Visual Studio Code – Unable to start MLflow UI

Problem: Following a quicksart guide in official docs (https://mlflow.org/docs/latest/tracking/autolog.html), every time I try running mlflow ui --port 8080 inside parent directory with particular conda env activated same error keeps popping up:

C:Usersuserminiconda3envsregularLibsite-packagespydantic_internal_config.py:322: UserWarning: Valid config keys have changed in V2:
* 'schema_extra' has been renamed to 'json_schema_extra'
  warnings.warn(message, UserWarning)
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:Usersuserminiconda3envsregularScriptsmlflow.exe__main__.py", line 4, in <module>  
  File "C:Usersuserminiconda3envsregularLibsite-packagesmlflowcli.py", line 356, in <module>
    type=click.Choice([e.name for e in importlib.metadata.entry_points().get("mlflow.app", [])]),   
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'EntryPoints' object has no attribute 'get'

I have tried multiple versions, reinstalling importlib-metadata (following same issue 'EntryPoints' object has no attribute 'get' – Digital ocean), installing older versions prior to mlflow=2.10.0 as well as older versions of pydantic prior to pydantic==2.6.1 (following the advice of @BenWilson2 from DataBricks on MLflow GH issues). Would really appreciate if anyone could help me wrap my head around the issue. Thanks in advance.

Project structure:

regModel.py:

import warnings
import argparse
import logging
import pandas as pd
import numpy as np
import mlflow
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

logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)

#evaluation function
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


def run_experiment(alpha, l1_ratio):
    warnings.filterwarnings("ignore")

    # Read the wine-quality csv file from local
    data = pd.read_csv("data/red-wine-quality.csv")
    data.to_csv("data/red-wine-quality.csv", index=False)

    # 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"]]

    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("  RMSE: %s" % rmse)
    print("  MAE: %s" % mae)
    print("  R2: %s" % r2)
    mlflow.log_param("alpha", alpha)
    mlflow.log_param("l1", l1_ratio)
    mlflow.log_metric("rmse", rmse)
    mlflow.log_metric("r2", r2)
    mlflow.log_metric("mae", mae)
    mlflow.sklearn.log_model(lr, "models")

inroduction.ipynb: