Update app.py

app.py

@@ -3,31 +3,27 @@ import joblib
 import pandas as pd
 import numpy as np
 import requests
-import time
 
-# ================================
-# WEATHER CONFIG
-# ================================
+# ===================== LOAD MODEL =====================
+artifact = joblib.load("dynamic_pricing_artifact_v1.joblib")
+
+model = artifact["model"]
+FEATURES = artifact["features"]
+FIXED_FARE = artifact["fixed_fare"]
+RATE_PER_KM = artifact["rate_per_km"]
+
+# ===================== WEATHER (SAFE) =====================
 API_KEY = "YOUR_API_KEY"
 CITY = "Visakhapatnam,IN"
 
-_last_weather = None
-_last_time = 0
-
 def get_weather_features():
-    global _last_weather, _last_time
-
-    # Cache for 5 minutes
-    if _last_weather and (time.time() - _last_time < 300):
-        return _last_weather
-
     try:
         url = "https://api.openweathermap.org/data/2.5/weather"
         params = {"q": CITY, "appid": API_KEY, "units": "metric"}
         data = requests.get(url, params=params, timeout=5).json()
 
-        condition = data["weather"][0]["main"]
-        temp = data["main"]["temp"]
+        condition = data.get("weather", [{}])[0].get("main", "Clear")
+        temp = data.get("main", {}).get("temp", 30)
 
         mapping = {
             "Clear": 1.0,
@@ -36,30 +32,13 @@ def get_weather_features():
             "Thunderstorm": 1.30
         }
 
-        weather_factor = mapping.get(condition, 1.0)
-
-        _last_weather = (weather_factor, temp)
-        _last_time = time.time()
-        return _last_weather
+        return mapping.get(condition, 1.0), temp
 
     except:
-        return (1.0, 30)
+        return 1.0, 30
 
 
-# ================================
-# LOAD MODEL
-# ================================
-artifact = joblib.load("dynamic_pricing_artifact_v1.joblib")
-
-model = artifact["model"]
-FEATURES = artifact["features"]
-FIXED_FARE = artifact["fixed_fare"]
-RATE_PER_KM = artifact["rate_per_km"]
-
-
-# ================================
-# PREDICTION FUNCTION
-# ================================
+# ===================== PREDICTION =====================
 def predict_dynamic_price(
     zone_id, demand, supply, driver_availability,
     event_factor, traffic_index,
@@ -68,15 +47,14 @@ def predict_dynamic_price(
     is_holiday, is_festival
 ):
 
-    # -------- WEATHER --------
     weather_factor, temperature = get_weather_features()
 
-    # -------- BASE FARE --------
     base_price = FIXED_FARE + (distance_km * RATE_PER_KM)
 
-    # -------- BUILD FEATURE VECTOR --------
+    # Initialize all features = 0
     row = {f: 0.0 for f in FEATURES}
 
+    # Fill known inputs
     inputs = {
         "zone_id": zone_id,
         "hour": hour,
@@ -93,27 +71,54 @@ def predict_dynamic_price(
         "traffic_index": traffic_index,
         "distance_km": distance_km,
         "duration_min": duration_min,
-        "base_fare": base_price
+        "base_fare": base_price,
+        "demand_supply_ratio": (demand + 1) / (supply + 1)
     }
 
     for k, v in inputs.items():
         if k in row:
             row[k] = float(v)
 
-    # -------- CRITICAL: DEMAND/SUPPLY RATIO --------
-    row["demand_supply_ratio"] = np.clip((demand + 1) / (supply + 1), 0, 50)
-
+    # Create dataframe with EXACT feature order
     df_row = pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
 
-    # -------- DEBUG (Remove later) --------
-    print("Demand:", demand, "Supply:", supply, "Drivers:", driver_availability)
-    print("Ratio:", row["demand_supply_ratio"])
-
-    # -------- MODEL PREDICTION --------
+    # Predict
     surge = float(model.predict(df_row)[0])
-    print("Raw Surge:", surge)
-
-    # Avoid constant clipping hiding variation
     surge = np.clip(surge, 1.0, 3.5)
 
     final_price = base_price * surge
+
+    return round(base_price, 2), round(surge, 3), round(final_price, 2)
+
+
+# ===================== UI =====================
+inputs = [
+    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="Event Factor", value=1.0),
+    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", value=0),
+    gr.Number(label="Is Holiday", value=0),
+    gr.Number(label="Is Festival", value=0),
+]
+
+outputs = [
+    gr.Number(label="Base Price"),
+    gr.Number(label="Surge Factor"),
+    gr.Number(label="Final Dynamic Price"),
+]
+
+demo = gr.Interface(
+    fn=predict_dynamic_price,
+    inputs=inputs,
+    outputs=outputs,
+    title="Dynamic Pricing (Stable Version)"
+)
+
+demo.launch()