добавить веб-сервис предсказания цен, с dockerfile

services/ml_service/.dockerignore

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+### Python
+__pycache__/
+*.pyc

services/ml_service/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /
+
+COPY . /service
+
+RUN pip install --no-cache-dir -r /service/requirements.txt
+
+VOLUME /models
+
+EXPOSE 8000/tcp
+
+ENV MODELS_PATH=/models
+
+WORKDIR /service
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
+
+# docker build -t ml_service services/ml_service/
+# docker run -v "$(pwd)/services/models:/models" -p 8000:8000 ml_service

services/ml_service/app/__init__.py

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+from ._meta import PACKAGE_PATH
+from .main import app

services/ml_service/app/_meta.py

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+from pathlib import Path
+
+
+PACKAGE_PATH = Path(__file__).parent

services/ml_service/app/main.py

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+from os import getenv
+from pathlib import Path
+
+from fastapi import FastAPI
+from pydantic import BaseModel, Field
+
+from ._meta import PACKAGE_PATH
+from .predictor import (
+    FuelType, SellingType, TransmissionType, PricePredictionFeatures, PricePredictor,
+)
+
+
+MODELS_PATH = getenv('MODELS_PATH', None)
+if MODELS_PATH is not None:
+    MODELS_PATH = Path(MODELS_PATH)
+else:
+    SERVICES_PATH = PACKAGE_PATH.parents[1]
+    assert SERVICES_PATH.name == 'services'
+    MODELS_PATH = SERVICES_PATH / 'models'
+
+MODEL_PATH = MODELS_PATH / 'model.pkl'
+
+
+predictor = PricePredictor(MODEL_PATH)
+
+
+API_BASE_PATH = '/api'
+
+
+app = FastAPI(
+    title='Сервис ML',
+    version='0.1.0',
+    root_path=API_BASE_PATH,
+    #redoc_url=None,
+)
+
+
+@app.get('/', summary='Тестовый эндпоинт')
+async def root():
+    return {'Hello': 'World'}
+
+
+class PricePredictionRequest(BaseModel):
+
+    selling_price: float = Field(..., gt=0)
+    driven_kms: float = Field(..., ge=0)
+    age: float = Field(..., ge=0)
+    fuel_type: FuelType
+    selling_type: SellingType
+    transmission_type: TransmissionType
+
+
+@app.post('/predict', summary='Предсказать цену подержанного автомобиля')
+def predict_price(item_id: int, req: PricePredictionRequest):
+    features = PricePredictionFeatures(
+        selling_price=req.selling_price,
+        driven_kms=req.driven_kms,
+        age=req.age,
+        fuel_type=req.fuel_type,
+        selling_type=req.selling_type,
+        transmission_type=req.transmission_type,
+    )
+    pred = predictor.predict(features)
+    return {'item_id': item_id, 'price': pred}

services/ml_service/app/predictor.py

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+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])

services/ml_service/requirements.txt

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+fastapi ~=0.120.4
+mlxtend ~=0.23.4
+pandas >=2.3.1,<3
+scikit-learn >=1.7.2,<2
+uvicorn ~=0.38.0

services/models/.gitignore

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+*.pkl

services/models/fetch_model_as_pickle_from_mlflow.py

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+from argparse import ArgumentParser
+from pathlib import Path
+from pickle import dump
+from sys import exit as sys_exit, argv as sys_argv
+
+from mlflow import set_tracking_uri, set_registry_uri
+from mlflow.sklearn import load_model
+
+
+MLFLOW_TRACKING_URI_DEFAULT = 'http://localhost:5000'
+
+
+def open_file_for_model(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, 'wb', buffering=buffering, opener=opener)
+
+
+def dump_model_to_file(model, file):
+    return dump(model, file)
+
+
+def dump_model_to_path(model, 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(map(repr, kwargs_extra.keys())))
+        )
+    with open_file_for_model(
+        path, buffering=buffering, opener=opener, **open_kwargs_extra,
+    ) as model_file:
+        return dump_model_to_file(model, model_file)
+
+
+def parse_args(argv):
+    parser = ArgumentParser(
+        description=(
+            'Скачать модель с tracking server MLFlow и сохранить в локальный файл pickle'
+        ),
+        allow_abbrev=False,
+        exit_on_error=True,
+    )
+    model_ref_parser = parser.add_mutually_exclusive_group(required=True)
+    model_ref_parser.add_argument('-m', '--model', type=str, dest='model_uri')
+    model_ref_parser.add_argument('--run', type=str, dest='run_id')
+    parser.add_argument(
+        '--tracking-uri', default=MLFLOW_TRACKING_URI_DEFAULT, type=str, dest='tracking_uri',
+    )
+    parser.add_argument('--registry-uri', type=str, dest='registry_uri')
+    parser.add_argument('out_path', default=Path('.'), type=Path)
+    args = parser.parse_args(argv)
+    return args
+
+
+def main(argv):
+    args = parse_args(argv)
+    set_tracking_uri(args.tracking_uri)
+    if args.registry_uri is not None:
+        set_registry_uri(args.registry_uri)
+    if args.model_uri is not None:
+        model_uri = args.model_uri
+    elif args.run_id is not None:
+        model_uri = f'runs:/{args.run_id}/model'
+    else:
+        assert False
+        return 1
+    model = load_model(model_uri)
+    dump_model_to_path(model, args.out_path)
+    return 0
+
+
+if __name__ == '__main__':
+    sys_exit(int(main(sys_argv) or 0))