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7d2a23e 3cbb16e 7d2a23e 01966fa 7d2a23e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | from __future__ import annotations
import json
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, Iterable, List
import joblib
import numpy as np
from keras.models import load_model
class BundleConfigError(RuntimeError):
"""Raised when the deployed bundle is missing required files or config."""
class RequestValidationError(ValueError):
"""Raised when an inference request does not match the model contract."""
@dataclass(frozen=True)
class ModelSpec:
id: str
display_name: str
description: str
artifact_dir: str
model_path: str
input_names_path: str
input_names_format: str
output_names_path: str
output_names_format: str
x_scaler_pattern: str | None = None
y_scaler_pattern: str | None = None
input_units: Dict[str, str] = field(default_factory=dict)
output_units: Dict[str, str] = field(default_factory=dict)
sample_request: Dict[str, Any] | None = None
sample_response: Dict[str, Any] | None = None
status: str = "ready"
status_detail: str = ""
tags: List[str] = field(default_factory=list)
prediction_output_index: int = 0
@classmethod
def from_dict(cls, payload: Dict[str, Any]) -> "ModelSpec":
return cls(**payload)
def _read_names(path: Path, fmt: str) -> List[str]:
if fmt == "text_lines":
return [line.strip() for line in path.read_text().splitlines() if line.strip()]
if fmt == "csv_header":
first_line = path.read_text().splitlines()[0]
return [item.strip() for item in first_line.split(",") if item.strip()]
if fmt == "npy":
values = np.load(path, allow_pickle=True)
return [str(item) for item in values.tolist()]
raise BundleConfigError(f"Unsupported name file format: {fmt}")
class EndpointModel:
def __init__(self, bundle_root: Path, spec: ModelSpec):
self.bundle_root = bundle_root
self.spec = spec
self.artifact_root = bundle_root / spec.artifact_dir
if not self.artifact_root.exists():
raise BundleConfigError(
f"Artifact directory for model '{spec.id}' is missing: {self.artifact_root}"
)
self.input_names = _read_names(
self.artifact_root / spec.input_names_path, spec.input_names_format
)
self.output_names = _read_names(
self.artifact_root / spec.output_names_path, spec.output_names_format
)
model_file = self.artifact_root / spec.model_path
if not model_file.exists():
raise BundleConfigError(
f"Model file for '{spec.id}' is missing: {model_file}"
)
self.model = load_model(model_file, compile=False)
def _load_scaler(self, pattern: str, name: str):
scaler_path = self.artifact_root / pattern.format(name=name)
if not scaler_path.exists():
raise BundleConfigError(
f"Required scaler for '{self.spec.id}' is missing: {scaler_path}"
)
return joblib.load(scaler_path)
def _normalize_rows(self, inputs: Dict[str, float] | List[Dict[str, float]]) -> List[Dict[str, float]]:
rows = inputs if isinstance(inputs, list) else [inputs]
normalized: List[Dict[str, float]] = []
for index, row in enumerate(rows):
if not isinstance(row, dict):
raise RequestValidationError(
f"Each input row must be an object, got {type(row).__name__} at index {index}."
)
missing = [name for name in self.input_names if name not in row]
extras = [name for name in row if name not in self.input_names]
if missing or extras:
raise RequestValidationError(
f"Model '{self.spec.id}' expects inputs {self.input_names}. "
f"Missing: {missing or 'none'}. Extra: {extras or 'none'}."
)
normalized.append({name: float(row[name]) for name in self.input_names})
return normalized
def _scale_inputs(self, rows: List[Dict[str, float]]) -> np.ndarray:
matrix = np.zeros((len(rows), len(self.input_names)), dtype=np.float32)
for row_idx, row in enumerate(rows):
for col_idx, name in enumerate(self.input_names):
value = row[name]
if self.spec.x_scaler_pattern:
scaler = self._load_scaler(self.spec.x_scaler_pattern, name)
scaled = scaler.transform([[value]])[0][0]
else:
scaled = value
matrix[row_idx, col_idx] = float(scaled)
return matrix
def _unscale_outputs(self, scaled_outputs: np.ndarray) -> np.ndarray:
matrix = np.asarray(scaled_outputs, dtype=np.float32).copy()
if not self.spec.y_scaler_pattern:
return matrix
for col_idx, name in enumerate(self.output_names):
scaler = self._load_scaler(self.spec.y_scaler_pattern, name)
column = matrix[:, col_idx].reshape(-1, 1)
matrix[:, col_idx] = scaler.inverse_transform(column).reshape(-1)
return matrix
def predict(
self,
inputs: Dict[str, float] | List[Dict[str, float]],
include_scaled_outputs: bool = False,
) -> Dict[str, Any]:
rows = self._normalize_rows(inputs)
scaled_inputs = self._scale_inputs(rows)
raw_predictions = self.model.predict(scaled_inputs, verbose=0)
if isinstance(raw_predictions, list):
scaled_outputs = np.asarray(raw_predictions[self.spec.prediction_output_index])
else:
scaled_outputs = np.asarray(raw_predictions)
unscaled_outputs = self._unscale_outputs(scaled_outputs)
predictions = [
{
output_name: float(unscaled_outputs[row_idx, col_idx])
for col_idx, output_name in enumerate(self.output_names)
}
for row_idx in range(unscaled_outputs.shape[0])
]
response: Dict[str, Any] = {
"model_id": self.spec.id,
"display_name": self.spec.display_name,
"predictions": predictions,
"metadata": {
"input_order": self.input_names,
"output_order": self.output_names,
"input_units": self.spec.input_units,
"output_units": self.spec.output_units,
"num_predictions": len(predictions),
},
}
if include_scaled_outputs:
response["scaled_predictions"] = [
{
output_name: float(scaled_outputs[row_idx, col_idx])
for col_idx, output_name in enumerate(self.output_names)
}
for row_idx in range(scaled_outputs.shape[0])
]
return response
class ModelRegistry:
def __init__(self, bundle_root: Path):
self.bundle_root = Path(bundle_root)
manifest_path = self.bundle_root / "deployment_manifest.json"
if not manifest_path.exists():
raise BundleConfigError(
f"Deployment manifest is missing from bundle: {manifest_path}"
)
payload = json.loads(manifest_path.read_text())
self.bundle_info = payload.get("space", {})
self.specs = [ModelSpec.from_dict(item) for item in payload.get("models", [])]
self._models: Dict[str, EndpointModel] = {}
def available_model_ids(self) -> List[str]:
return [spec.id for spec in self.specs if spec.status == "ready"]
def describe_models(self) -> List[Dict[str, Any]]:
return [
{
"id": spec.id,
"display_name": spec.display_name,
"description": spec.description,
"status": spec.status,
"status_detail": spec.status_detail,
"input_units": spec.input_units,
"output_units": spec.output_units,
"tags": spec.tags,
}
for spec in self.specs
]
def _get_model(self, model_id: str) -> EndpointModel:
spec = next((item for item in self.specs if item.id == model_id), None)
if spec is None:
raise RequestValidationError(
f"Unknown model_id '{model_id}'. Available models: {self.available_model_ids()}."
)
if spec.status != "ready":
raise RequestValidationError(
f"Model '{model_id}' is not deployable in this bundle: {spec.status_detail or spec.status}."
)
if model_id not in self._models:
self._models[model_id] = EndpointModel(self.bundle_root, spec)
return self._models[model_id]
def predict(
self,
model_id: str,
inputs: Dict[str, float] | List[Dict[str, float]],
include_scaled_outputs: bool = False,
) -> Dict[str, Any]:
model = self._get_model(model_id)
return model.predict(inputs=inputs, include_scaled_outputs=include_scaled_outputs)
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