app (2).py

# Import the libraries

import os 
import uuid
import joblib
import json

import gradio as gr
import pandas as pd 

# 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'

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-mlops-logs",  # provide a name "insurance-charge-mlops-logs" for the repo_id
    repo_type="dataset",
    folder_path=log_folder,
    path_in_repo="data",
    every=2
)

# 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_charge(age, sex, bmi, children, somker, region):
    smaple = {'age': age,
              'bmi': bmi,
              'children': children,
              'sex': sex,
              'smoker': smoker,
              'region': region,
              'prediction': prediction[0]
             }
    data_point = pd.DataFrame([sample])
    prediction = insurance_charge_predicter.predict(data_point).tolist()

    # 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

    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]



# 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(["male","female"], 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 Chaeges")

# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
demo = gr.Interface(
    fn=predict_insurance_charge,
    inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
    outputs = model_output,
    title = "Healthy Insurence Candidate Perdiction",
    description = "This API will predict and estimate isurance charges based on candidate's attributes"
    examples = [[33,33.44,5,"male","no", "southeast"],
                [40,38.20,2,"female","no", "northwest"],
                [52,36.20,0,"male","no", "northwest"]],
    concurrency_limit = 16

)

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