Update PUE

PUE

@@ -1,12 +1,37 @@
-import joblib
 import pandas as pd
+import joblib
 import gradio as gr
 
-# Load model once (HF best practice)
+# Load model artifact once
 artifact = joblib.load("pue_artifact.joblib")
 
-def predict_pue(input_dict):
-    X = pd.DataFrame([input_dict])
+def predict_pue(
+    zone_id, hour, day_of_week, is_weekend,
+    weather_factor, event_factor, traffic_index,
+    distance_km, duration_min, base_fare,
+    avg_distance, avg_duration, avg_fare,
+    discount_usage_rate, total_rides
+):
+
+    input_data = {
+        "zone_id": int(zone_id),
+        "hour": int(hour),
+        "day_of_week": int(day_of_week),
+        "is_weekend": int(is_weekend),
+        "weather_factor": float(weather_factor),
+        "event_factor": float(event_factor),
+        "traffic_index": float(traffic_index),
+        "distance_km": float(distance_km),
+        "duration_min": float(duration_min),
+        "base_fare": float(base_fare),
+        "avg_distance": float(avg_distance),
+        "avg_duration": float(avg_duration),
+        "avg_fare": float(avg_fare),
+        "discount_usage_rate": float(discount_usage_rate),
+        "total_rides": int(total_rides)
+    }
+
+    X = pd.DataFrame([input_data])
     X = X.reindex(columns=artifact["features"], fill_value=0)
 
     ride = artifact["ride_encoder"].inverse_transform(
@@ -25,36 +50,9 @@ def predict_pue(input_dict):
         "Preferred Route": route
     }
 
-def ui_predict(
-    zone_id, hour, day_of_week, is_weekend,
-    weather_factor, event_factor, traffic_index,
-    distance_km, duration_min, base_fare,
-    avg_distance, avg_duration, avg_fare,
-    discount_usage_rate, total_rides
-):
-
-    input_data = {
-        "zone_id": int(zone_id),
-        "hour": int(hour),
-        "day_of_week": int(day_of_week),
-        "is_weekend": int(is_weekend),
-        "weather_factor": weather_factor,
-        "event_factor": event_factor,
-        "traffic_index": traffic_index,
-        "distance_km": distance_km,
-        "duration_min": duration_min,
-        "base_fare": base_fare,
-        "avg_distance": avg_distance,
-        "avg_duration": avg_duration,
-        "avg_fare": avg_fare,
-        "discount_usage_rate": discount_usage_rate,
-        "total_rides": int(total_rides)
-    }
-
-    return predict_pue(input_data)
-
-demo = gr.Interface(
-    fn=ui_predict,
+# Gradio UI
+app = gr.Interface(
+    fn=predict_pue,
     inputs=[
         gr.Number(label="Zone ID"),
         gr.Slider(0,23,step=1,label="Hour"),
@@ -73,8 +71,8 @@ demo = gr.Interface(
         gr.Number(label="Total Rides")
     ],
     outputs="json",
-    title="Personalized User Experience – Ride-Hailing ML",
-    description="Real-time personalization: ride type, discount probability, and route preference."
+    title="Personalized User Experience – Real-Time ML",
+    description="Real-time personalization for ride-hailing apps"
 )
 
-demo.launch()
+app.launch()