File size: 1,403 Bytes
4b9c4a3 c81191b 4b9c4a3 c81191b 4b9c4a3 56be571 67f0a4a 232c77b 67f0a4a 87c7e78 67f0a4a 4b9c4a3 3f5e8cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | 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() |