app.py

import pickle
import gradio as gr

# Load the pickled model
with open('./salifort_rf3.pickle', 'rb') as file:
    model = pickle.load(file)

# Define the function for making predictions
def salifort(last_evaluation, number_project, tenure, work_accident, promotion_last_5years, salary, department_IT, department_RandD, department_accounting, department_hr, department_management, department_marketing, department_product_mng, department_sales, department_support, department_technical, overworked):
    inputs = [[float(last_evaluation), float(number_project), float(tenure), float(work_accident), float(promotion_last_5years), float(salary), float(department_IT), float(department_RandD), float(department_accounting), float(department_hr), float(department_management), float(department_marketing), float(department_product_mng), float(department_sales), float(department_support), float(department_technical), float(overworked)]]
    prediction = model.predict(inputs)
    prediction_value = prediction[0]
    if prediction_value == 0:
        label_text = 'Employee would not leave the company 🟢'
    else:
        label_text = 'Employee will leave the company 🔴'
    return label_text

# Create the Gradio interface
salifort_ga = gr.Interface(fn=salifort, 
                               inputs = [
                                            gr.Number(0, 1, label="last_evaluation: [0 1]"),
                                            gr.Number(2, 7, label="number_project: [2 to 7]"),
                                            gr.Number(2, 10, label="tenure: [2 to 10]"),
                                            gr.Number(0, 1, label="work_accident: [0 1]"),
                                            gr.Number(0, 1, label="promotion_last_5years: [0 1]"),
                                            gr.Number(0, 2, label="salary: [0 1 2]"),
                                            gr.Number(0, 1, label="department_IT: [0 1]"),
                                            gr.Number(0, 1, label="department_RandD: [0 1]"),
                                            gr.Number(0, 1, label="department_accounting: [0 1]"),
                                            gr.Number(0, 1, label="department_hr: [0 1]"),
                                            gr.Number(0, 1, label="department_management: [0 1]"),
                                            gr.Number(0, 1, label="department_marketing: [0 1]"),
                                            gr.Number(0, 1, label="department_product_mng: [0 1]"),
                                            gr.Number(0, 1, label="department_sales: [0 1]"),
                                            gr.Number(0, 1, label="department_support: [0 1]"),
                                            gr.Number(0, 1, label="department_technical: [0 1]"),
                                            gr.Number(0, 1, label="overworked: [0 1]")
                                        ],
                               outputs  = "text", title="Data-driven suggestions for HR - Salifort Motors - Employee Retention", 
                               examples = [
                                        [0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
                                        [0, 3, 3, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
                                        [0, 2, 3, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
                                        [0, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1]
                                         ],
                               description="Employee Retention Prediction Using Machine Learning",
                               theme='dark'
                               )

salifort_ga.launch(share=True)