Update app.py

app.py

@@ -1,34 +1,114 @@
+import gradio as gr
+import joblib
 import pandas as pd
+import numpy as np
 
-df = pd.read_csv("dynamic_pricing_data.csv")
-df.head()
-from sklearn.linear_model import Ridge
-from sklearn.model_selection import train_test_split
+# -------------------------------------------------
+# Load trained artifact (NO dataset, NO training)
+# -------------------------------------------------
+ARTIFACT_PATH = "dynamic_pricing_artifact_v1.joblib"
 
-X = df.drop("surge", axis=1)
-y = df["surge"]
+artifact = joblib.load(ARTIFACT_PATH)
 
-FEATURES = X.columns.tolist()
+model = artifact["model"]
+FEATURES = artifact["features"]
+FIXED_FARE = artifact["fixed_fare"]
+RATE_PER_KM = artifact["rate_per_km"]
 
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.2, random_state=42
-)
+# -------------------------------------------------
+# Prediction Logic
+# -------------------------------------------------
+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
+):
 
-model = Ridge(alpha=1.0)
-model.fit(X_train, y_train)
+    # Base price calculation
+    base_price = FIXED_FARE + (distance_km * RATE_PER_KM)
 
-print("Training completed")
-import joblib
+    # Initialize input row
+    row = {f: 0.0 for f in FEATURES}
+
+    inputs = {
+        "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
+    }
+
+    # Fill feature vector
+    for k, v in inputs.items():
+        if k in row:
+            row[k] = float(v)
+
+    # Derived feature
+    row["demand_supply_ratio"] = np.clip(demand / (supply + 1), 0, 20)
 
-FIXED_FARE = 30
-RATE_PER_KM = 6
+    # Create DataFrame in correct order
+    df_row = pd.DataFrame([[row[f] for f in FEATURES]], columns=FEATURES)
 
-artifact = {
-    "model": model,
-    "features": FEATURES,
-    "fixed_fare": FIXED_FARE,
-    "rate_per_km": RATE_PER_KM
-}
+    # Predict surge
+    surge = float(model.predict(df_row)[0])
+    surge = np.clip(surge, 1.0, 2.5)
 
-joblib.dump(artifact, "dynamic_pricing_artifact.joblib")
+    final_price = base_price * surge
+
+    return round(base_price, 2), round(surge, 3), round(final_price, 2)
+
+# -------------------------------------------------
+# 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="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 (1–12)", 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 Dynamic Price"),
+]
+
+demo = gr.Interface(
+    fn=predict_dynamic_price,
+    inputs=inputs,
+    outputs=outputs,
+    title="Dynamic Pricing Model (CPU)",
+    description=(
+        "Inference-only Dynamic Pricing model. "
+        "Model trained on Hugging Face, exported to ONNX, "
+        "and designed for CPU-only deployment."
+    )
+)
 
+demo.launch()