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# Import the libraries
import os
import uuid
import joblib
import json
import gradio as gr
import pandas as pd
# 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="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, sex, bmi, children, somker, region):
smaple = {'age': age,
'bmi': bmi,
'children': children,
'sex': sex,
'smoker': smoker,
'region': region,
'prediction': prediction[0]
}
data_point = pd.DataFrame([sample])
prediction = insurance_charge_predicter.predict(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_input = gr.Dropdown(["male","female"], label = "sex")
smoker_input = gr.Dropdown(["Yes","No"], label = "smoker")
region_input = gr.Dropdown(["southeast","southwest", "northwest", "northeast"], label = "region")
model_output = gr.Label( label = " Insurance Chaeges")
# 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],
outputs = model_output,
title = "Healthy Insurence Candidate Perdiction",
description = "This API will predict and estimate isurance charges based on candidate's attributes"
examples = [[33,33.44,5,"male","no", "southeast"],
[40,38.20,2,"female","no", "northwest"],
[52,36.20,0,"male","no", "northwest"]],
concurrency_limit = 16
)
# Launch with a load balancer
demo.queue()
demo.launch(share=False)
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