добавить веб-сервис предсказания цен, с dockerfile

services/ml_service/app/predictor.py

@@ -0,0 +1,81 @@
+from dataclasses import dataclass
+from enum import Enum
+from pandas import DataFrame
+from pickle import load
+
+
+def open_model_file(file, *, buffering=-1, opener=None, **kwargs_extra):
+    open_kwargs_extra = {}
+    if 'closefd' in kwargs_extra:
+        open_kwargs_extra['closefd'] = kwargs_extra.pop('closefd')
+    if len(kwargs_extra) > 0:
+        raise TypeError(
+            'Unexpected keyword arguments given: {}'
+            .format(', '.join(map(repr, kwargs_extra.keys())))
+        )
+    return open(file, 'rb', buffering=buffering, opener=opener)
+
+
+def load_model_from_file(file):
+    return load(file)
+
+
+def load_model_from_path(path, *, buffering=-1, opener=None, **kwargs_extra):
+    open_kwargs_extra = {}
+    for k in ('closefd',):
+        if k in kwargs_extra:
+            open_kwargs_extra[k] = kwargs_extra.pop(k)
+    if len(kwargs_extra) > 0:
+        raise TypeError(
+            'Unexpected keyword arguments given: {}'.format(', '.join(kwargs_extra.keys()))
+        )
+    with open_model_file(
+        path, buffering=buffering, opener=opener, **open_kwargs_extra,
+    ) as model_file:
+        return load_model_from_file(model_file)
+
+
+class FuelType(Enum):
+    PETROL = 'petrol'
+    DIESEL = 'diesel'
+    CNG = 'cng'
+
+
+class SellingType(Enum):
+    DEALER  = 'dealer'
+    INDIVIDUAL  = 'individual'
+
+
+class TransmissionType(Enum):
+    MANUAL  = 'manual'
+    AUTOMATIC   = 'automatic'
+
+
+@dataclass
+class PricePredictionFeatures:
+    selling_price: float
+    driven_kms: float
+    age: float
+    fuel_type: FuelType
+    selling_type: SellingType
+    transmission_type: TransmissionType
+
+
+class PricePredictor:
+
+    def __init__(self, model_path):
+        self._model = load_model_from_path(model_path)
+
+    def predict(self, features):
+        # WARN: порядок столбцов вроде имеет значение
+        features_df = DataFrame([{
+            'selling_price':    features.selling_price,
+            'driven_kms':   features.driven_kms,
+            'fuel_type':    features.fuel_type.value,
+            'selling_type': features.selling_type.value,
+            'transmission': features.transmission_type.value,
+            'age':  features.age,
+        }])
+        predictions = self._model.predict(features_df)
+        assert len(predictions) == 1
+        return float(predictions[0])