import gradio as gr
import pandas as pd
from joblib import load


def humands(Sex,Age,Married,Monthlyincome,TotalWorkingYears,DistanceFromHome,Overtime,YearsAtCompany,NumCompaniesWorked):
    model = load('modelo_entrenado.pkl')
    df = pd.DataFrame.from_dict(
        {
            "MonthlyIncome" : [Monthlyincome],
            "Age" : [Age],
            "TotalWorkingYears" : [TotalWorkingYears],
            "DailyRate" : [Monthlyincome*2/30],
            "HourlyRate" : [Monthlyincome*2/1640],
            "DistanceFromHome" : [DistanceFromHome],
            "OverTime_Yes" : [1 if Overtime else 0],
            "OverTime_No" : [1 if not Overtime else 0],
            "YearsAtCompany" : [YearsAtCompany],
            "MonthlyRate" : [Monthlyincome*2],
            "NumCompaniesWorked" : [NumCompaniesWorked],
            "PercentSalaryHike" : [15],
            "YearsInCurrentRole" : [YearsAtCompany-1],
            "YearsWithCurrManager" : [YearsAtCompany-1],
            "StockOptionLevel" : [1],
            "YearsSinceLastPromotion" : [YearsAtCompany-1],
            "JobSatisfaction" : [2],
            "JobLevel" : [3],
            "TrainingTimesLastYear" : [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" : [1],
            "Gender_Female" : [1 if Sex==0 else 0],
            "MaritalStatus_Married" : [1 if Married==1 else 0],
            "JobRole_Sales Executive" : [0],
            "EducationField_Technical Degree" : [1],
            "Gender_Male" : [1 if Sex==1 else 0],
            "EducationField_Life Sciences" : [0],
            "BusinessTravel_Travel_Rarely" : [0],
            "MaritalStatus_Divorced" : [1 if Married==2 else 0],
            "JobRole_Research Scientist" : [1],
            "EducationField_Marketing" : [0],
            "PerformanceRating" : [3],
            "EducationField_Other" : [0],
            "JobRole_Human Resources" : [0],
            "BusinessTravel_Non-Travel" : [1 if not Overtime else 0],
            "Department_Human Resources" : [0],
            "JobRole_Manufacturing Director" : [0],
            "JobRole_Healthcare Representative" : [0],
            "EducationField_Human Resources" : [0],
            "JobRole_Manager" : [0],
            "JobRole_Research Director" : [0],
            
            
                                 
        }
    )
         
    pred = model.predict(df)[0]

    if pred == "Yes":
        predicted1="Estamos ante un trabajador con alto nivel de desgaste del trabajo. Habría que plantearse alguna acción."
        predicted2="stressed_image.jpg"
    else:
        predicted1="Estamos ante un trabajador con un nivel bajo de desgaste del trabajo. Se ha de seguir así."
        predicted2="ok_image2.jpg"
    return [predicted1,predicted2]
   
    
iface = gr.Interface(
    humands,
    [
        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"),
        gr.Checkbox(label="¿Realiza horas extras habitualmente?"),
        gr.inputs.Slider(0,40,1,label="Años del trabajador en la empresa"),
        gr.inputs.Slider(0,40,1,label="Numero de empresas en las que ha estado el trabajador"),
        
     ],

    ["text",gr.Image(type='file')],
    examples=[
        ["Mujer",33,"Soltero",2917,9,1,False,9,1],
        ["Hombre",42,"Casado",3111,16,5,False,7,3],
        ["Hombre",50,"Divorciado",1732,20,50,True,3,3],
        ["Mujer",25,"Soltero",2556,6,58,True,2,4],
    ],
    interpretation="default",
    title = 'HUMANDS: Inteligencia artificial para empleados',
    description = 'Uno de los motivos por los que las organizaciones pierden a sus empleados es la insatisfacción laboral, por ello, nuestro objetivo es predecir el verdadero nivel de desgaste de los empleados dentro de una organización mediante Inteligencia Artificial. Para saber más: https://saturdays.ai/2021/12/31/inteligencia-artificial-empleados/',
    theme = 'peach'
)


   
iface.launch()