Update app.py

app.py

@@ -9,7 +9,7 @@ import time
 # WEATHER CONFIG
 # ================================
 API_KEY = "YOUR_API_KEY"
-CITY = "Visakhapatnam"
+CITY = "Visakhapatnam,IN"
 
 _last_weather = None
 _last_time = 0
@@ -17,7 +17,7 @@ _last_time = 0
 def get_weather_features():
     global _last_weather, _last_time
 
-    # Cache for 5 minutes (avoid API spam)
+    # Cache for 5 minutes
     if _last_weather and (time.time() - _last_time < 300):
         return _last_weather
 
@@ -29,30 +29,27 @@ def get_weather_features():
         condition = data["weather"][0]["main"]
         temp = data["main"]["temp"]
 
-        if condition == "Clear":
-            weather_factor = 1.0
-        elif condition == "Clouds":
-            weather_factor = 1.05
-        elif condition == "Rain":
-            weather_factor = 1.20
-        elif condition == "Thunderstorm":
-            weather_factor = 1.30
-        else:
-            weather_factor = 1.0
+        mapping = {
+            "Clear": 1.0,
+            "Clouds": 1.05,
+            "Rain": 1.20,
+            "Thunderstorm": 1.30
+        }
+
+        weather_factor = mapping.get(condition, 1.0)
 
         _last_weather = (weather_factor, temp)
         _last_time = time.time()
         return _last_weather
 
     except:
-        return (1.0, 30)  # fallback safe
+        return (1.0, 30)
 
 
 # ================================
-# LOAD MODEL ARTIFACT
+# LOAD MODEL
 # ================================
-ARTIFACT_PATH = "dynamic_pricing_artifact_v1.joblib"
-artifact = joblib.load(ARTIFACT_PATH)
+artifact = joblib.load("dynamic_pricing_artifact_v1.joblib")
 
 model = artifact["model"]
 FEATURES = artifact["features"]
@@ -71,13 +68,13 @@ def predict_dynamic_price(
     is_holiday, is_festival
 ):
 
-    # ---- Auto fetch weather ----
+    # -------- WEATHER --------
     weather_factor, temperature = get_weather_features()
 
-    # ---- Base price ----
+    # -------- BASE FARE --------
     base_price = FIXED_FARE + (distance_km * RATE_PER_KM)
 
-    # ---- Build feature row ----
+    # -------- BUILD FEATURE VECTOR --------
     row = {f: 0.0 for f in FEATURES}
 
     inputs = {
@@ -103,51 +100,20 @@ def predict_dynamic_price(
         if k in row:
             row[k] = float(v)
 
-    # Derived feature
-    row["demand_supply_ratio"] = np.clip(demand / (supply + 1), 0, 20)
+    # -------- CRITICAL: DEMAND/SUPPLY RATIO --------
+    row["demand_supply_ratio"] = np.clip((demand + 1) / (supply + 1), 0, 50)
 
     df_row = pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
 
-    # ---- Predict surge ----
-    surge = float(model.predict(df_row)[0])
-    surge = np.clip(surge, 1.0, 2.5)  # stability
-
-    final_price = base_price * surge
+    # -------- DEBUG (Remove later) --------
+    print("Demand:", demand, "Supply:", supply, "Drivers:", driver_availability)
+    print("Ratio:", row["demand_supply_ratio"])
 
-    return round(base_price, 2), round(surge, 3), round(final_price, 2)
+    # -------- MODEL PREDICTION --------
+    surge = float(model.predict(df_row)[0])
+    print("Raw Surge:", surge)
 
+    # Avoid constant clipping hiding variation
+    surge = np.clip(surge, 1.0, 3.5)
 
-# ================================
-# GRADIO 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 (1.0–1.5)", value=1.0),
-    gr.Number(label="Traffic Index (0–1)", value=0.5),
-    gr.Number(label="Distance (km)", value=10),
-    gr.Number(label="Duration (min)", value=20),
-    gr.Number(label="Hour (0–23)", value=18),
-    gr.Number(label="Day of Week (0=Mon)", value=4),
-    gr.Number(label="Is Weekend (0/1)", value=0),
-    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 Dynamic Price"),
-]
-
-demo = gr.Interface(
-    fn=predict_dynamic_price,
-    inputs=inputs,
-    outputs=outputs,
-    title="Dynamic Pricing Model (Auto Weather)",
-    description="Dynamic Pricing with real-time weather from OpenWeatherMap."
-)
-
-demo.launch()
+    final_price = base_price * surge