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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']) | |