Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import pickle
+import pandas as pd
+
+# Load trained XGBoost model
+with open("xgboost_trip_delay_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Define the prediction function for Gradio
+def predict_bus_delay(route_id, trip_direction, speed, trip_delay_y, hour, minute, last_stop_arrival_seconds):
+    data = pd.DataFrame({
+        "route_id": [route_id],  
+        "trip_direction": [1 if trip_direction == "UP" else 0],
+        "speed": [float(speed)],
+        "trip_delay_y": [float(trip_delay_y)],
+        "hour": [int(hour)],
+        "minute": [int(minute)],
+        "last_stop_arrival_seconds": [int(last_stop_arrival_seconds)]
+    })
+
+    data["route_id"] = data["route_id"].astype("category")
+
+    # Make prediction
+    prediction = model.predict(data)[0]
+    return f"Predicted Bus Delay Between Stops: {prediction:.2f} seconds"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_bus_delay,
+    inputs=[
+        gr.Textbox(label="Route ID"),
+        gr.Dropdown(label="Direction", choices=["UP", "DN"]),
+        gr.Number(label="Speed"),
+        gr.Number(label="Previous Stop Delay"),
+        gr.Number(label="Hour"),
+        gr.Number(label="Minute"),
+        gr.Number(label="Last Stop Arrival (s)")
+    ],
+    outputs="text"
+)
+
+# Launch the Gradio interface
+interface.launch()