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# Importing 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
# Preparing the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
scheduler = CommitScheduler(
repo_id="Insurance_Charge_Prediction_Project",
repo_type="dataset",
folder_path=log_folder,
path_in_repo="data",
every=2
)
charges_predictor = joblib.load('model.joblib')
age_input = gr.Number(label='Age')
bmi_input = gr.Number(label='BMI')
children_input = gr.Number(label='Children')
sex_input = gr.Dropdown(['male', 'female', 'N/A'], value='N/A', label='Sex')
smoker_input = gr.Dropdown(['yes', 'no', 'N/A'], value='N/A', label="Smoker")
region_input = gr.Dropdown(['southeast', 'southwest', 'northeast', 'northwest', 'N/A'],
value='N/A', label='Region')
model_output = gr.Label(label='Charges')
# The function runs when 'Submit' is clicked or when a API request is made
def predict_charges(age, bmi, children, sex, smoker, region, prediction):
sample = {
'age': age,
'bmi': bmi,
'children': children,
'sex': sex,
'smoker': smoker,
'region': region,
'prediction': prediction
}
data_point = pd.DataFrame([sample])
print('data point: ', data_point)
prediction = charges_predictor.predict(data_point).tolist()
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]
# Setting up UI components for input and output
demo = gr.Interface(
fn=predict_charges,
inputs=[age_input, bmi_input,
children_input, sex_input, smoker_input, region_input],
outputs=model_output,
title="HealthyLife Insurance Charge Prediction",
description="This API allows you to predict the appropiate charges for each patient",
flagging_mode="manual",
concurrency_limit=8
)
# Launch with a load balancer
demo.queue()
demo.launch(share=False)
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