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# Import the libraries
import os
import uuid
import joblib
importjson
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'
os.system("python train.py")
# 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
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
def predict_insurance_charge(age,sex,bmi,children,smoker,region):
sample = {
'age':age,
'bmi':bmi,
'children':children,
'sex':sex,
'smoker':smoker,
'region':region
}
data_point = pd.DataFrame([sample])
prediction = insurance_charge_predictor(data_point).tolist()
# 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_input = gr.Number(label='age')
bmi_input = gr.Number(label='bmi')
children_input = gr.Number(label='children')
sex_ipnut = gr.Dropdown(['male','female'],label='sex')
smoker_input = gr.Dropdown(['yes','no'],label='smoker')
region_input = gr.Dropdown(
['southeast','southwest','northwest','northeast'],
lable='region'
)
model_output = gr.label(label="Insurace Charges")
# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
demo = gr.interface(
fn=predict_insurance_charge,
inputs=[age_input,bmi_input,children_input,sex_input,smoker_input,region_input],
output=model_output,
title="HealthyLife Insurance Charge Prediction",
description="This API allows you to predict the estimating insurnace charges based on customer attributes",
examples=[[33,33.44,5,'male','no','southeast'],
[58,25.177,0,'male','no','northeast'],
[52,38.380,2,'female','no','northeast']],
concurrency_limit=16
)
# Launch with a load balancer
demo.queue()
demo.launch(share=False)
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