How do I create an app such that everytime any user enters our login uri ,it prompts him to enter username and password and only in case of correct password it allow him
Basically I tried using Mlflow client and evironemnt variables bit its not working. It showed the user name and login option only once and then it didn't showed it.
# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
import os
import warnings
import sys
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from urllib.parse import urlparse
import mlflow
from mlflow.models import infer_signature
import mlflow.sklearn
import logging
logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)
def eval_metrics(actual, pred):
rmse = np.sqrt(mean_squared_error(actual, pred))
mae = mean_absolute_error(actual, pred)
r2 = r2_score(actual, pred)
return rmse, mae, r2
if __name__ == "__main__":
warnings.filterwarnings("ignore")
np.random.seed(40)
# Read the wine-quality csv file from the URL
csv_url = (
"https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-red.csv"
)
try:
data = pd.read_csv(csv_url, sep=";")
except Exception as e:
logger.exception(
"Unable to download training & test CSV, check your internet connection. Error: %s", e
)
# Split the data into training and test sets. (0.75, 0.25) split.
train, test = train_test_split(data)
# The predicted column is "quality" which is a scalar from [3, 9]
train_x = train.drop(["quality"], axis=1)
test_x = test.drop(["quality"], axis=1)
train_y = train[["quality"]]
test_y = test[["quality"]]
alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.7
l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.8
from mlflow import MlflowClient
from mlflow.server import get_app_client
tracking_uri = "http://localhost:5000/"
auth_client = get_app_client("basic-auth", tracking_uri=tracking_uri)
auth_client.create_user(username="user1", password="pw1")
auth_client.create_user(username="user2", password="pw2")
client = MlflowClient(tracking_uri=tracking_uri)
experiment_id = client.create_experiment(name="experiment1")
auth_client.create_experiment_permission(
experiment_id=experiment_id, username="user2", permission="MANAGE"
)
with mlflow.start_run():
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
lr.fit(train_x, train_y)
predicted_qualities = lr.predict(test_x)
(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
print("Elasticnet model (alpha={:f}, l1_ratio={:f}):".format(alpha, l1_ratio))
print(f" RMSE: {rmse}")
print(f" MAE: {mae}")
print(f" R2: {r2}")
mlflow.log_param("alpha", alpha)
mlflow.log_param("l1_ratio", l1_ratio)
mlflow.log_metric("rmse", rmse)
mlflow.log_metric("r2", r2)
mlflow.log_metric("mae", mae)
predictions = lr.predict(train_x)
signature = infer_signature(train_x, predictions)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
# Model registry does not work with file store
if tracking_url_type_store != "file":
mlflow.sklearn.log_model(
lr, "model", registered_model_name="ElasticnetWineModel", signature=signature
)
else:
mlflow.sklearn.log_model(lr, "model", signature=signature)
