Lesson6 / app.py
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import gradio as gr
import torch, numpy as np, pandas as pd
import skimage
import pickle
defaultColumns = [
'matchType',
'assists',
'boosts',
'damageDealt',
'DBNOs',
'headshotKills',
'heals',
'killPlace',
'killPoints',
'kills',
'killStreaks',
'longestKill',
'matchDuration',
'maxPlace',
'numGroups',
'rankPoints',
'revives',
'rideDistance',
'roadKills',
'swimDistance',
'teamKills',
'vehicleDestroys',
'walkDistance',
'weaponsAcquired',
'winPoints'
]
options = [
'crashfpp',
'crashtpp',
'duo',
'duo-fpp',
'flarefpp',
'flaretpp',
'normal-duo',
'normal-duo-fpp',
'normal-solo',
'normal-solo-fpp',
'normal-squad',
'normal-squad-fpp',
'solo',
'solo-fpp',
'squad',
'squad-fpp'
]
with open("model.pkl", "rb") as f:
model = pickle.load(f)
def win_position(matchType, assists, boosts, damageDealt, DBNOs, headshotKills, heals, killPlace, killPoints, kills, killStreaks, longestKill, matchDuration, maxPlace, numGroups, rankPoints, revives, rideDistance, roadKills, swimDistance, teamKills, vehicleDestroys, walkDistance, weaponsAcquired, winPoints):
f_matchType = options.index(matchType)
f_assists = float(assists)
f_boosts = float(boosts)
f_damageDealt = float(damageDealt)
f_DBNOs = float(DBNOs)
f_headshotKills = float(headshotKills)
f_heals = float(heals)
f_killPlace = float(killPlace)
f_killPoints = float(killPoints)
f_kills = float(kills)
f_killStreaks = float(killStreaks)
f_longestKill = float(longestKill)
f_matchDuration = float(matchDuration)
f_maxPlace = float(maxPlace)
f_numGroups = float(numGroups)
f_rankPoints = float(rankPoints)
f_revives = float(revives)
f_rideDistance = float(rideDistance)
f_roadKills = float(roadKills)
f_swimDistance = float(swimDistance)
f_teamKills = float(teamKills)
f_vehicleDestroys = float(vehicleDestroys)
f_walkDistance = float(walkDistance)
f_weaponsAcquired = float(weaponsAcquired)
f_winPoints = float(winPoints)
default = [
f_matchType,
f_assists,
f_boosts,
f_damageDealt,
f_DBNOs,
f_headshotKills,
f_heals,
f_killPlace,
f_killPoints,
f_kills,
f_killStreaks,
f_longestKill,
f_matchDuration,
f_maxPlace,
f_numGroups,
f_rankPoints,
f_revives,
f_rideDistance,
f_roadKills,
f_swimDistance,
f_teamKills,
f_vehicleDestroys,
f_walkDistance,
f_weaponsAcquired,
f_winPoints
]
df=pd.DataFrame([default], columns = defaultColumns)
predictions = model.predict(df)
result = 'você ficou em ' + str((100 - (int(predictions[0]*100)))) + '° lugar. [' + str(predictions[0]) + ']'
return result
iface = gr.Interface(
fn=win_position,
title="Win Predict",
allow_flagging="never",
inputs=[
gr.Dropdown(options, default='squad-fpp', label="matchType"),
gr.inputs.Number(default= 0, label="assists"),
gr.inputs.Number(default= 8, label="boosts"),
gr.inputs.Number(default= 501.5, label="damageDealt"),
gr.inputs.Number(default= 3, label="DBNOs"),
gr.inputs.Number(default= 1, label="headshotKills"),
gr.inputs.Number(default= 9, label="heals"),
gr.inputs.Number(default= 3, label="killPlace"),
gr.inputs.Number(default= 1551, label="killPoints"),
gr.inputs.Number(default= 4, label="kills"),
gr.inputs.Number(default= 2, label="killStreaks"),
gr.inputs.Number(default= 115.9, label="longestKill"),
gr.inputs.Number(default= 1810, label="matchDuration"),
gr.inputs.Number(default= 30, label="maxPlace"),
gr.inputs.Number(default= 30, label="numGroups"),
gr.inputs.Number(default= -1, label="rankPoints"),
gr.inputs.Number(default= 1, label="revives"),
gr.inputs.Number(default= 7958.0, label="rideDistance"),
gr.inputs.Number(default= 0, label="roadKills"),
gr.inputs.Number(default= 0.0, label="swimDistance"),
gr.inputs.Number(default= 0, label="teamKills"),
gr.inputs.Number(default= 0, label="vehicleDestroys"),
gr.inputs.Number(default= 2923.0, label="walkDistance"),
gr.inputs.Number(default= 8, label="weaponsAcquired"),
gr.inputs.Number(default= 1516, label="winPoints")
],
outputs="text")
iface.launch()