Create PUE

PUE

@@ -0,0 +1,80 @@
+import joblib
+import pandas as pd
+import gradio as gr
+
+# Load model once (HF best practice)
+artifact = joblib.load("pue_artifact.joblib")
+
+def predict_pue(input_dict):
+    X = pd.DataFrame([input_dict])
+    X = X.reindex(columns=artifact["features"], fill_value=0)
+
+    ride = artifact["ride_encoder"].inverse_transform(
+        artifact["ride_model"].predict(X)
+    )[0]
+
+    discount_prob = artifact["discount_model"].predict_proba(X)[0][1]
+
+    route = artifact["route_encoder"].inverse_transform(
+        artifact["route_model"].predict(X)
+    )[0]
+
+    return {
+        "Recommended Ride Type": ride,
+        "Discount Probability": round(float(discount_prob), 2),
+        "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,
+    inputs=[
+        gr.Number(label="Zone ID"),
+        gr.Slider(0,23,step=1,label="Hour"),
+        gr.Slider(0,6,step=1,label="Day of Week"),
+        gr.Radio([0,1],label="Weekend"),
+        gr.Slider(1,1.5,label="Weather Factor"),
+        gr.Slider(1,1.6,label="Event Factor"),
+        gr.Slider(0.5,2,label="Traffic Index"),
+        gr.Number(label="Distance (km)"),
+        gr.Number(label="Duration (min)"),
+        gr.Number(label="Base Fare"),
+        gr.Number(label="Avg Distance"),
+        gr.Number(label="Avg Duration"),
+        gr.Number(label="Avg Fare"),
+        gr.Slider(0,1,label="Discount Usage Rate"),
+        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."
+)
+
+demo.launch()