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#import os
#import gradio as gr
#import joblib
#import subprocess
#import pandas as pd
#import json
#from pathlib import Path
#from threading import Lock
#from huggingface_hub import CommitScheduler
#import uuid
#from huggingface_hub import HfApi
import seaborn as sns
import pandas as pd
import numpy as np
import pyod
import pyreadr
import urllib
import rdata
import wget
import os
import gradio as gr
import joblib
import subprocess
import pandas as pd
import json
import uuid
from sklearn.metrics import f1_score, confusion_matrix
from pyod.models.mcd import MCD
from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from pathlib import Path
from threading import Lock
from huggingface_hub import CommitScheduler
from huggingface_hub import HfApi
#from IPython.display import display, HTML
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore")
# Run the training script placed in the same directory as app.py
# The training script will train and persist a linear regression
# model with the filename 'model.joblib'
subprocess.run(['python', 'train.py'])
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