Add application file

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import pandas as pd
+import joblib
+
+# Load the trained XGBoost model
+best_model = joblib.load("xgb_model.pkl")
+
+# Define the prediction function
+def predict_fuel_rate(loaded_drv, empty_drv, eng_speed, empty_stop, loading_stop, loaded_stop):
+    input_data = {
+        'loaded_drv_time_percycle': loaded_drv,
+        'empty_drv_time_percycle': empty_drv,
+        'Eng_Speed_Ave': eng_speed,
+        'empty_stop_time_percycle': empty_stop,
+        'loadingstoptime_percycle': loading_stop,
+        'loaded_stop_time_percycle': loaded_stop
+    }
+    input_df = pd.DataFrame([input_data])
+    prediction = best_model.predict(input_df)[0]
+    return round(prediction, 2)
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_fuel_rate,
+    inputs=[
+        gr.Slider(3, 60, value=18, label="Loaded Drive Time per Cycle"),
+        gr.Slider(2, 51, value=16, label="Empty Drive Time per Cycle"),
+        gr.Slider(1051, 1596, value=1416, label="Engine Speed Average"),
+        gr.Slider(0.2, 24.6, value=4.2, label="Empty Stop Time per Cycle"),
+        gr.Slider(2, 18, value=2, label="Loading Stop Time per Cycle"),
+        gr.Slider(0.4, 9, value=0.4, label="Loaded Stop Time per Cycle"),
+    ],
+    outputs=gr.Number(label="Predicted Fuel Rate per Cycle (L)"),
+    title="🚛 Fuel Rate What-If Simulator",
+    description="Adjust the sliders to simulate different operating conditions and estimate fuel consumption."
+)
+
+# Launch the app
+interface.launch()