# Before: Manual resource allocation
def allocate_servers(base_count: int) -> int:
    return base_count + int(base_count * 0.4)  # 40% buffer always

# After: AI-driven predictive allocation  
async def predict_and_allocate(historical_data: DataFrame) -> AllocationPlan:
    demand_forecast = await demand_model.predict(historical_data)
    optimal_allocation = rl_agent.act(current_state, demand_forecast)
    return AllocationPlan.from_prediction(optimal_allocation)

# Scattered, hard-to-maintain training code
import tensorflow as tf
model = tf.keras.Sequential([...])  # No version tracking
model.fit(data)  # No reproducibility
model.save("model.h5")  # No metadata

from domain.demand import DemandForecastConfig
from models.training import ModelTrainer
from infra.monitoring import log_training_metrics

@dataclass(slots=True, frozen=True)
class TrainingConfig:
    model_version: str
    data_hash: str
    seed: int = 42

async def train_demand_model(config: TrainingConfig) -> ModelArtifact:
    """Train demand forecasting model with full auditability.
    
    Examples:
        >>> config = TrainingConfig("v2.1.0", "abc123", 42)
        >>> artifact = await train_demand_model(config)
        >>> assert artifact.metrics.mae <= 0.02
    """
    torch.manual_seed(config.seed)
    
    with CodeCarbon() as tracker:
        model = DemandForecaster.from_config(config)
        metrics = await model.train(golden_dataset)
        
    artifact = ModelArtifact(
        model=model,
        version=config.model_version,
        data_hash=config.data_hash,
        metrics=metrics,
        energy_kwh=tracker.energy_consumed,
        co2e_kg=tracker.co2_emissions
    )
    
    await artifact.save_with_card()
    return artifact

# Brittle, unmonitored endpoint
@app.post("/allocate")  
def allocate(request):
    try:
        return some_ml_model.predict(request)
    except:
        return {"error": "something broke"}

from api.schemas import AllocationRequest, AllocationResponse
from domain.allocation import AllocationService
from infra.monitoring import track_inference_latency

@router.post("/v1/allocations", response_model=AllocationResponse)
@track_inference_latency
async def allocate_resources(
    req: AllocationRequest, 
    svc: AllocationService = Depends()
) -> AllocationResponse:
    """Allocate resources using AI optimization.
    
    Returns:
        AllocationResponse with confidence scores and energy impact
        
    Raises:
        HTTPException: 422 on validation errors
        HTTPException: 503 on model unavailable
    """
    if req.demand.quantity == 0:
        return AllocationResponse.empty()
        
    try:
        allocation = await svc.predict_optimal_allocation(req)
        
        if allocation.confidence < 0.8:
            await svc.trigger_human_review(req, allocation)
            
        return AllocationResponse(
            allocation=allocation,
            energy_impact=allocation.estimated_kwh,
            co2_impact=allocation.estimated_co2e
        )
        
    except (ModelUnavailableError, ResourceNotFoundError) as exc:
        logger.exception("Allocation failed", extra={"request_id": req.id})
        raise HTTPException(status_code=503, detail=str(exc)) from exc

from infra.sustainability import EthicsValidator, CarbonTracker
from models.validation import BiasDetector

async def train_and_validate_model(config: TrainingConfig) -> DeploymentDecision:
    """Train model with mandatory sustainability and ethics checks."""
    
    # Carbon tracking built-in
    with CarbonTracker() as carbon:
        model = await train_rl_agent(config)
        
    # Mandatory bias detection
    bias_report = BiasDetector.analyze(model, protected_features)
    if bias_report.parity_score < 0.8:
        return DeploymentDecision.blocked(
            reason="Bias detected in protected features",
            report=bias_report
        )
    
    # Energy efficiency validation
    if carbon.kwh_per_decision > 1.0:  # Exceeds sustainability threshold
        return DeploymentDecision.blocked(
            reason=f"Energy cost too high: {carbon.kwh_per_decision} Wh/decision"
        )
        
    return DeploymentDecision.approved(
        model=model,
        carbon_footprint=carbon.summary(),
        ethics_clearance=bias_report
    )

# Set up your development environment
pip install ruff mypy black pytest-asyncio locust codecarbon aequitas
pip install tensorflow torch scikit-learn pandas fastapi

# Configure pre-commit hooks
cat > .pre-commit-config.yaml << EOF
repos:
  - repo: local
    hooks:
      - id: ruff
        name: ruff
        entry: ruff check
        language: python
      - id: mypy
        name: mypy
        entry: mypy --strict
        language: python
      - id: black
        name: black
        entry: black --line-length 100
        language: python
EOF

mkdir -p {domain,pipelines,models,api,infra}/{__init__.py}
touch {domain,pipelines,models,api,infra}/__init__.py

# Create your first resource allocation domain model
cat > domain/allocation.py << 'EOF'
from __future__ import annotations
from dataclasses import dataclass
from typing import Protocol

@dataclass(slots=True, frozen=True)
class Resource:
    id: str
    capacity: float
    energy_cost_per_unit: float
    
@dataclass(slots=True, frozen=True) 
class Allocation:
    resource_id: str
    allocated_units: float
    confidence_score: float
    estimated_kwh: float
    estimated_co2e: float
    
    @classmethod
    def none(cls) -> Allocation:
        return cls("", 0.0, 1.0, 0.0, 0.0)

class AllocationService(Protocol):
    async def predict_optimal_allocation(self, req: AllocationRequest) -> Allocation:
        ...
EOF

# models/demand_forecaster.py
from __future__ import annotations
import torch
import torch.nn as nn
from dataclasses import dataclass
from typing import Dict, Any

@dataclass(slots=True, frozen=True)
class ForecastConfig:
    sequence_length: int = 24
    hidden_size: int = 128
    num_layers: int = 2
    dropout: float = 0.1

class DemandForecaster(nn.Module):
    """LSTM-based demand forecasting model."""
    
    def __init__(self, config: ForecastConfig):
        super().__init__()
        self.config = config
        self.lstm = nn.LSTM(
            input_size=1,
            hidden_size=config.hidden_size,
            num_layers=config.num_layers,
            dropout=config.dropout,
            batch_first=True
        )
        self.linear = nn.Linear(config.hidden_size, 1)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass with shape validation.
        
        Args:
            x: Input tensor of shape (batch_size, seq_len, 1)
            
        Returns:
            Predictions of shape (batch_size, 1)
        """
        if x.dim() != 3:
            raise ValueError(f"Expected 3D input, got {x.dim()}D")
            
        lstm_out, _ = self.lstm(x)
        predictions = self.linear(lstm_out[:, -1, :])
        return predictions

# api/main.py
from fastapi import FastAPI, HTTPException
from prometheus_client import Counter, Histogram
import time

app = FastAPI(title="AI Resource Manager", version="1.0.0")

# Prometheus metrics
request_counter = Counter('api_requests_total', 'Total API requests')
request_duration = Histogram('api_request_duration_seconds', 'Request duration')

@app.middleware("http")
async def monitor_requests(request, call_next):
    start_time = time.time()
    response = await call_next(request)
    
    request_counter.inc()
    request_duration.observe(time.time() - start_time)
    
    return response

@app.get("/healthz")
async def health_check():
    return {"status": "healthy"}

@app.get("/metrics")
async def metrics():
    # Return Prometheus metrics
    pass

# Before: Static allocation
servers_needed = peak_demand * 1.4  # Always over-provision by 40%
monthly_cost = servers_needed * cost_per_server * 24 * 30

# After: AI-driven dynamic allocation  
predicted_demand = await demand_model.forecast(historical_data)
optimal_servers = rl_agent.optimize(predicted_demand, cost_constraints)
monthly_cost = sum(hourly_allocation * cost_per_server for hourly_allocation in optimal_servers)

# Typical savings: 30-40% reduction in compute costs
# Energy savings: 25-35% reduction in kWh consumption
# SLA improvement: 99.9% uptime vs 99.5% with manual allocation