Update app.py

app.py

@@ -1,61 +1,90 @@
 import gradio as gr
 import joblib
+import pandas as pd
 import numpy as np
 
-# Load model DIRECTLY
-model = joblib.load("dynamic_pricing_artifact.joblib")
+ARTIFACT_PATH = "dynamic_pricing_artifact.joblib"
 
-FIXED_FARE = 30
-RATE_PER_KM = 6
+artifact = joblib.load(ARTIFACT_PATH)
 
-def predict_price(
-    demand, supply, weather_factor, event_factor,
-    traffic_index, is_weekend, is_holiday, is_festival,
-    hour, distance_km
-):
-    demand_supply_ratio = demand / (supply + 1)
-
-    X = np.array([[
-        demand,
-        supply,
-        demand_supply_ratio,
-        weather_factor,
-        event_factor,
-        traffic_index,
-        is_weekend,
-        is_holiday,
-        is_festival,
-        hour
-    ]])
-
-    surge = float(model.predict(X)[0])
-    surge = min(max(surge, 1.0), 2.2)  # realistic cap
+model = artifact["model"]
+FEATURES = artifact["features"]
+FIXED_FARE = artifact["fixed_fare"]
+RATE_PER_KM = artifact["rate_per_km"]
 
+def predict_dynamic_price(
+    zone_id, demand, supply, driver_availability,
+    weather_factor, event_factor, temperature, traffic_index,
+    distance_km, duration_min,
+    hour, day_of_week, is_weekend, month,
+    is_holiday, is_festival
+):
+    # 1️⃣ Base price (distance based)
     base_price = FIXED_FARE + distance_km * RATE_PER_KM
+
+    # 2️⃣ Build feature row
+    row = {f: 0.0 for f in FEATURES}
+
+    row.update({
+        "zone_id": zone_id,
+        "hour": hour,
+        "day_of_week": day_of_week,
+        "is_weekend": is_weekend,
+        "month": month,
+        "is_holiday": is_holiday,
+        "is_festival": is_festival,
+        "demand": demand,
+        "supply": supply,
+        "driver_availability": driver_availability,
+        "weather_factor": weather_factor,
+        "event_factor": event_factor,
+        "temperature": temperature,
+        "traffic_index": traffic_index,
+        "distance_km": distance_km,
+        "duration_min": duration_min,
+        "base_fare": base_price
+    })
+
+    # 3️⃣ Demand–Supply ratio
+    row["demand_supply_ratio"] = np.clip(demand / (supply + 1), 0, 20)
+
+    df_row = pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
+
+    # 4️⃣ Predict surge
+    surge = float(model.predict(df_row)[0])
+    surge = np.clip(surge, 1.0, 2.5)   # Rapido/Ola realistic cap
+
     final_price = base_price * surge
 
-    return round(base_price,2), round(surge,2), round(final_price,2)
+    return round(base_price, 2), round(surge, 3), round(final_price, 2)
 
 demo = gr.Interface(
-    fn=predict_price,
+    fn=predict_dynamic_price,
     inputs=[
-        gr.Number(value=150, label="Demand"),
-        gr.Number(value=80, label="Supply"),
-        gr.Number(value=1.0, label="Weather Factor"),
-        gr.Number(value=1.0, label="Event Factor"),
-        gr.Number(value=0.5, label="Traffic Index"),
-        gr.Number(value=0, label="Weekend (0/1)"),
-        gr.Number(value=0, label="Holiday (0/1)"),
-        gr.Number(value=0, label="Festival (0/1)"),
-        gr.Number(value=18, label="Hour"),
-        gr.Number(value=10, label="Distance (km)")
+        gr.Number(label="Zone ID", value=1),
+        gr.Number(label="Demand", value=150),
+        gr.Number(label="Supply", value=80),
+        gr.Number(label="Driver Availability", value=60),
+        gr.Number(label="Weather Factor", value=1.0),
+        gr.Number(label="Event Factor", value=1.0),
+        gr.Number(label="Temperature (°C)", value=30),
+        gr.Number(label="Traffic Index", value=0.5),
+        gr.Number(label="Distance (km)", value=10),
+        gr.Number(label="Duration (min)", value=20),
+        gr.Number(label="Hour", value=18),
+        gr.Number(label="Day of Week", value=4),
+        gr.Number(label="Is Weekend (0/1)", value=0),
+        gr.Number(label="Month", value=11),
+        gr.Number(label="Is Holiday (0/1)", value=0),
+        gr.Number(label="Is Festival (0/1)", value=0),
     ],
     outputs=[
         gr.Number(label="Base Price"),
         gr.Number(label="Surge Factor"),
-        gr.Number(label="Final Price")
+        gr.Number(label="Final Price"),
     ],
-    title="Dynamic Pricing Model"
+    title="Dynamic Pricing Model",
+    description="Realistic dynamic pricing similar to Rapido/Ola"
 )
 
 demo.launch()