Update app.py

app.py

@@ -1,114 +1,61 @@
 import gradio as gr
 import joblib
-import pandas as pd
 import numpy as np
-import os
 
-# ---------------------------------------------------------
-# 1) LOAD ARTIFACT
-# ---------------------------------------------------------
-ARTIFACT_PATH = "dynamic_pricing_artifact.joblib"
+# Load model DIRECTLY
+model = joblib.load("dynamic_pricing_artifact.joblib")
 
-if not os.path.exists(ARTIFACT_PATH):
-    raise FileNotFoundError("Upload dynamic_pricing_artifact.joblib to your Space!")
+FIXED_FARE = 30
+RATE_PER_KM = 6
 
-artifact = joblib.load(ARTIFACT_PATH)
-model = artifact["model"]
-FEATURES = artifact["features"]
-
-
-# ---------------------------------------------------------
-# 2) PREDICTION FUNCTION
-# ---------------------------------------------------------
-def gh_predict(
-    zone_id, demand, supply, driver_availability, weather_factor, event_factor,
-    temperature, traffic_index, distance_km, duration_min, base_fare_fixed,
-    hour, day_of_week, is_weekend, month, is_holiday, is_festival
+def predict_price(
+    demand, supply, weather_factor, event_factor,
+    traffic_index, is_weekend, is_holiday, is_festival,
+    hour, distance_km
 ):
-
-    RATE_PER_KM = 6  # distance pricing rule
-
-    # -------- Base Price (distance-based) ----------
-    distance_price = distance_km * RATE_PER_KM
-    base_price = float(base_fare_fixed) + distance_price
-
-    # -------- ML Input Row ----------
-    inp = {
-        "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,   # model needs this
-    }
-
-    # demand supply ratio
-    inp["demand_supply_ratio"] = np.clip(demand / (supply + 1), 0, 10)
-
-    # ------ Build single row DF in correct order -------
-    row = {f: 0.0 for f in FEATURES}
-    for k, v in inp.items():
-        if k in row:
-            row[k] = float(v)
-
-    # create DataFrame
-    df_row = pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
-
-    # -------- Predict Surge --------
-    surge = float(model.predict(df_row)[0])
-
-    # -------- Final Price ----------
+    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
+
+    base_price = FIXED_FARE + distance_km * RATE_PER_KM
     final_price = base_price * surge
 
-    return round(base_price, 2), round(surge, 3), round(final_price, 2)
-
-
-# ---------------------------------------------------------
-# 3) 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="Weather Factor (1.0–1.35)", value=1.0),
-    gr.Number(label="Event Factor (1.0–1.5)", value=1.0),
-    gr.Number(label="Temperature °C", value=30),
-    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="Base Fare (Fixed)", value=30),
-    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="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 (Distance Based)"),
-    gr.Number(label="Predicted Surge Factor"),
-    gr.Number(label="Final Dynamic Price"),
-]
+    return round(base_price,2), round(surge,2), round(final_price,2)
 
 demo = gr.Interface(
-    fn=gh_predict,
-    inputs=inputs,
-    outputs=outputs,
-    title="Dynamic Pricing Model",
-    description="This model adjusts fare using demand, supply, weather, events, weekends, holidays & festivals. Base fare = fixed base fare + distance × KM rate.",
+    fn=predict_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)")
+    ],
+    outputs=[
+        gr.Number(label="Base Price"),
+        gr.Number(label="Surge Factor"),
+        gr.Number(label="Final Price")
+    ],
+    title="Dynamic Pricing Model"
 )
 
 demo.launch()