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import gradio as gr
import xgboost as xgb
import numpy as np
import pandas as pd
import joblib
import os
import warnings
# Suppress XGBoost warnings
warnings.filterwarnings("ignore", category=UserWarning, message=".*WARNING.*")
# Load your model (automatically detect XGBoost or joblib model)
def load_model():
model_path = "best_model.json" # Ensure this matches your file name
if os.path.exists(model_path):
model = xgb.Booster()
model.load_model(model_path)
print("✅ Model loaded successfully.")
return model
else:
print("❌ Model file not found.")
return None
model = load_model()
# Prediction function
def predict_employee_status(satisfaction_level, last_evaluation, number_project,
average_monthly_hours, time_spent_company,
work_accident, promotion_last_5years, salary, department):
# One-hot encode the department
departments = [
'RandD', 'accounting', 'hr', 'management', 'marketing',
'product_mng', 'sales', 'support', 'technical'
]
department_features = {f"department_{dept}": 0 for dept in departments}
if department in departments:
department_features[f"department_{department}"] = 1
# Prepare the input with all 18 features as a DataFrame with column names
input_data = {
"satisfaction_level": [satisfaction_level],
"last_evaluation": [last_evaluation],
"number_project": [number_project],
"average_montly_hours": [average_monthly_hours],
"time_spend_company": [time_spent_company],
"Work_accident": [work_accident],
"promotion_last_5years": [promotion_last_5years],
"salary": [salary],
**department_features,
"satisfaction_evaluation": [satisfaction_level * last_evaluation]
}
input_df = pd.DataFrame(input_data)
# Predict using the model
if model is None:
return "❌ No model found. Please upload the model file."
try:
dmatrix = xgb.DMatrix(input_df)
prediction = model.predict(dmatrix)
return "✅ Employee is likely to quit." if prediction[0] > 0.5 else "✅ Employee is likely to stay."
except Exception as e:
return f"❌ Error: {str(e)}"
# Gradio interface
interface = gr.Interface(
fn=predict_employee_status,
inputs=[
gr.Number(label="Satisfaction Level (0.0 - 1.0)"),
gr.Number(label="Last Evaluation (0.0 - 1.0)"),
gr.Number(label="Number of Projects (1 - 10)"),
gr.Number(label="Average Monthly Hours (80 - 320)"),
gr.Number(label="Time Spent at Company (Years)"),
gr.Radio([0, 1], label="Work Accident (0 = No, 1 = Yes)"),
gr.Radio([0, 1], label="Promotion in Last 5 Years (0 = No, 1 = Yes)"),
gr.Radio([0, 1, 2], label="Salary (0 = Low, 1 = Medium, 2 = High)"),
gr.Dropdown(
['RandD', 'accounting', 'hr', 'management', 'marketing',
'product_mng', 'sales', 'support', 'technical'],
label="Department"
)
],
outputs="text",
title="Employee Retention Prediction System",
description="Predict whether an employee is likely to stay or quit based on their profile."
)
interface.launch()
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