chore: e2e is done with new pipeline

backend/provider/app.py

@@ -4,9 +4,27 @@ from pydantic import BaseModel
 from typing import Literal, Optional
 import uvicorn, os, sys
 from supabase import create_client, Client
+from dotenv import load_dotenv
+import numpy as np
+import pandas as pd
+load_dotenv()
 
 # Local imports of registry and pricing function
 
+sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../experiments/")
+from procesing.providers import SupabaseProvider, BackendAPIProvider
+from procesing.pricers import (
+    StaticPricer,
+    RandomPricer,
+    ElasticityBasedPricer
+)
+from procesing.steps import (
+    StateSpace,
+    PredictPricesStep
+)
+from procesing import PipelineContext
+sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../lib/")
+from lib.model_registry import ModelRegistry
 
 # Config
 app = FastAPI(title="PHANTOM Pricing Provider")
@@ -34,23 +52,80 @@ def get_price(mode: Literal['hotel', 'airline'], productId: str, sessionId: Opti
 
     metadata = product['metadata']
     base_price = metadata.get('base_price', 100.0)
+
+    class Provider(SupabaseProvider, BackendAPIProvider):
+        def __init__(self, backend_url: str):
+            SupabaseProvider.__init__(self)
+            BackendAPIProvider.__init__(self, backend_url=backend_url)
+    context = PipelineContext(
+        provider=Provider(backend_url=os.getenv("BACKEND_API_URL")),
+        store_mode=mode
+    )
+
+    pricing_model = registry.get_pricing_model('latest')
     elasticity_df = registry.get_elasticity('latest')
 
-    if not elasticity_df:
-        return PriceResponse(productId=productId, price=base_price, base_price=base_price, markup=1.0)
+    if pricing_model is None or elasticity_df is None:
+        # fallback to base price if no model available
+        return PriceResponse(
+            productId=productId,
+            price=base_price,
+            base_price=base_price,
+            markup=1.0,
+            elasticity=None
+        )
 
-    pricing_model = registry.get_pricing_model('latest') or ElasticityBasedPricingFunction().fit(elasticity_df)
-    product_elasticity = elasticity_df[elasticity_df['productId'] == productId]['elasticity'].iloc[0] if (elasticity_row := elasticity_df[elasticity_df['productId'] == productId]).any().any() else None
+    # build full state space for all products in catalog
+    products = context.products
+    if products.empty:
+        raise HTTPException(500, "No products available in catalog")
 
-    state = StateSpace(np.array([0.0]), np.array([base_price]), pd.DataFrame())
-    optimal_price = pricing_model.transform(state, np.array([productId]))[0]
+    # merge elasticity with product base prices
+    products_with_meta = products.copy()
+    products_with_meta['base_price'] = products_with_meta['metadata'].apply(
+        lambda m: m.get('base_price', 100.0) if isinstance(m, dict) else 100.0
+    )
+
+    merged = products_with_meta[['id', 'base_price']].rename(
+        columns={'id': 'productId'}
+    ).merge(
+        elasticity_df[['productId', 'elasticity']],
+        on='productId',
+        how='left'
+    ).fillna({'elasticity': 0.0})
+
+    # use fitted pricer's mean_demand if available, else default to 10.0
+    demand_values = (pricing_model.mean_demand
+                    if hasattr(pricing_model, 'mean_demand') and pricing_model.mean_demand is not None
+                    else np.ones(len(merged)) * 10.0)
+
+    state = StateSpace(
+        demand=demand_values,
+        prices=merged['base_price'].values,
+        session_features=pd.DataFrame()
+    )
+
+    oracle = PredictPricesStep(context=context)
+    prices_df = oracle.transform((pricing_model, state))
+
+    # extract price for requested product
+    product_price_row = prices_df[prices_df['productId'] == productId]
+    if product_price_row.empty:
+        raise HTTPException(404, f"No pricing available for product {productId}")
+
+    optimal_price = float(product_price_row['predicted_price'].iloc[0])
+
+    # extract elasticity if available
+    product_elasticity_row = elasticity_df[elasticity_df['productId'] == productId]
+    product_elasticity = (float(product_elasticity_row['elasticity'].iloc[0])
+                         if not product_elasticity_row.empty else None)
 
     return PriceResponse(
         productId=productId,
-        price=float(optimal_price),
+        price=optimal_price,
         base_price=base_price,
         markup=optimal_price/base_price,
-        elasticity=float(product_elasticity) if product_elasticity is not None else None
+        elasticity=product_elasticity
     )
 
 @app.get("/models")