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import gradio as gr
import numpy as np
import pandas as pd
import joblib
import os
import warnings
import zipfile
warnings.filterwarnings("ignore")
def load_model():
zip_path = "final_ensemble_model.zip"
pkl_path = "final_ensemble_model.pkl"
if not os.path.exists(pkl_path):
print("📦 Extracting model from zip...")
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(".")
try:
model = joblib.load(pkl_path)
print("✅ Ensemble model loaded.")
return model
except Exception as e:
print(f"❌ Failed to load model: {e}")
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, department, threshold=0.5):
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
# Feature engineering
satisfaction_evaluation = satisfaction_level * last_evaluation
work_balance = average_monthly_hours / number_project
# Construct DataFrame
input_data = {
"satisfaction_level": [satisfaction_level],
"last_evaluation": [last_evaluation],
"number_project": [number_project],
"average_monthly_hours": [average_monthly_hours],
"time_spend_company": [time_spend_company],
"Work_accident": [work_accident],
"promotion_last_5years": [promotion_last_5years],
"salary": [salary],
"satisfaction_evaluation": [satisfaction_evaluation],
"work_balance": [work_balance],
**department_features
}
input_df = pd.DataFrame(input_data)
# Prediction
if model is None:
return "❌ No model loaded."
try:
prob = model.predict_proba(input_df)[0][1]
label = "✅ Employee is likely to quit." if prob >= threshold else "✅ Employee is likely to stay."
return f"{label} (Probability: {prob:.2%})"
except Exception as e:
return f"❌ Error during prediction: {str(e)}"
# Launch Gradio 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 Spend 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(departments, label="Department"),
gr.Slider(0.1, 0.9, value=0.5, step=0.05, label="Prediction Threshold")
],
outputs="text",
title="Employee Retention Prediction System (Voting Ensemble)",
description="Predict whether an employee will stay or quit. Adjust threshold for sensitivity.",
theme="dark"
).launch()
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