app.py

import gradio as gr
import pandas as pd
import pickle


# Define params names
PARAMS_NAME = [
        "Age",
        "BusinessTravel",
        "DailyRate",
        "Department",
        "DistanceFromHome",
        "Education",
        "EducationField",
        "EnvironmentSatisfaction",
        "Gender",
        "HourlyRate",
        "JobInvolvement",
        "JobLevel",
        "JobRole",
        "JobSatisfaction",
        "MaritalStatus",
        "MonthlyIncome",
        "MonthlyRate",
        "NumCompaniesWorked",
        "OverTime",
        "PercentSalaryHike",
        "PerformanceRating",
        "RelationshipSatisfaction",
        "StockOptionLevel",
        "TotalWorkingYears",
        "TrainingTimesLastYear",
        "WorkLifeBalance",
        "YearsAtCompany",
        "YearsInCurrentRole",
        "YearsSinceLastPromotion",
        "YearsWithCurrManager"
]

           
# Load model
with open("model/model1.pkl", "rb") as f:
    model = pickle.load(f)




def predict(*args):
    answer_dict = {}

    for i in range(len(PARAMS_NAME)):
        answer_dict[PARAMS_NAME[i]] = [args[i]]

    # Crear dataframe
    single_instance = pd.DataFrame.from_dict(answer_dict)

    single_instance_numbers = single_instance
    
    for columna in single_instance_numbers:
            # Verificar si el tipo de dato es "object"
            if single_instance_numbers[columna].dtype == 'object':
                # Obtener los valores únicos de la columna
                valores_unicos = single_instance_numbers[columna].unique()
                
                # Crear un diccionario de reemplazo
                diccionario_reemplazo = {valor: indice for indice, valor in enumerate(valores_unicos)}
                
                # Reemplazar los valores en la columna
                single_instance_numbers[columna] = single_instance_numbers[columna].map(diccionario_reemplazo)


    
    prediction = model.predict(single_instance_numbers)

    
    
    Attrition = int(prediction[0])


    # Adaptación respuesta
    response = Attrition
    if Attrition == 1:
        response = "Good idea, \n but not now, I am not atrittioned yet"
    if Attrition == 0:
        response = "🤯  \n OMG! PLEEEEEASE"


    return response


with gr.Blocks() as demo:
    gr.Markdown(
        """
        #   Attrition Prevention 🤯
        """
    )

    with gr.Row():
        with gr.Column():

            gr.Markdown(
                """
                ## Insert your job data here please 🤓
                """
            )
            
            Age = gr.Slider(
                label='Age',
                minimum=18,
                maximum=60,
                step=1,
                value=41
            )

            BusinessTravel = gr.Radio(
                label='Business Travel',
                choices=['Travel Rarely', 'Travel Frequently', 'Non-Travel'],
                value='Travel Rarely',
            )

            DailyRate = gr.Slider(
                label='Daily Rate',
                minimum=102,
                maximum=1499,
                step=1,
                value=1102
            )

            Department = gr.Radio(
                label='Department',
                choices=['Sales', 'Research & Development', 'Human Resources'],
                value='Sales',
            )

            DistanceFromHome = gr.Slider(
                label='Distance From Home',
                minimum=1,
                maximum=29,
                step=1,
                value=1
            )

            Education = gr.Dropdown(
                label='Education',
                choices=['College', 'Below College', 'Master', 'Bachelor', 'Doctor'],
                multiselect=False,
                value='Bachelor',
            )

            EducationField = gr.Dropdown(
                label='Education Field',
                choices=['Life Sciences', 'Other', 'Medical', 'Marketing', 'Technical Degree', 'Human Resources'],
                multiselect=False,
                value='Life Sciences',
            )

            EnvironmentSatisfaction = gr.Dropdown(
                label='Environment Satisfaction',
                choices=['Medium', 'High', 'Very High', 'Low'],
                multiselect=False,
                value='Medium',
            )

            Gender = gr.Radio(
                label='Gender',
                choices=['Female', 'Male'],
                value='Female',
            )

            HourlyRate = gr.Slider(
                label='Hourly Rate',
                minimum=30,
                maximum=100,
                step=1,
                value=94
            )

            JobInvolvement = gr.Dropdown(
                label='Job Involvement',
                choices=['High', 'Medium', 'Very High', 'Low'],
                multiselect=False,
                value='High',
            )

            JobLevel = gr.Radio(
                label='Job Level',
                choices=[2, 1, 3, 4, 5],
                value=2,
            )

            JobRole = gr.Dropdown(
                label='Job Role',
                choices=['Sales Executive', 'Research Scientist', 'Laboratory Technician', 'Manufacturing Director', 'Healthcare Representative', 'Manager', 'Sales Representative', 'Research Director', 'Human Resources'],
                multiselect=False,
                value='Sales Executive',
            )

            JobSatisfaction = gr.Dropdown(
                label='Job Satisfaction',
                choices=['Very High', 'Medium', 'High', 'Low'],
                multiselect=False,
                value='High',
            )

            MaritalStatus = gr.Radio(
                label='Marital Status',
                choices=['Single', 'Married', 'Divorced'],
                value='Single',
            )

            MonthlyIncome = gr.Slider(
                label='Monthly Income',
                minimum=1009,
                maximum=19999,
                step=1,
                value=5993
            )

            MonthlyRate = gr.Slider(
                label='Monthly Rate',
                minimum=2094,
                maximum=26999,
                step=1,
                value=19479
            )

            NumCompaniesWorked = gr.Slider(
                label='Num Companies Worked',
                minimum=0,
                maximum=9,
                step=1,
                value=8
            )

            OverTime = gr.Radio(
                label='Overtime',
                choices=['Yes', 'No'],
                value='Yes',
            )

            PercentSalaryHike = gr.Slider(
                label='Percent Salary Hike',
                minimum=11,
                maximum=25,
                step=1,
                value=11
            )

            PerformanceRating = gr.Radio(
                label='Performance Rating',
                choices=['Excellent', 'Outstanding'],
                value='Excellent',
            )

            RelationshipSatisfaction = gr.Dropdown(
                label='Relationship Satisfaction',
                choices=['Low', 'Very High', 'Medium', 'High'],
                multiselect=False,
                value='Low',
            )

            StockOptionLevel = gr.Radio(
                label='Stockoption Level',
                choices=[0, 1, 3, 2],
                value=0,
            )

            TotalWorkingYears = gr.Slider(
                label='Total Working Years',
                minimum=0,
                maximum=40,
                step=1,
                value=8
            )

            TrainingTimesLastYear = gr.Slider(
                label='Training Times Last Year',
                minimum=0,
                maximum=6,
                step=1,
                value=0
            )

            WorkLifeBalance = gr.Dropdown(
                label='Work Life balance',
                choices=['Bad', 'Better', 'Good', 'Best'],
                multiselect=False,
                value='Bad',
            )

            YearsAtCompany = gr.Slider(
                label='Years At Company',
                minimum=0,
                maximum=40,
                step=1,
                value=6
            )

            YearsInCurrentRole = gr.Slider(
                label='Years In Currentrole',
                minimum=0,
                maximum=18,
                step=1,
                value=41
            )

            YearsSinceLastPromotion = gr.Slider(
                label='Years Since Last Promotion',
                minimum=0,
                maximum=15,
                step=1,
                value=0
            )

            YearsWithCurrManager = gr.Slider(
                label='Years With Curr Manager',
                minimum=0,
                maximum=17,
                step=1,
                value=5
            )            




        with gr.Column():

            gr.Markdown(
                """
                ## Look if you need some Holy Days 🏝️
                """
            )

            label = gr.Label(label="Attrition Level")
            predict_btn = gr.Button(value="Click Here Please")
            predict_btn.click(
                predict,
                inputs=[
                    Age,
                    BusinessTravel,
                    DailyRate,
                    Department,
                    DistanceFromHome,
                    Education,
                    EducationField,
                    EnvironmentSatisfaction,
                    Gender,
                    HourlyRate,
                    JobInvolvement,
                    JobLevel,
                    JobRole,
                    JobSatisfaction,
                    MaritalStatus,
                    MonthlyIncome,
                    MonthlyRate,
                    NumCompaniesWorked,
                    OverTime,
                    PercentSalaryHike,
                    PerformanceRating,
                    RelationshipSatisfaction,
                    StockOptionLevel,
                    TotalWorkingYears,
                    TrainingTimesLastYear,
                    WorkLifeBalance,
                    YearsAtCompany,
                    YearsInCurrentRole,
                    YearsSinceLastPromotion,
                    YearsWithCurrManager,
                ],
                outputs=[label],
                api_name="prediccion"
            )
    gr.Markdown(
        """
        <p style='text-align: center'>
            <a href='https://www.escueladedatosvivos.ai/cursos/bootcamp-de-data-science' 
                target='_blank'>Proyecto demo creado en el bootcamp de EDVAI 🤗
            </a>
        </p>
        <p style='text-align: center'>
            <a href='https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset' 
                target='_blank'>Data From IBM HR Analytics Employee Attrition & Performance
            </a>
        </p>
        """
    )

demo.launch()