# 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) | |