {
"success": True,
"model_id": model_id,
"rolled_back_to": version,
"message": f"Rolled back {model_id} to v{version}"
}
@admin_router.get("/models/compare")
async def compare_model_versions(
model_id: str,
version1: str,
version2: str,
metric: str = "accuracy"
) -> Dict[str, Any]:
"""Compare two model versions"""
versioning = get_versioning_system()
comparison = versioning.model_registry.compare_versions(
model_id, version1, version2, metric
)
return comparison
# ================================
# COMPLIANCE ENDPOINTS
# ================================
@admin_router.get("/compliance/hipaa-report")
async def get_hipaa_report(
days: int = 30
) -> Dict[str, Any]:
"""Generate HIPAA compliance report"""
compliance = get_compliance_system()
end_date = datetime.utcnow()
start_date = end_date - timedelta(days=days)
report = compliance.generate_hipaa_report(start_date, end_date)
return report
@admin_router.get("/compliance/gdpr-report")
async def get_gdpr_report(
days: int = 30
) -> Dict[str, Any]:
"""Generate GDPR compliance report"""
compliance = get_compliance_system()
end_date = datetime.utcnow()
start_date = end_date - timedelta(days=days)
report = compliance.generate_gdpr_report(start_date, end_date)
return report
@admin_router.get("/compliance/quality-metrics")
async def get_quality_metrics(
days: int = 30
) -> Dict[str, Any]:
"""Get clinical quality metrics"""
compliance = get_compliance_system()
report = compliance.generate_quality_metrics_report(days)
return report
@admin_router.get("/compliance/security-incidents")
async def get_security_incidents(
days: int = 30
) -> Dict[str, Any]:
"""Get security incidents report"""
compliance = get_compliance_system()
report = compliance.generate_security_incidents_report(days)
return report
# ================================
# SYSTEM CONFIGURATION ENDPOINTS
# ================================
# In-memory configuration (in production, use database)
system_config = SystemConfiguration()
@admin_router.get("/config")
async def get_system_configuration() -> SystemConfiguration:
"""Get current system configuration"""
return system_config
@admin_router.post("/config")
async def update_system_configuration(
config: SystemConfiguration
) -> Dict[str, Any]:
"""Update system configuration"""
global system_config
system_config = config
logger = get_medical_logger()
logger.info(
"System configuration updated",
details=config.dict()
)
return {
"success": True,
"config": config.dict(),
"message": "System configuration updated"
}
@admin_router.post("/cache/clear")
async def clear_cache() -> Dict[str, Any]:
"""Clear all cache entries"""
versioning = get_versioning_system()
versioning.input_cache.clear()
return {
"success": True,
"message": "Cache cleared successfully"
}
# ================================
# ALERTS MANAGEMENT
# ================================
@admin_router.get("/alerts")
async def get_active_alerts(
level: Optional[str] = None
) -> Dict[str, Any]:
"""Get active system alerts"""
monitoring = get_monitoring_service()
from monitoring_service import AlertLevel
alert_level = None
if level:
alert_level = AlertLevel(level.upper())
alerts = monitoring.alert_manager.get_active_alerts(level=alert_level)
summary = monitoring.alert_manager.get_alert_summary()
return {
"active_alerts": [a.to_dict() for a in alerts],
"summary": summary,
"timestamp": datetime.utcnow().isoformat()
}
@admin_router.post("/alerts/{alert_id}/resolve")
async def resolve_alert(alert_id: str) -> Dict[str, Any]:
"""Resolve an active alert"""
monitoring = get_monitoring_service()
monitoring.alert_manager.resolve_alert(alert_id)
return {
"success": True,
"alert_id": alert_id,
"message": "Alert resolved"
}
# ================================
# CACHE MANAGEMENT ENDPOINTS
# ================================
@admin_router.get("/cache/statistics")
async def get_cache_statistics() -> Dict[str, Any]:
"""
Get comprehensive cache statistics
Returns cache performance metrics including:
- Hit/miss rates
- Memory usage
- Entry count
- Eviction statistics
"""
monitoring = get_monitoring_service()
cache_stats = monitoring.get_cache_statistics()
return {
"statistics": cache_stats,
"recommendations": _generate_cache_recommendations_v2(cache_stats),
"timestamp": datetime.utcnow().isoformat()
}
@admin_router.get("/cache/entries")
async def list_cache_entries(limit: int = 100) -> Dict[str, Any]:
"""
List cache entries with metadata
Args:
limit: Maximum number of entries to return (default: 100)
"""
monitoring = get_monitoring_service()
entries = monitoring.cache_service.list_entries(limit=limit)
return {
"entries": entries,
"total_shown": len(entries),
"timestamp": datetime.utcnow().isoformat()
}
@admin_router.get("/cache/entry/{key}")
async def get_cache_entry_info(key: str) -> Dict[str, Any]:
"""
Get detailed information about a specific cache entry
Args:
key: Cache key (SHA256 fingerprint)
"""
monitoring = get_monitoring_service()
entry_info = monitoring.cache_service.get_entry_info(key)
if entry_info is None:
raise HTTPException(status_code=404, detail="Cache entry not found")
return entry_info
@admin_router.post("/cache/invalidate/{key}")
async def invalidate_cache_entry(key: str) -> Dict[str, Any]:
"""
Invalidate a specific cache entry
Args:
key: Cache key (SHA256 fingerprint)
"""
monitoring = get_monitoring_service()
success = monitoring.cache_service.invalidate(key)
if not success:
raise HTTPException(status_code=404, detail="Cache entry not found")
return {
"success": True,
"key": key,
"message": "Cache entry invalidated"
}
@admin_router.post("/cache/clear")
async def clear_cache() -> Dict[str, Any]:
"""
Clear all cache entries
WARNING: This will clear all cached data and may temporarily impact performance
"""
monitoring = get_monitoring_service()
monitoring.cache_service.clear()
return {
"success": True,
"message": "All cache entries cleared",
"timestamp": datetime.utcnow().isoformat()
}
# ================================
# HELPER FUNCTIONS
# ================================
def _generate_cache_recommendations_v2(stats: Dict[str, Any]) -> List[str]:
"""Generate cache optimization recommendations based on statistics"""
recommendations = []
hit_rate = stats.get("hit_rate", 0.0)
memory_usage = stats.get("memory_usage_mb", 0.0)
max_memory = stats.get("max_memory_mb", 512)
evictions = stats.get("evictions", 0)
total_entries = stats.get("total_entries", 0)
# Hit rate recommendations
if hit_rate < 0.5:
recommendations.append(f"Low cache hit rate ({hit_rate*100:.1f}%). Consider increasing cache size or TTL.")
elif hit_rate > 0.8:
recommendations.append(f"Excellent cache hit rate ({hit_rate*100:.1f}%). Cache performing optimally.")
# Memory recommendations
utilization = (memory_usage / max_memory) * 100 if max_memory > 0 else 0
if utilization > 90:
recommendations.append(f"Cache near capacity ({utilization:.1f}% used). Consider increasing max cache size.")
# Eviction recommendations
if total_entries > 0 and evictions > total_entries * 0.1:
recommendations.append(f"High eviction rate ({evictions} evictions). Increase cache size to improve performance.")
# Default message
if not recommendations:
recommendations.append("Cache performing within normal parameters.")
return recommendations
def _generate_cache_recommendations(stats: Dict[str, Any]) -> List[str]:
"""Generate cache optimization recommendations"""
recommendations = []
if stats["hit_rate_percent"] < 50:
recommendations.append("Low cache hit rate. Consider increasing cache size or TTL.")
if stats["utilization_percent"] > 90:
recommendations.append("Cache near capacity. Consider increasing max cache size.")
if stats["evictions"] > stats["total_requests"] * 0.1:
recommendations.append("High eviction rate. Increase cache size to improve performance.")
if not recommendations:
recommendations.append("Cache performing optimally.")
return recommendations