app.py

# 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)