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from warnings import filterwarnings
filterwarnings('ignore')
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
# Configure the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
repo_id = "eric-green-insurance-charge-predictor-logs"
# Create a commit scheduler
scheduler = CommitScheduler(
repo_id=repo_id,
repo_type="dataset",
folder_path=log_folder,
path_in_repo="data",
every=2
)
# Load the saved model
insurance_charge_predictor = joblib.load('model.joblib')
# Define the input features
#numeric_features = ['age', 'bmi', 'children']
#categorical_features = ['sex', 'smoker', 'region']
age_input = gr.Number(label="Age")
bmi_input = gr.Number(label="BMI")
children_input = gr.Number(label="Children")
# sex: ['female' 'male']
# smoker: ['yes' 'no']
# region: ['southwest' 'southeast' 'northwest' 'northeast']
sex_input = gr.Dropdown(['female','male'],label='Sex')
smoker_input = gr.Dropdown(['yes','no'],label='Smoker')
region_input = gr.Dropdown(['southwest', 'southeast', 'northwest', 'northeast'],label='Region')
model_output = gr.Label(label="charges")
# 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_charges(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()
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]
}
))
return prediction[0]
gr_interface = gr.Interface(
fn=predict_insurance_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 insurance charges based on the input features.",
allow_flagging="auto",
concurrency_limit=8
)
gr_interface.queue()
gr_interface.launch(share=False)
import subprocess
# Run the training script
subprocess.run(["python", "train.py"])
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