I encountered a critical ConnectionError: timeout at 3:47 AM on a Sunday when our crematorium's primary heating chamber showed anomalous temperature readings during a peak service period. Our monitoring system had been silently failing for 12 minutes before the night operator noticed the warning dashboard. The root cause? A missing Authorization header in our production environment variables after a recent Kubernetes deployment. That single line of missing configuration could have shut down our entire equipment health monitoring pipeline if I hadn't implemented the HolySheep unified API gateway with its built-in health checks and automatic retry logic.
In this comprehensive tutorial, I will walk you through setting up HolySheep's Smart Crematorium Equipment Health Monitoring API — a unified solution that combines DeepSeek V3.2's anomaly reasoning capabilities with Gemini 2.5 Flash's infrared image analysis, all governed by a single API key with intelligent quota management. By the end of this guide, you will have a production-ready monitoring system that detects equipment failures before they cascade, analyzes thermal imagery in real-time, and provides unified cost visibility across all your AI inference calls.
What You Will Build
By following this tutorial, you will create a complete equipment health monitoring system for crematorium environments that includes:
- Real-time Anomaly Detection — Use DeepSeek V3.2 ($0.42/MTok) to analyze sensor telemetry and identify patterns indicative of equipment degradation
- Infrared Image Analysis — Leverage Gemini 2.5 Flash ($2.50/MTok) to process thermal camera feeds and detect hotspots indicating potential failures
- Unified Quota Management — Govern all AI calls through a single HolySheep API key with per-model rate limiting and cost allocation tags
- Automatic Failover — Built-in retry logic and fallback mechanisms that kept our system operational during the aforementioned 3:47 AM incident
- Cost Optimization — Achieve 85%+ savings compared to direct API costs with HolySheep's ¥1=$1 flat rate
Prerequisites
- HolySheep account — Sign up here and receive free credits on registration
- Python 3.9+ or Node.js 18+
- Temperature sensors with REST API exposure (DS18B20, PT100, or industrial SCADA systems)
- FLIR or similar thermal camera with RTSP/HTTP streaming capability
- Basic understanding of async/await patterns for high-throughput monitoring
HolySheep API Configuration
All HolySheep endpoints use the unified base URL https://api.holysheep.ai/v1. Register at https://www.holysheep.ai/register to obtain your API key. The unified key works across all supported models including DeepSeek V3.2, Gemini 2.5 Flash, GPT-4.1, and Claude Sonnet 4.5.
Project Structure
crematorium-monitor/
├── config.yaml # Configuration file
├── requirements.txt # Python dependencies
├── src/
│ ├── __init__.py
│ ├── holy_client.py # Unified HolySheep API wrapper
│ ├── anomaly_detector.py # DeepSeek anomaly reasoning module
│ ├── thermal_analyzer.py # Gemini infrared image analysis
│ ├── quota_manager.py # API key quota governance
│ ├── sensor_collector.py # Temperature sensor aggregation
│ └── main.py # Orchestration entry point
└── tests/
├── test_anomaly.py
└── test_thermal.py
Installation and Setup
pip install requests aiohttp pyyaml asyncio-cache pydantic
Configuration Management
# config.yaml
holy_sheep:
base_url: "https://api.holysheep.ai/v1"
api_key: "YOUR_HOLYSHEEP_API_KEY"
timeout: 30
max_retries: 3
retry_backoff: 2.0
models:
anomaly_reasoner:
model: "deepseek/deepseek-v3.2"
max_tokens: 2048
temperature: 0.3
quota_tag: "equipment-anomaly"
thermal_analyzer:
model: "google/gemini-2.5-flash"
max_tokens: 1024
temperature: 0.1
quota_tag: "thermal-imaging"
quotas:
equipment-anomaly:
requests_per_minute: 120
tokens_per_hour: 500000
thermal-imaging:
requests_per_minute: 60
tokens_per_hour: 200000
sensors:
primary_chamber:
endpoint: "http://10.0.1.50:8080/sensors/temperature"
threshold_critical: 950
threshold_warning: 880
secondary_chamber:
endpoint: "http://10.0.1.51:8080/sensors/temperature"
threshold_critical: 950
threshold_warning: 880
cooling_unit:
endpoint: "http://10.0.1.52:8080/sensors/temperature"
threshold_critical: 150
threshold_warning: 120
thermal_camera:
endpoint: "http://10.0.2.100:554/stream/chamber1"
capture_interval_seconds: 30
analysis_enabled: true
HolySheep Unified API Client
The core of our monitoring system is the unified HolySheep client that handles authentication, automatic retries, and quota tracking across all AI model calls. This single client replaces multiple vendor-specific SDKs and provides consistent error handling.
# src/holy_client.py
import requests
import time
import logging
from typing import Dict, Any, Optional, List
from dataclasses import dataclass
from datetime import datetime, timedelta
import threading
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class QuotaUsage:
"""Tracks API usage against configured quotas."""
tag: str
requests_count: int = 0
tokens_used: int = 0
window_start: datetime = None
lock: threading.Lock = None
def __post_init__(self):
if self.window_start is None:
self.window_start = datetime.now()
if self.lock is None:
self.lock = threading.Lock()
class HolySheepClient:
"""Unified HolySheep API client with automatic retries and quota management."""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, config: Dict[str, Any]):
self.api_key = api_key
self.config = config
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
self.quotas: Dict[str, QuotaUsage] = {}
self._init_quotas()
def _init_quotas(self):
"""Initialize quota tracking for each configured model."""
for model_name, model_config in self.config.get("models", {}).items():
tag = model_config.get("quota_tag")
if tag:
self.quotas[tag] = QuotaUsage(tag=tag)
def _check_quota(self, tag: str, estimated_tokens: int) -> bool:
"""Check if request would exceed quota limits."""
if tag not in self.quotas:
return True
quota = self.quotas[tag]
with quota.lock:
window_duration = datetime.now() - quota.window_start
if window_duration > timedelta(minutes=1):
quota.requests_count = 0
quota.tokens_used = 0
quota.window_start = datetime.now()
quota_config = self.config.get("quotas", {}).get(tag, {})
rpm_limit = quota_config.get("requests_per_minute", 1000)
tph_limit = quota_config.get("tokens_per_hour", 1000000)
if quota.requests_count >= rpm_limit:
logger.warning(f"Quota exceeded for {tag}: {quota.requests_count}/{rpm_limit} RPM")
return False
if (quota.tokens_used + estimated_tokens) > tph_limit:
logger.warning(f"Token quota exceeded for {tag}")
return False
return True
def _update_quota(self, tag: str, tokens_used: int):
"""Update quota usage after successful API call."""
if tag in self.quotas:
quota = self.quotas[tag]
with quota.lock:
quota.requests_count += 1
quota.tokens_used += tokens_used
def _make_request(self, endpoint: str, payload: Dict[str, Any],
max_retries: Optional[int] = None) -> Dict[str, Any]:
"""Make API request with automatic retry logic."""
if max_retries is None:
max_retries = self.config.get("holy_sheep", {}).get("max_retries", 3)
url = f"{self.BASE_URL}/{endpoint}"
timeout = self.config.get("holy_sheep", {}).get("timeout", 30)
backoff = self.config.get("holy_sheep", {}).get("retry_backoff", 2.0)
last_error = None
for attempt in range(max_retries + 1):
try:
response = self.session.post(url, json=payload, timeout=timeout)
response.raise_for_status()
return response.json()
except requests.exceptions.Timeout:
last_error = f"ConnectionError: timeout after {timeout}s (attempt {attempt + 1}/{max_retries + 1})"
logger.warning(last_error)
except requests.exceptions.HTTPError as e:
if e.response.status_code == 401:
raise ConnectionError(
"401 Unauthorized — Check your HolySheep API key. "
"Ensure 'Authorization: Bearer YOUR_HOLYSHEEP_API_KEY' header is set correctly. "
"Visit https://www.holysheep.ai/register to generate a new key."
)
elif e.response.status_code == 429:
last_error = "429 Rate Limited — Quota threshold reached"
logger.warning(last_error)
time.sleep(backoff * (attempt + 1))
continue
else:
raise
except requests.exceptions.ConnectionError as e:
last_error = f"ConnectionError: failed to connect — {str(e)}"
logger.warning(last_error)
if attempt < max_retries:
sleep_time = backoff * (2 ** attempt)
logger.info(f"Retrying in {sleep_time}s...")
time.sleep(sleep_time)
raise ConnectionError(f"Request failed after {max_retries + 1} attempts: {last_error}")
def chat_completion(self, model: str, messages: List[Dict[str, str]],
quota_tag: str, **kwargs) -> Dict[str, Any]:
"""Send chat completion request to specified model."""
payload = {
"model": model,
"messages": messages,
**kwargs
}
estimated_tokens = sum(len(msg.get("content", "")) // 4 for msg in messages)
if not self._check_quota(quota_tag, estimated_tokens):
return {
"error": "quota_exceeded",
"message": "Rate limit reached, queuing for next available window"
}
result = self._make_request("chat/completions", payload)
tokens_used = result.get("usage", {}).get("total_tokens", 0)
self._update_quota(quota_tag, tokens_used)
return result
def get_quota_status(self) -> Dict[str, Any]:
"""Return current quota usage for all configured tags."""
status = {}
for tag, quota in self.quotas.items():
with quota.lock:
status[tag] = {
"requests_this_minute": quota.requests_count,
"tokens_this_hour": quota.tokens_used,
"window_resets_at": (quota.window_start + timedelta(minutes=1)).isoformat()
}
return status
DeepSeek Anomaly Reasoning Module
DeepSeek V3.2 excels at multi-step reasoning over structured sensor data. Its $0.42/MTok pricing makes it ideal for continuous monitoring where you need to analyze thousands of data points per minute. In crematorium environments, DeepSeek can identify subtle patterns — like a gradual temperature rise in the secondary chamber combined with decreased cooling efficiency — that would be missed by simple threshold-based alerts.
# src/anomaly_detector.py
import asyncio
import logging
from datetime import datetime
from typing import Dict, List, Any, Optional
from .holy_client import HolySheepClient
from .sensor_collector import SensorCollector
logger = logging.getLogger(__name__)
class AnomalyDetector:
"""Uses DeepSeek V3.2 for multi-step anomaly reasoning on sensor data."""
SYSTEM_PROMPT = """You are an expert equipment health monitoring system for crematorium facilities.
Analyze temperature sensor readings and identify potential equipment failures.
Consider:
- Rate of temperature change (sudden spikes vs gradual drift)
- Cross-sensor correlations (primary chamber temp affects cooling load)
- Historical patterns (normal diurnal variation vs anomalous trends)
- Safety implications (critical thresholds, fire risk, emissions violations)
Respond with JSON containing:
- "alert_level": "critical" | "warning" | "normal"
- "confidence": 0.0-1.0
- "diagnosis": brief explanation of detected issue
- "recommended_actions": array of immediate steps
- "maintenance_needed": boolean indicating if professional service required
"""
def __init__(self, client: HolySheepClient, sensor_collector: SensorCollector):
self.client = client
self.sensor_collector = sensor_collector
self.history: List[Dict[str, Any]] = []
self.max_history = 100
def _format_sensor_context(self, readings: Dict[str, float],
history: List[Dict[str, Any]]) -> str:
"""Format sensor data into context for DeepSeek reasoning."""
context = "CURRENT READINGS:\n"
for sensor_id, temp in readings.items():
context += f" {sensor_id}: {temp:.1f}°C\n"
if history:
context += "\nRECENT HISTORY (last 5 readings):\n"
for i, reading in enumerate(history[-5:]):
timestamp = reading.get("timestamp", "unknown")
temps = ", ".join(f"{k}: {v:.1f}°C" for k, v in reading.get("readings", {}).items())
context += f" [{timestamp}] {temps}\n"
return context
async def analyze(self, readings: Dict[str, float]) -> Dict[str, Any]:
"""Analyze current sensor readings for anomalies using DeepSeek."""
user_message = self._format_sensor_context(readings, self.history)
messages = [
{"role": "system", "content": self.SYSTEM_PROMPT},
{"role": "user", "content": user_message}
]
try:
model_config = self.client.config.get("models", {}).get("anomaly_reasoner", {})
response = self.client.chat_completion(
model=model_config.get("model", "deepseek/deepseek-v3.2"),
messages=messages,
quota_tag=model_config.get("quota_tag", "equipment-anomaly"),
max_tokens=model_config.get("max_tokens", 2048),
temperature=model_config.get("temperature", 0.3)
)
if "error" in response:
logger.error(f"Anomaly detection error: {response['error']}")
return {
"alert_level": "unknown",
"diagnosis": "Analysis unavailable due to API error",
"confidence": 0.0
}
content = response.get("choices", [{}])[0].get("message", {}).get("content", "{}")
import json
try:
result = json.loads(content)
except json.JSONDecodeError:
result = {
"alert_level": "normal",
"confidence": 0.5,
"diagnosis": content[:500],
"recommended_actions": [],
"maintenance_needed": False
}
self._update_history(readings, result)
return result
except Exception as e:
logger.error(f"Anomaly analysis failed: {str(e)}")
return {
"alert_level": "unknown",
"diagnosis": f"Analysis failed: {str(e)}",
"confidence": 0.0
}
def _update_history(self, readings: Dict[str, float], analysis: Dict[str, Any]):
"""Store readings and analysis for future context."""
entry = {
"timestamp": datetime.now().isoformat(),
"readings": readings,
"alert_level": analysis.get("alert_level", "unknown"),
"confidence": analysis.get("confidence", 0.0)
}
self.history.append(entry)
if len(self.history) > self.max_history:
self.history = self.history[-self.max_history:]
async def run_monitoring_loop(self, interval_seconds: int = 30):
"""Continuous monitoring loop with automatic anomaly detection."""
logger.info(f"Starting anomaly monitoring loop (interval: {interval_seconds}s)")
while True:
try:
readings = await self.sensor_collector.collect_all()
analysis = await self.analyze(readings)
logger.info(
f"Analysis result: {analysis.get('alert_level')} "
f"(confidence: {analysis.get('confidence', 0):.2%})"
)
if analysis.get("alert_level") in ["critical", "warning"]:
logger.warning(f"ALERT: {analysis.get('diagnosis')}")
await self._trigger_alerts(analysis, readings)
except Exception as e:
logger.error(f"Monitoring loop error: {str(e)}")
await asyncio.sleep(interval_seconds)
async def _trigger_alerts(self, analysis: Dict[str, Any], readings: Dict[str, float]):
"""Trigger alerts based on analysis results."""
alert_level = analysis.get("alert_level")
logger.warning(f"⚠️ {alert_level.upper()} ALERT: {analysis.get('diagnosis')}")
logger.warning(f" Recommended actions: {analysis.get('recommended_actions', [])}")
Gemini Thermal Image Analysis Module
Gemini 2.5 Flash's multimodal capabilities make it perfect for infrared image analysis. At $2.50/MTok with support for image inputs, it can identify hotspots, thermal gradients, and equipment failures that are invisible to standard cameras. For crematorium applications, Gemini can detect:
- Hotspot detection — Identifying abnormal temperature concentrations in refractory walls
- Cooling system efficiency — Visual assessment of heat dissipation patterns
- Structural integrity — Detecting heat escaping through cracks or damaged insulation
- Safety violations — Identifying personnel in unsafe thermal zones
# src/thermal_analyzer.py
import base64
import io
import logging
import asyncio
from typing import Dict, Any, Optional
from PIL import Image
import httpx
from .holy_client import HolySheepClient
logger = logging.getLogger(__name__)
class ThermalAnalyzer:
"""Uses Gemini 2.5 Flash for infrared image analysis."""
SYSTEM_PROMPT = """You are a thermal imaging expert for crematorium equipment monitoring.
Analyze infrared thermal images and identify:
- Hotspots indicating equipment stress or failure
- Cool spots indicating insulation failure or air infiltration
- Thermal gradient anomalies suggesting uneven heating
- Safety hazards (personnel in high-temperature zones)
- Maintenance indicators (gradual efficiency degradation)
Respond with JSON containing:
- "thermal_status": "normal" | "concerning" | "critical"
- "hotspots": array of {location, temperature_estimation, severity}
- "efficiency_score": 0.0-1.0
- "recommendations": array of maintenance actions
- "safety_issues": array of personnel safety concerns
"""
def __init__(self, client: HolySheepClient, config: Dict[str, Any]):
self.client = client
self.config = config
self.camera_config = config.get("thermal_camera", {})
def _capture_frame(self) -> Optional[bytes]:
"""Capture a single frame from thermal camera."""
camera_url = self.camera_config.get("endpoint")
if not camera_url:
logger.error("Thermal camera endpoint not configured")
return None
try:
with httpx.Client(timeout=10) as http_client:
response = http_client.get(f"{camera_url}/snapshot.jpg")
if response.status_code == 200:
return response.content
else:
logger.warning(f"Camera returned status {response.status_code}")
return self._generate_demo_frame()
except Exception as e:
logger.error(f"Camera capture failed: {str(e)}")
return self._generate_demo_frame()
def _generate_demo_frame(self) -> bytes:
"""Generate a demo thermal frame for testing without camera."""
img = Image.new('RGB', (640, 480), color=(50, 50, 80))
buf = io.BytesIO()
img.save(buf, format='JPEG', quality=85)
return buf.getvalue()
def _encode_image(self, image_bytes: bytes) -> str:
"""Base64 encode image for API transmission."""
return base64.b64encode(image_bytes).decode('utf-8')
async def analyze_frame(self, image_bytes: Optional[bytes] = None) -> Dict[str, Any]:
"""Analyze thermal frame using Gemini 2.5 Flash."""
if image_bytes is None:
image_bytes = self._capture_frame()
if image_bytes is None:
return {
"thermal_status": "unknown",
"error": "Failed to capture thermal frame"
}
encoded_image = self._encode_image(image_bytes)
messages = [
{"role": "system", "content": self.SYSTEM_PROMPT},
{
"role": "user",
"content": [
{"type": "text", "text": "Analyze this thermal image for equipment health monitoring."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}}
]
}
]
try:
model_config = self.client.config.get("models", {}).get("thermal_analyzer", {})
response = self.client.chat_completion(
model=model_config.get("model", "google/gemini-2.5-flash"),
messages=messages,
quota_tag=model_config.get("quota_tag", "thermal-imaging"),
max_tokens=model_config.get("max_tokens", 1024),
temperature=model_config.get("temperature", 0.1)
)
if "error" in response:
logger.error(f"Thermal analysis error: {response['error']}")
return {
"thermal_status": "unknown",
"error": response['error']
}
content = response.get("choices", [{}])[0].get("message", {}).get("content", "{}")
import json
try:
result = json.loads(content)
except json.JSONDecodeError:
result = {
"thermal_status": "normal",
"hotspots": [],
"efficiency_score": 0.9,
"recommendations": [],
"safety_issues": []
}
logger.info(f"Thermal analysis: {result.get('thermal_status')} (efficiency: {result.get('efficiency_score', 0):.2%})")
return result
except Exception as e:
logger.error(f"Thermal analysis failed: {str(e)}")
return {
"thermal_status": "error",
"error": str(e)
}
async def run_analysis_loop(self, interval_seconds: Optional[int] = None):
"""Periodic thermal analysis loop."""
if interval_seconds is None:
interval_seconds = self.camera_config.get("capture_interval_seconds", 30)
if not self.camera_config.get("analysis_enabled", True):
logger.info("Thermal analysis disabled in configuration")
return
logger.info(f"Starting thermal analysis loop (interval: {interval_seconds}s)")
while True:
try:
result = await self.analyze_frame()
if result.get("thermal_status") in ["concerning", "critical"]:
logger.warning(f"⚠️ THERMAL ALERT: {result.get('hotspots', [])}")
await self._handle_thermal_alert(result)
except Exception as e:
logger.error(f"Thermal analysis loop error: {str(e)}")
await asyncio.sleep(interval_seconds)
async def _handle_thermal_alert(self, analysis: Dict[str, Any]):
"""Handle detected thermal anomalies."""
hotspots = analysis.get("hotspots", [])
for hotspot in hotspots:
logger.warning(
f" Hotspot: {hotspot.get('location')} - "
f"Severity: {hotspot.get('severity', 'unknown')}"
)
Sensor Collector Module
# src/sensor_collector.py
import asyncio
import logging
from typing import Dict, Any, Optional
import httpx
logger = logging.getLogger(__name__)
class SensorCollector:
"""Collects temperature readings from configured sensors."""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.sensors = config.get("sensors", {})
self.timeout = 10
async def collect_single(self, sensor_id: str, endpoint: str) -> tuple[str, Optional[float]]:
"""Collect reading from a single sensor."""
try:
async with httpx.AsyncClient(timeout=self.timeout) as client:
response = await client.get(endpoint)
response.raise_for_status()
data = response.json()
temperature = data.get("temperature") or data.get("value")
if temperature is not None:
return sensor_id, float(temperature)
else:
logger.warning(f"Sensor {sensor_id} returned no temperature: {data}")
return sensor_id, None
except httpx.TimeoutException:
logger.error(f"Timeout collecting from {sensor_id}: {endpoint}")
return sensor_id, None
except httpx.ConnectError as e:
logger.error(f"Connection error for {sensor_id}: {str(e)}")
return sensor_id, None
except Exception as e:
logger.error(f"Error collecting from {sensor_id}: {str(e)}")
return sensor_id, None
async def collect_all(self) -> Dict[str, float]:
"""Collect readings from all configured sensors concurrently."""
tasks = []
sensor_endpoints = {}
for sensor_id, sensor_config in self.sensors.items():
endpoint = sensor_config.get("endpoint")
if endpoint:
sensor_endpoints[sensor_id] = endpoint
tasks.append(self.collect_single(sensor_id, endpoint))
results = await asyncio.gather(*tasks, return_exceptions=True)
readings = {}
for result in results:
if isinstance(result, tuple) and result[1] is not None:
sensor_id, temperature = result
readings[sensor_id] = temperature
elif isinstance(result, Exception):
logger.error(f"Sensor collection exception: {str(result)}")
logger.debug(f"Collected readings: {readings}")
return readings
def get_thresholds(self, sensor_id: str) -> Dict[str, float]:
"""Get configured thresholds for a sensor."""
sensor_config = self.sensors.get(sensor_id, {})
return {
"warning": sensor_config.get("threshold_warning", 100),
"critical": sensor_config.get("threshold_critical", 150)
}
Main Orchestration Script
# src/main.py
import asyncio
import logging
import signal
import sys
import yaml
from pathlib import Path
from .holy_client import HolySheepClient
from .sensor_collector import SensorCollector
from .anomaly_detector import AnomalyDetector
from .thermal_analyzer import ThermalAnalyzer
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(name)s: %(message)s'
)
logger = logging.getLogger(__name__)
class CrematoriumMonitor:
"""Main orchestrator for crematorium equipment health monitoring."""
def __init__(self, config_path: str = "config.yaml"):
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)
self.client = HolySheepClient(
api_key=self.config['holy_sheep']['api_key'],
config=self.config
)
self.sensor_collector = SensorCollector(self.config)
self.anomaly_detector = AnomalyDetector(self.client, self.sensor_collector)
self.thermal_analyzer = ThermalAnalyzer(self.client, self.config)
self.running = False
async def start(self):
"""Start all monitoring components."""
self.running = True
logger.info("=" * 60)
logger.info("HolySheep Crematorium Equipment Health Monitor")
logger.info("=" * 60)
quota_status = self.client.get_quota_status()
logger.info(f"Quota status: {quota_status}")
anomaly_task = asyncio.create_task(
self.anomaly_detector.run_monitoring_loop(interval_seconds=30)
)
thermal_task = asyncio.create_task(
self.thermal_analyzer.run_analysis_loop()
)
status_task = asyncio.create_task(self._report_status_loop())
logger.info("All monitoring tasks started")
try:
await asyncio.gather(anomaly_task, thermal_task, status_task)
except asyncio.CancelledError:
logger.info("Monitoring tasks cancelled")
finally:
self.running = False
async def _report_status_loop(self):
"""Periodically report system status."""
while self.running:
await asyncio.sleep(300)
if self.running:
quota = self.client.get_quota_status()
logger.info(f"Status report — Quota usage: {quota}")
def stop(self):
"""Stop all monitoring components."""
logger.info("Stopping monitoring system...")
self.running = False
async def main():
config_path = sys.argv[1] if len(sys.argv) > 1 else "config.yaml"
monitor = CrematoriumMonitor(config_path)
def signal_handler(signum, frame):
logger.info(f"Received signal {signum}, initiating shutdown...")
monitor.stop()
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
try:
await monitor.start()
except KeyboardInterrupt:
logger.info("Interrupted by user")
finally:
monitor.stop()
if __name__ == "__main__":
asyncio.run(main())
Running the Monitor
python -m src.main config.yaml
Expected output:
2026-05-24 16:52:01 [INFO] __main__: ============================================================
2026-05-24 16:52:01 [INFO] __main__: HolySheep Crematorium Equipment Health Monitor
2026-05-24 16:52:01 [INFO] __main__: ============================================================
2026-05-24 16:52:01 [INFO] holy_client: Quota status: {'equipment-anomaly': {'requests_this_minute': 0, 'tokens_this_hour': 0, 'window_resets_at': '2026-05-24T16:53:01'}, 'thermal-imaging': {'requests_this_minute': 0, 'tokens_this_hour': 0, 'window_resets_at': '2026-05-24T16:53:01'}}
2026-05-24 16:52:01 [INFO] anomaly_detector: Starting anomaly monitoring loop (interval: 30s)
2026-05-24 16:52:01 [INFO] thermal_analyzer: Starting thermal analysis loop (interval: 30s)
2026-05-24 16:52:01 [INFO] __main__: All monitoring tasks started
2026-05-24 16:52:31 [INFO] sensor_collector: Collected readings: {'primary_chamber': 875.3, 'secondary_chamber': 842.1, 'cooling_unit': 98.5}
2026-05-24 16:52:31 [INFO] anomaly_detector: Analysis result: normal (confidence: 94.50%)
2026-05-24 16:52:31 [INFO] thermal_analyzer: Thermal analysis: normal (efficiency: 95.00%)
Pricing and ROI
The HolySheep unified API gateway delivers exceptional value for crematorium equipment health monitoring. Here is a detailed cost comparison:
| Metric | Direct API Costs | HolySheep Gateway | Savings |
|---|---|---|---|
| DeepSeek V3.2 (Anomaly Detection) | ¥7.30/MTok (market rate) | ¥1.00/MTok ($1.00) | 86% |
| Gemini 2.5 Flash (Thermal Analysis) | $2.50/MTok | $2.50/MTok (¥1.00) | ¥1 = $1 rate advantage |
| Claude Sonnet 4.5 (if used) | $15.00/MTok | $15.00/MTok (¥1.00) | ¥1 = $1 rate advantage |
| GPT-4.1 (if used) | $8.00/MTok | $8.00/MTok (¥1.00) | ¥1 = $1 rate advantage |
| Typical Monthly Cost (100 facilities) |