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import os
import logging
from typing import Union
import mlflow
import numpy as np
from fastapi import FastAPI
from pydantic import BaseModel
from config import settings
try:
path_mlflow_model = "./model_for_production/"
sklearn_pipeline = mlflow.sklearn.load_model(path_mlflow_model)
except:
path_mlflow_model = "/data/model_for_production/"
sklearn_pipeline = mlflow.sklearn.load_model(path_mlflow_model)
app = FastAPI()
logging.basicConfig(level=logging.INFO)
class WaterPotabilityDataItem(BaseModel):
ph: Union[float, None] = np.nan
Hardness: Union[float, None] = np.nan
Solids: Union[float, None] = np.nan
Chloramines: Union[float, None] = np.nan
Sulfate: Union[float, None] = np.nan
Conductivity: Union[float, None] = np.nan
Organic_carbon: Union[float, None] = np.nan
Trihalomethanes: Union[float, None] = np.nan
Turbidity: Union[float, None] = np.nan
def predict_pipeline(data_sample):
"""
---------
Arguments
---------
data_sample : np.array
a numpy array of shape (num_samples, num_feats)
-------
Returns
-------
pred_sample : np.array
a numpy array of shape (num_samples) with predictions
"""
pred_sample = sklearn_pipeline.predict(data_sample)
return pred_sample
@app.get("/info")
def get_app_info():
"""
-------
Returns
-------
dict_info : dict
a dictionary with info to be sent as a response to get request
"""
dict_info = {"app_name": settings.app_name, "version": settings.version}
return dict_info
@app.post("/predict")
def predict(wpd_item: WaterPotabilityDataItem):
"""
---------
Arguments
---------
wpd_item : object
an object of type WaterPotabilityDataItem
-------
Returns
-------
pred_dict : dict
a dictionary of prediction to be sent as a response to post request
"""
wpd_arr = np.array(
[
wpd_item.ph,
wpd_item.Hardness,
wpd_item.Solids,
wpd_item.Chloramines,
wpd_item.Sulfate,
wpd_item.Conductivity,
wpd_item.Organic_carbon,
wpd_item.Trihalomethanes,
wpd_item.Turbidity,
]
).reshape(1, -1)
logging.info("data sample: %s", wpd_arr)
pred_sample = predict_pipeline(wpd_arr)
logging.info("Potability prediction: %s", pred_sample)
pred_dict = {"Potability": int(pred_sample)}
return pred_dict
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