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import gradio as gr
import joblib as jb
import pandas as pd
import numpy as np
from functions import custom_encode, cria_feature
#Importa os arquivos necessários
top_50_mais_quebrados = pd.read_csv('top_50_mais_quebrados.csv')
products_id = pd.read_csv('Unique Product ID.csv')
#Importa os modelos de previsão e processamento de dados
model_1 = jb.load('modelo 1.pkl')
model_2 = jb.load('modelo 2.pkl')
model_3 = jb.load('modelo 3.pkl')
model_4 = jb.load('modelo 4.pkl')
stack = jb.load('Stack.pkl')
pipeline = jb.load('pipeline.pkl')
def predict(product_id,air_temperature,process_temperature,rotational_speed,torque,tool_wear,TWF,HDF,PWF,OSF,RNF):
type = products_id.loc[products_id['Product ID'] == product_id]['Type'].values[0]
base = {'Product ID':[product_id],
'Type':[type],
'Air temperature [K]':[int(air_temperature)],
'Process temperature [K]':[int(process_temperature)],
'Rotational speed [rpm]':[int(rotational_speed)],
'Torque [Nm]':[int(torque)],
'Tool wear [min]':[int(tool_wear)],
'TWF':[int(TWF)],
'HDF':[int(HDF)],
'PWF':[int(PWF)],
'OSF':[int(OSF)],
'RNF':[int(RNF)]}
input = pd.DataFrame(data=base)
input['Product ID'] = custom_encode(top_50_mais_quebrados,input['Product ID'])
input = cria_feature(input)
input = pipeline.transform(input)
preds = np.zeros((1,4))
preds[:, 0] = model_1.predict_proba(input)[:,1]
preds[:, 1] = model_2.predict_proba(input)[:,1]
preds[:, 2] = model_3.predict_proba(input)[:,1]
preds[:, 3] = model_4.predict_proba(input)[:,1]
pred_return = preds.mean(axis=1)
return {"Will Not Fail": float(1-pred_return[0]), "Will Fail": float(pred_return[0])}
# 'Product ID' 'Type' 'Air temperature [K]' 'Process temperature [K]' 'Rotational speed [rpm]' 'Torque [Nm]' 'Tool wear [min]' 'TWF' 'HDF' 'PWF' 'OSF' 'RNF'
demo = gr.Interface(fn = predict,
inputs=[gr.Dropdown(choices=products_id['Product ID'].to_list(),type='value',label='Product ID'),
gr.Number(label='Air temperature [K]'),
gr.Number(label='Process temperature [K]'),
gr.Number(label='Rotational speed [RPM]'),
gr.Number(label='Torque [Nm]'),
gr.Number(label='Tool wear [min]'),
gr.Dropdown(choices=['1','0'],type='value',label='TWF'),
gr.Dropdown(choices=['1','0'],type='value',label='HDF'),
gr.Dropdown(choices=['1','0'],type='value',label='PWF'),
gr.Dropdown(choices=['1','0'],type='value',label='OSF'),
gr.Dropdown(choices=['1','0'],type='value',label='RNF')
],
outputs='label',
examples=[
['L52498',330,330,1345,70,196,'0','0','0','0','0'],
['H34252',330,330,1345,150,240,'0','0','0','0','0'],
['M20199',330,330,1345,20,55,'0','1','0','0','0'],
]
)
demo.launch() |