Update app.py

app.py

@@ -47,16 +47,15 @@ def predict_dynamic_price(
     is_holiday, is_festival
 ):
 
-    # --- Weather ---
+    # ---- WEATHER ----
     weather_factor, temperature = get_weather_features()
 
-    # --- Base Fare ---
+    # ---- BASE FARE ----
     base_price = FIXED_FARE + (distance_km * RATE_PER_KM)
 
-    # --- Initialize feature vector ---
+    # ---- BUILD FEATURE VECTOR ----
     row = {f: 0.0 for f in FEATURES}
 
-    # --- Fill primary inputs ---
     inputs = {
         "zone_id": zone_id,
         "hour": hour,
@@ -80,51 +79,47 @@ def predict_dynamic_price(
         if k in row:
             row[k] = float(v)
 
-    # =========================================================
-    # CRITICAL FIX — FORCE DEMAND/SUPPLY EFFECT STRONG
-    # =========================================================
+    # =====================================================
+    # DEMAND vs SUPPLY EFFECT
+    # =====================================================
     ratio = (demand + 1) / (supply + 1)
     row["demand_supply_ratio"] = np.clip(ratio, 0, 50)
 
-    # =========================================================
-    # HANDLE SEASON FEATURES (avoid all-zero vector)
-    # =========================================================
+    # =====================================================
+    # SEASON FIX (avoid zero vector)
+    # =====================================================
     if "season_winter" in row:
         row["season_winter"] = 0
     if "season_summer" in row:
-        row["season_summer"] = 1   # set one season active
+        row["season_summer"] = 1
     if "season_monsoon" in row:
         row["season_monsoon"] = 0
     if "season_autumn" in row:
         row["season_autumn"] = 0
 
-    # --- Create dataframe in correct order ---
+    # ---- Create dataframe ----
     df_row = pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
 
-    # --- Predict ---
-    surge = float(model.predict(df_row)[0])
-
-    # =========================================================
-    # ADD EXTRA RESPONSE FROM DEMAND/SUPPLY (if model too flat)
-    # =========================================================
+    # ---- MODEL PREDICTION ----
     surge = float(model.predict(df_row)[0])
 
-    # Demand vs Supply effect
+    # =====================================================
+    # EXTRA RESPONSE: DEMAND vs SUPPLY
+    # =====================================================
     surge += 0.15 * (ratio - 1)
 
     # =====================================================
-    # DRIVER AVAILABILITY EFFECT (NEW)
+    # DRIVER AVAILABILITY EFFECT (STRONG)
     # =====================================================
     driver_factor = (supply + 1) / (driver_availability + 1)
+    surge += 0.20 * (driver_factor - 1)
 
-    # If fewer drivers → driver_factor > 1 → surge increases
-    # If many drivers → driver_factor < 1 → surge decreases
-
-    surge += 0.10 * (driver_factor - 1)
-
-    # Final stability clip
+    # ---- Stability ----
     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 =====================
@@ -154,8 +149,7 @@ demo = gr.Interface(
     fn=predict_dynamic_price,
     inputs=inputs,
     outputs=outputs,
-    title="Dynamic Pricing (Demand/Supply Fixed)"
+    title="Dynamic Pricing (Fully Responsive)"
 )
 
 demo.launch()
-