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import gradio as gr
import xgboost as xgb
import numpy as np
import joblib
import os
# Load your model (adjust path as needed)
def load_model():
if os.path.exists("best_model.json"):
model = xgb.Booster()
model.load_model("best_model.json")
print("✅ Model loaded using XGBoost's native method.")
return model
elif os.path.exists("best_model.pkl"):
model = joblib.load("best_model.pkl")
print("✅ Model loaded using Joblib.")
return model
else:
print("❌ No model file found.")
return None
model = load_model()
# Prediction function
def predict_employee_status(satisfaction_level, last_evaluation, number_project,
average_monthly_hours, time_spend_company,
work_accident, promotion_last_5years, salary):
input_data = np.array([[satisfaction_level, last_evaluation, number_project,
average_monthly_hours, time_spend_company,
work_accident, promotion_last_5years, salary]])
if model is None:
return "❌ No model found. Please upload the model file."
if isinstance(model, xgb.Booster):
dmatrix = xgb.DMatrix(input_data)
prediction = model.predict(dmatrix)[0]
result = "✅ The employee is likely to Quit." if prediction > 0.5 else "✅ The employee is likely to Stay."
else:
prediction = model.predict(input_data)[0]
result = "✅ The employee is likely to Quit." if prediction == 1 else "✅ The employee is likely to Stay."
return result
# Gradio interface
interface = gr.Interface(
fn=predict_employee_status,
inputs=[
gr.inputs.Number(label="Satisfaction Level"),
gr.inputs.Number(label="Last Evaluation"),
gr.inputs.Number(label="Number of Projects"),
gr.inputs.Number(label="Average Monthly Hours"),
gr.inputs.Number(label="Time Spent at Company (Years)"),
gr.inputs.Number(label="Work Accident (0 = No, 1 = Yes)"),
gr.inputs.Number(label="Promotion in Last 5 Years (0 = No, 1 = Yes)"),
gr.inputs.Dropdown(choices=[0, 1, 2], label="Salary Level (0 = Low, 1 = Medium, 2 = High)")
],
outputs="text",
title="Employee Retention Prediction",
description="Predict whether an employee will stay or quit based on their profile.",
live=False
)
# Launch Gradio app
interface.launch()
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