"""
05_production_monitoring_drift.py

Use Case: Real-Time LLM/AI Production Monitoring, Drift & Model Quality Alerting
-------------------------------------------------------------------------------
Run-time monitoring, drift tracking, and auto-failover for multi-model or A/B tests:
 - Stream quality/cost/safety metrics per-step
 - Custom alert logic for quality/cost anomaly (editable to your needs)
 - Automated failover and optimization stubs for production incident response

Ideal template for deploying BlazeMetrics in real product inference/serving environments
"""
import time
from blazemetrics import ProductionMonitor

models = ["llama-3", "gpt-4", "gemini-pro"]
metrics = ["rouge1", "bleu", "safety"]
alert_thresholds = {"rouge1": 0.4, "bleu": 0.2, "safety": 0.7}

monitor = ProductionMonitor(models=models, metrics=metrics, alert_thresholds=alert_thresholds)

def alert_callback(result):
    if result.get("quality_drop_detected"):
        print(f"[ALERT] Quality dropped for {result['model']} (quality={result.get('quality', 0.0):.2f})")
    if result.get("cost_spike_detected"):
        print(f"[ALERT] Cost spike for {result['model']} (cost={result.get('cost', 0.0):.4f})")

print("--- Streaming production metrics (simulated) ---")
for step, result in enumerate(monitor.track_production()):
    print(f"Step {step+1}: {result}")
    alert_callback(result)
    time.sleep(1)
    if step == 9:
        break

def failover():
    monitor.auto_failover_to_backup()
    monitor.optimize_inference_parameters()
failover()
