app.py

# Import the libraries
import os
import uuid
import joblib
import json
import gradio as gr
import pandas as pd

from pathlib import Path

from huggingface_hub import CommitScheduler


log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent


scheduler = CommitScheduler(
    repo_id="insurace-charge-logs",
    repo_type="dataset",
    folder_path=log_folder,
    path_in_repo="data",
    every=2
)



insurace_charge_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'],label='Sex')
smoker_input = gr.Dropdown(['yes', 'no'],label='Smoker')
region_input = gr.Dropdown(['northwest', 'northeast', 'southeast', 'southwest'],label='Region')


model_output = gr.Label(label="charges")



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 = insurace_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]
                }
            ))
            f.write("\n")

    
  
  return prediction[0]


demo = gr.Interface(
    fn=predict_insurance_charge,
    inputs=[age_input,
            bmi_input,
            children_input,
            sex_input,
            smoker_input,
            region_input],
    outputs=model_output,
    title="Charge Amount Prediction",
    description="HealthyLife Insurance Charge Prediction",
    allow_flagging="auto",
    concurrency_limit=8
    )




demo.queue()
demo.launch()
# Define the predict function which will take features, convert to dataframe and make predictions using the saved model


# 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'
# Load the freshly trained model from disk
# Prepare the logging functionality




# 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

    # 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



# Set up UI components for input and output



# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"


# Launch with a load balancer
#demo.queue()
#demo.launch(share=False)