import joblib
import gradio as gr
import pandas as pd
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[['score','previously_insured',
                                    'annual_premium','vintage','gender',
                                    'age','region_code','policy_sales_channel',
                                    'driving_license','vehicle_age',
                                    'vehicle_damage']] 

def create_input_table():
    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 create_output_table():
    return gr.Dataframe(headers = ['score','previously_insured',
                                    'annual_premium','vintage','gender',
                                    'age','region_code','policy_sales_channel',
                                    'driving_license','vehicle_age',
                                    'vehicle_damage'],
                        type = 'pandas',
                        label = 'Output',
                        wrap = True,
                        interactive =False)

def create_file_object():
    return gr.File(label = 'File upload',
                    type = 'bytes')

def convert_file_to_pandas(file):

    df = pd.read_excel(io = file)

    return df

def build_interface():

    with gr.Blocks() as interface:

        file_object = create_file_object()
        input_table = create_input_table()
        output_table = create_output_table()

        greet_btn = gr.Button("Submit")
        greet_btn.click(fn=predict, inputs=input_table, outputs=output_table)

        file_object.change(fn = convert_file_to_pandas, 
                           inputs = file_object, 
                           outputs = input_table)
    
    interface.launch()


if __name__ == "__main__":
    load_data()
    build_interface()