Update app.py

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 from joblib import load
 
 
-def humands(Sex,Age,Monthlyincome,TotalWorkingYears,DistanceFromHome,Overtime,YearsAtCompany,NumCompaniesWorked):
+def humands(Sex,Age,Married,Monthlyincome,TotalWorkingYears,DistanceFromHome,Overtime,YearsAtCompany,NumCompaniesWorked):
  model = load('modelo_entrenado.pkl')
  df = pd.DataFrame.from_dict(
  {
@@ -18,40 +18,40 @@ def humands(Sex,Age,Monthlyincome,TotalWorkingYears,DistanceFromHome,Overtime,Ye
  "YearsAtCompany" : [YearsAtCompany],
  "MonthlyRate" : [Monthlyincome*2],
  "NumCompaniesWorked" : [NumCompaniesWorked],
- "PercentSalaryHike" : [0],
- "YearsInCurrentRole" : [0],
- "YearsWithCurrManager" : [0],
- "StockOptionLevel" : [0],
- "YearsSinceLastPromotion" : [0],
- "JobSatisfaction" : [0],
- "JobLevel" : [0],
+ "PercentSalaryHike" : [15],
+ "YearsInCurrentRole" : [YearsAtCompany-1],
+ "YearsWithCurrManager" : [YearsAtCompany-1],
+ "StockOptionLevel" : [1],
+ "YearsSinceLastPromotion" : [YearsAtCompany-1],
+ "JobSatisfaction" : [2],
+ "JobLevel" : [3],
  "TrainingTimesLastYear" : [0],
- "EnvironmentSatisfaction" : [0],
- "WorkLifeBalance" : [0],
- "MaritalStatus_Single" : [0],
- "JobInvolvement" : [0],
- "RelationshipSatisfaction" : [0],
- "Education" : [0],
- "BusinessTravel_Travel_Frequently" : [0],
+ "EnvironmentSatisfaction" : [2],
+ "WorkLifeBalance" : [2],
+ "MaritalStatus_Single" : [1 if Married==0 else 0],
+ "JobInvolvement" : [2],
+ "RelationshipSatisfaction" : [Married+1],
+ "Education" : [2],
+ "BusinessTravel_Travel_Frequently" : [1 if Overtime else 0],
  "JobRole_Sales Representative" : [0],
  "EducationField_Medical" : [0],
  "Department_Sales" : [0],
  "JobRole_Laboratory Technician" : [0],
- "Department_Research & Development" : [0],
+ "Department_Research & Development" : [1],
  "Gender_Female" : [1 if Sex==0 else 0],
- "MaritalStatus_Married" : [0],
+ "MaritalStatus_Married" : [1 if Married==1 else 0],
  "JobRole_Sales Executive" : [0],
- "EducationField_Technical Degree" : [0],
+ "EducationField_Technical Degree" : [1],
  "Gender_Male" : [1 if Sex==1 else 0],
  "EducationField_Life Sciences" : [0],
  "BusinessTravel_Travel_Rarely" : [0],
- "MaritalStatus_Divorced" : [0],
- "JobRole_Research Scientist" : [0],
+ "MaritalStatus_Divorced" : [1 if Married==2 else 0],
+ "JobRole_Research Scientist" : [1],
  "EducationField_Marketing" : [0],
- "PerformanceRating" : [0],
+ "PerformanceRating" : [3],
  "EducationField_Other" : [0],
  "JobRole_Human Resources" : [0],
- "BusinessTravel_Non-Travel" : [0],
+ "BusinessTravel_Non-Travel" : [1 if not Overtime else 0],
  "Department_Human Resources" : [0],
  "JobRole_Manufacturing Director" : [0],
  "JobRole_Healthcare Representative" : [0],
@@ -80,6 +80,7 @@ iface = gr.Interface(
  [
  gr.Radio(["Mujer","Hombre"],type = "index",label="Sexo"),
  gr.inputs.Slider(18,70,1,label="Edad del trabajador"),
+ gr.Radio(["Soltero","Casado","Divorciado"],type = "index",label="Esstado civil:"),
  gr.inputs.Slider(1000,20000,1,label="Ingresos mensuales del trabajador"),
  gr.inputs.Slider(0,40,1,label="Total de años trabajados del trabajador"),
  gr.inputs.Slider(0,100,1,label="Distancia del trabajo al domicilio en Km"),
@@ -91,9 +92,9 @@ iface = gr.Interface(
 
  "text",
  examples=[
- ["Mujer",25,1500,20,2,True,2,2],
- ["Hombre",25,1500,20,2,False,2,2],
- ["Hombre",25,1500,20,2,True,2,2],
+ ["Mujer",25,"Soltero",1500,20,2,True,2,2],
+ ["Hombre",25,"Casado",1500,20,2,False,2,2],
+ ["Hombre",25,"Divordiado",1500,20,2,True,2,2],
  ],
  interpretation="default",
  title = 'Student Experience: cómo mejorar la experiencia de aprendizaje en la universidad',