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from typing import Optional, Dict, Any
from sklearn.pipeline import Pipeline
import joblib
import logging
logger = logging.getLogger(__name__)
class Model:
_current: Optional['Model'] = None
def __init__(self,
metadata: Optional[Dict[str, Any]] = None,
pipeline: Pipeline = None):
self._pipeline = pipeline
self._metadata = metadata
@property
def pipeline(self) -> Optional[Pipeline]:
return self._pipeline
@pipeline.setter
def pipeline(self, value: Optional[Pipeline]):
self._pipeline = value
@property
def metadata(self) -> Optional[str]:
return self._metadata
@metadata.setter
def metadata(self, value: Optional[str]):
self._metadata = value
@classmethod
def get_instance(cls) -> 'Model':
if cls._current is None:
cls.load_model()
return cls._current
@classmethod
def clear_instance(cls):
cls._current = None
@classmethod
def load_model(cls, path: str = './data/model.pkl') -> 'Model':
"""
Load the model from the given path
"""
data = joblib.load(path)
cls._current = Model(pipeline=data['pipeline'], metadata=data['metadata'])
logging.info("Model loaded")
return cls._current
@classmethod
def save_model(cls,
pipeline: Pipeline,
metadata: Dict,
path: str = './data/model.pkl') -> None:
"""
Save the model to the given path
"""
logging.info(f"Saving model to {path}")
data = {
'pipeline': pipeline,
'metadata': metadata
}
joblib.dump(data, path)
logging.info("Model saved")
# Update the current instance
cls._current = Model(pipeline=pipeline, metadata=metadata)
logging.info("Model instance updated") |