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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 train
train
# Load the freshly trained model from disk
saved_model = joblib.load('model.joblib')
# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
scheduler = CommitScheduler(
repo_id="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_charge(age, bmi, children, sex, smoker, region ):
sample = {
'age': age,
'bmi': bmi,
'children': children,
'sex': sex,
'smoker': smoker,
'region': region,
}
data_point = pd.DataFrame([sample])
prediction = saved_model.predict(data_point).tolist()
# if prediction is less than zero assign zero
if prediction[0] < 0:
prediction[0] = 0
# 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
# age = gr.Number(label="age")
age = gr.Slider(1, 100, step =1, minimum =1, maximum = 100, label="age", info='Age between 1 and 100')
bmi = gr.Number(label="bmi")
# children = gr.Number(label='children')
children = gr.Slider(label='children', step =1, minimum =0, maximum = 10, info = 'Enter number of children')
sex = gr.Dropdown(label='sex', choices=['male', 'female'])
smoker = gr.Dropdown(label='smoker', choices=['yes', 'no'])
region = gr.Dropdown(label='region', choices =['southwest', 'southeast', 'northwest', 'northeast'])
charge = gr.Number(label="Prediction")
# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
demo = gr.Interface(
fn = predict_charge,
inputs = [age, bmi, children, sex, smoker, region,],
outputs = charge,
title = 'HealthyLife Insurance Charge Prediction',
description = 'Calculate charges')
# Launch with a load balancer
demo.queue()
demo.launch(share=False)