import joblib
import gradio as gr
from models import HealthInsurance

def load_data():
    global _model 
    global _column_transformer
    global _bins_annual_premium_type

    _model = joblib.load(filename = 'parameters/random_forrest.gz')
    _column_transformer = joblib.load(filename = 'parameters/column_transformer.joblib')
    _bins_annual_premium_type = joblib.load(filename = 'parameters/bins_annual_premium_type.joblib')

def predict(df):

    health_insurance = HealthInsurance(_model,_column_transformer,
                                            _bins_annual_premium_type)
    
    df_predicted = health_insurance.predict(df)   

    return df_predicted

def input():
    return gr.Dataframe(headers = ['previously_insured',
                                    'annual_premium','vintage','gender',
                                    'age','region_code','policy_sales_channel',
                                    'driving_license','vehicle_age',
                                    'vehicle_damage'],
                        datatype = ['number','number','number','str','number',
                                    'number','number','number','str','str'],
                        row_count= 1,
                        col_count= (10,'fixed'),
                        type = 'pandas',
                        label = 'Input')

def output():
    return gr.Dataframe(headers = ['previously_insured',
                                    'annual_premium','vintage','gender',
                                    'age','region_code','policy_sales_channel',
                                    'driving_license','vehicle_age',
                                    'vehicle_damage', 'score'],
                        datatype = ['number','number','number','str','number',
                                    'number','number','number','str','str',
                                    'number'],
                        type = 'pandas',
                        label = 'Output')

if __name__ == "__main__":

    load_data()
    iface = gr.Interface(fn=predict, 
                        inputs=input(), 
                        outputs=output(),
                        title='Health Insurance App'
                        )
    iface.launch()