import gradio as gr
import numpy as np
from joblib import load

rf = load('random_forest_model.joblib')

columns = ['gp', 'min', 'pts', 'fgm', 
           'fga', 'fg', '3p_made', '3pa', 
           'ftm', 'fta', 'ft', 'oreb',
           'dreb', 'reb', 'ast', 'stl',
           'blk', 'tov']


def predict(gp, miin, pts, fgm, fga, fg, p_made, pa, ftm, fta, ft, oreb, dreb, reb, ast, stl, blk, tov):
    data = np.array([[gp, miin, pts, fgm, fga, fg, p_made, pa, ftm, fta, ft, oreb, dreb, reb, ast, stl, blk, tov]])
    pred = rf.predict(data)
    return {'target_5yrs': pred[0]}

inputs = [
    gr.Number(label="gp"),
    gr.Number(label="min"),
    gr.Number(label="pts"),
    gr.Number(label="fgm"),
    
    gr.Number(label="fga"),
    gr.Number(label="fg"),
    gr.Number(label="3p_made"),
    gr.Number(label="3pa"),

    gr.Number(label="ftm"),
    gr.Number(label="fta"),
    gr.Number(label="ft"),
    gr.Number(label="oreb"),

    gr.Number(label="dreb"),
    gr.Number(label="reb"),
    gr.Number(label="ast"),
    gr.Number(label="stl"),

    gr.Number(label="blk"),
    gr.Number(label="tov")
]

output = gr.Label(num_top_classes=1)

iface = gr.Interface(fn=predict, inputs=inputs, outputs=output,
                     description="O modelo em questão classifica 0, se a carreira de um jogador de basquete irá durar menos de 5 anos, e 1 se a carreira durar 5 ou mais anos.")

iface.launch()