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# Import the libraries
import os
import uuid
import joblib
import json
import gradio as gr
import pandas as pd
from huggingface_hub import CommitScheduler
from pathlib import Path
# 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'
import subprocess
try:
result = subprocess.run(['python', 'train.py'], check=True, capture_output=True, text=True)
print(f"Training done.{result.stdout}")
except subprocess.CalledProcessError as e:
print(f"Error occurred:{e.stderr}")
exit(1)
# Load the freshly trained model from disk
insurance_charge_predictor = joblib.load('model.joblib')
# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
log_folder.mkdir(parents=True, exist_ok=True) # mkdir!
scheduler = CommitScheduler(
repo_id="Keytaro/insurance-charge-mlops-logs", # 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
def predict_insurance_charge(age, bmi, children, sex, smoker, region):
# Create a DataFrame from the input values
input_data = pd.DataFrame(
[[age, bmi, children, sex, smoker, region]],
columns=['age', 'bmi', 'children', 'sex', 'smoker', 'region']
)
# Make a prediction using the loaded model
prediction = insurance_charge_predictor.predict(input_data)
# 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
inputs = [
gr.Number(label="Age (Number)"),
gr.Number(label="BMI (Number)"),
gr.Number(label="The number of children (Number)"),
gr.Dropdown(label="Sex", choices=["male", "female"]),
gr.Dropdown(label="Smoker", choices=["yes", "no"]),
gr.Dropdown(label="Region", choices=["northeast", "northwest", "southeast", "southwest"])
]
output = gr.Number(label="Predicted Insurance Charge")
# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
demo = gr.Interface(
fn=predict_insurance_charge,
inputs=inputs,
outputs=output,
title="HealthyLife Insurance Charge Predictor",
description="This API allows you to predict the insurance charges based on personal health data.",
allow_flagging="auto", #
concurrency_limit=8 #
)
# Launch with a load balancer
demo.queue()
demo.launch(share=False)
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