# Importing libraries
import joblib
import gradio as gr
import pandas as pd

charges_predictor = joblib.load("model.joblib")


age_input = gr.Number(label="Age", value=25)
bmi_input = gr.Number(label="BMI", value=25)
children_input = gr.Number(label="Children", value=0)

sex_input = gr.Dropdown(
    ["male","female"],
    value="male",
    label="Sex"
)

smoker_input = gr.Dropdown(
    ["yes","no"],
    value="no",
    label="Smoker"
)

region_input = gr.Dropdown(
    ["southeast","southwest","northeast","northwest"],
    value="southeast",
    label="Region"
)


model_output = gr.Textbox(label="Predicted Insurance Cost")


def predict_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 = charges_predictor.predict(data_point)

    return f"${prediction[0]:,.2f}"


demo = gr.Interface(
    fn=predict_charges,
    inputs=[
        age_input,
        bmi_input,
        children_input,
        sex_input,
        smoker_input,
        region_input
    ],
    outputs=model_output,
    title="Insurance Charge Prediction",
    description="Predict insurance medical charges based on user information"
)

demo.launch()