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app.py

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+import gradio as gr
+import xgboost as xgb
+import numpy as np
+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():
+    if os.path.exists("models/best_model.json"):
+        model = xgb.Booster()
+        model.load_model("models/best_model.json")
+        print("✅ Model loaded using XGBoost's native method.")
+        return model
+    elif os.path.exists("models/best_model.pkl"):
+        model = joblib.load("models/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 with enhanced UI
+interface = gr.Interface(
+    fn=predict_employee_status,
+    inputs=[
+        gr.Number(label="Satisfaction Level (0.0 - 1.0)", value=0.5),
+        gr.Number(label="Last Evaluation (0.0 - 1.0)", value=0.6),
+        gr.Number(label="Number of Projects (1 - 10)", value=3),
+        gr.Number(label="Average Monthly Hours (80 - 320)", value=150),
+        gr.Number(label="Time Spent at Company (Years)", value=3),
+        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.Dropdown(choices=[0, 1, 2], label="Salary Level (0 = Low, 1 = Medium, 2 = High)")
+    ],
+    outputs="text",
+    title="🚀 Employee Retention Prediction System",
+    description="Predict whether an employee is likely to stay or quit based on their profile.",
+    live=False
+)
+
+# Launch Gradio app
+interface.launch()

requirements.txt

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+gradio==3.15.0
+xgboost>=1.6.0
+numpy
+joblib
+matplotlib
+pandas