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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 logistic 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-logs",
# repo_type="dataset",
# folder_path=log_folder,
# path_in_repo="data",
# every=2
# )
# Define the predict function that runs when 'Submit' is clicked or when a API request is made
def predict_insurance_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 = insurance_charge_predictor.predict(data_point).tolist()
# While the prediction is made, log both the inputs and outputs to a local 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 round(prediction[0],2)
# 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(['female','male'],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 Charges")
# Create the interface
demo = gr.Interface(
fn=predict_insurance_charge,
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 estimating insurance charges based on customer attributes",
examples=[[33,33.44,5,'male','no','southeast'],
[58,25.175,0,'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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