Delete app.py

app.py

@@ -1,69 +0,0 @@
-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()