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# Import the libraries
# 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)
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