Spaces:
Runtime error
Runtime error
| 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() |