# Import the libraries
import time  # Import the time module for time-related functions
from gradio_client import Client  # Import the Gradio client for interacting with Gradio interfaces
from sklearn.datasets import fetch_openml  # Import fetch_openml to fetch datasets
from sklearn.model_selection import train_test_split  # Import train_test_split for splitting datasets
from tqdm import tqdm  # Import tqdm for progress bars


# 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'




# Load the freshly trained model from disk


# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent

scheduler = CommitScheduler(
    repo_id="-----------",  # provide a name "insurance-charge-mlops-logs" for the repo_id
    repo_type="dataset",
    folder_path=log_folder,
    path_in_repo="data",
    every=2
)

# Define the predict function which will take features, convert to dataframe and make predictions using the saved model
# the functions runs when 'Submit' is clicked or when a API request is made


    # While the prediction is made, log both the inputs and outputs to a  log file
    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
    # access

    with scheduler.lock:
        with log_file.open("a") as f:
            f.write(json.dumps(
                {
                    'age': age,
                    'bmi': bmi,
                    'children': children,
                    'sex': sex,
                    'smoker': smoker,
                    'region': region,
                    'prediction': prediction[0]
                }
            ))
            f.write("\n")

    return prediction[0]



# Set up UI components for input and output



# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"


# Launch with a load balancer
demo.queue()
demo.launch(share=False)