static price reading

backend/provider/app.py

@@ -24,6 +24,7 @@ from procesing.steps import (
 )
 from procesing import PipelineContext
 sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../lib/")
+print(os.path.dirname(os.path.abspath(__file__))+ "/../../lib/")
 from lib.model_registry import ModelRegistry
 
 # Config
@@ -53,20 +54,12 @@ 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):
+    # fetch pre-computed prices from registry
-        def __init__(self, backend_url: str):
+    prices_df = registry.get_prices('latest')
-            SupabaseProvider.__init__(self)
-            BackendAPIProvider.__init__(self, backend_url=backend_url)
-
-    context = PipelineContext(
-        provider=Provider(backend_url=os.getenv("BACKEND_URL")),
-        store_mode=mode
-    )
-
-    pricing_model = registry.get_pricing_model('latest')
     elasticity_df = registry.get_elasticity('latest')
 
-    if pricing_model is None or elasticity_df is None:
+    if prices_df is None:
+        # fallback: no pre-computed prices available
         return PriceResponse(
             productId=productId,
             price=base_price,
@@ -75,87 +68,26 @@ def get_price(mode: Literal['hotel', 'airline'], productId: str, sessionId: Opti
             elasticity=None
         )
 
-    products = context.products
+    # lookup pre-computed price for this product
-    if products.empty:
-        raise HTTPException(500, "No products available in catalog")
-
-    # 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})
-
-    # compute demand: use pricer's mean_demand if available, else default
-    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)
-
-    # build state space with session features if sessionId provided
-    session_features = pd.DataFrame()
-    if sessionId:
-        try:
-            # fetch recent session interactions from backend
-            from procesing.steps.session import ExtractSessionFeaturesStep
-            import requests
-            from datetime import datetime, timedelta
-
-            t_end = datetime.utcnow()
-            t_start = t_end - timedelta(hours=1)
-            backend_url = os.getenv("BACKEND_URL")
-            print(backend_url)
-
-            resp = requests.get(
-                f"{os.getenv('BACKEND_URL')}/api/kafka/dump", # TODO: THIS IS SHIT, must fix this
-                params={'topic': 'user-interactions', 't_start': t_start.isoformat(), 't_end': t_end.isoformat()},
-                timeout=2
-            )
-
-            if resp.ok:
-                msgs = resp.json().get('messages', [])
-                interactions_df = pd.DataFrame(msgs)
-
-                if not interactions_df.empty and 'sessionId' in interactions_df.columns:
-                    session_interactions = interactions_df[interactions_df['sessionId'] == sessionId]
-
-                    if not session_interactions.empty:
-                        extractor = ExtractSessionFeaturesStep(context=context)
-                        session_features_df = extractor.transform(session_interactions)
-
-                        if not session_features_df.empty:
-                            session_features = session_features_df.drop(columns=['sessionId'])
-        except Exception as e:
-            print(f"[session-features-error] {e}")
-            # continue without session features
-
-    state = StateSpace(
-        demand=demand_values,
-        prices=merged['base_price'].values,
-        session_features=session_features,
-        product_ids=merged['productId'].values,
-        elasticity=merged['elasticity'].values,
-        metadata={'sessionId': sessionId, 'experimentId': experimentId}
-    )
-
-    oracle = PredictPricesStep(context=context)
-    prices_df = oracle.transform((pricing_model, state))
-
     product_price_row = prices_df[prices_df['productId'] == productId]
     if product_price_row.empty:
-        raise HTTPException(404, f"No pricing available for product {productId}")
+        # product not in pre-computed prices, fallback to base
+        return PriceResponse(
+            productId=productId,
+            price=base_price,
+            base_price=base_price,
+            markup=1.0,
+            elasticity=None
+        )
 
     optimal_price = float(product_price_row['predicted_price'].iloc[0])
 
-    product_elasticity_row = elasticity_df[elasticity_df['productId'] == productId]
+    # get elasticity if available
-    product_elasticity = (float(product_elasticity_row['elasticity'].iloc[0])
+    product_elasticity = None
-                         if not product_elasticity_row.empty else None)
+    if elasticity_df is not None:
+        product_elasticity_row = elasticity_df[elasticity_df['productId'] == productId]
+        if not product_elasticity_row.empty:
+            product_elasticity = float(product_elasticity_row['elasticity'].iloc[0])
 
     return PriceResponse(
         productId=productId,

docker-compose.yml

@@ -208,6 +208,7 @@ services:
       - KAFKA_PORT=29092
       - NEXT_PUBLIC_SUPABASE_URL=${NEXT_PUBLIC_SUPABASE_URL}
       - NEXT_PUBLIC_SUPABASE_ANON_KEY=${NEXT_PUBLIC_SUPABASE_ANON_KEY}
+      - BACKEND_URL=http://localhost:5000
     ports:
       - "${PROVIDER_PORT:-5001}:5001"
     volumes:

experiments/airflow/dags/elasticity_pricing_dag.py

@@ -202,7 +202,7 @@ def predict_prices(**kwargs):
     return len(prices_df)
 
 def publish_results(**kwargs):
-    """Task: Publish elasticity and pricing results to model registry"""
+    """Task: Publish elasticity, pricing model, and predicted prices to registry"""
     ti = kwargs['ti']
     elasticity_df = pickle.loads(ti.xcom_pull(key='elasticity_results'))
     prices_df = pickle.loads(ti.xcom_pull(key='predicted_prices'))
@@ -222,7 +222,6 @@ def publish_results(**kwargs):
 
     registry.publish_elasticity(elasticity_df, model_name='latest', metadata=metadata)
 
-    # get fitted pricer from XCom
     pricer = pickle.loads(ti.xcom_pull(key='pricer'))
     registry.publish_pricing_model(
         pricer,
@@ -230,10 +229,13 @@ def publish_results(**kwargs):
         metadata={**metadata, 'model_type': type(pricer).__name__}
     )
 
-    logging.info(f"Published elasticity + pricing for {len(elasticity_df)} products")
+    registry.publish_prices(prices_df, model_name='latest', metadata=metadata)
+
+    logging.info(f"Published elasticity + pricing + prices for {len(elasticity_df)} products")
 
     return {
         'n_products': len(elasticity_df),
+        'n_prices': len(prices_df),
         'registry_status': 'success',
         'elasticity_mean': float(elasticity_df['elasticity'].mean())
     }

experiments/procesing/pipelines.py

@@ -121,6 +121,8 @@ if __name__ == '__main__':
     context = PipelineContext(
         provider=Provider(backend_url="http://localhost:5000"),
         store_mode='hotel',
+        # 15 min not month
+        window_size='15min',
     )
 
     elasticity_df, prices_df = full_pipeline(context)

experiments/procesing/steps/pricing.py

@@ -2,6 +2,7 @@ import numpy as np
 import pandas as pd
 from typing import Optional, List, Dict, Any
 from dataclasses import dataclass, field
+from experiments.procesing.pricers.simple import StaticPricer
 from procesing.steps.base import BaseContextStep
 from procesing.pricers import ElasticityBasedPricer
 
@@ -113,17 +114,17 @@ class BuildStateSpaceStep(BaseContextStep):
 
 class FitPricingFunctionStep(BaseContextStep):
     """
-    Fit pricing function using elasticity data.
+    Fit pricing function using data.
-    Input: elasticity_df
+    Input: pricing_data
     Output: fitted pricing function instance
     """
 
-    def transform(self, elasticity_df: pd.DataFrame):
+    def transform(self, pricing_data: pd.DataFrame):
-        pricing_class = self.context.config.get('pricing_function_class', ElasticityBasedPricer)
+        pricing_class = self.context.config.get('pricing_function_class', StaticPricer)
         pricing_params = self.context.config.get('pricing_function_params', {})
 
         pricer = pricing_class(**pricing_params)
-        pricer.fit(elasticity_df)
+        pricer.fit(pricing_data)
 
         return pricer
 

lib/model_registry.py

@@ -26,6 +26,7 @@ class ModelRegistry:
         self.metadata_prefix = "model:meta:"
         self.data_prefix = "model:data:"
         self.elasticity_prefix = "elasticity:"
+        self.prices_prefix = "prices:"
 
     def publish_elasticity(self,
                           elasticity_df: pd.DataFrame,
@@ -130,6 +131,46 @@ class ModelRegistry:
 
         return models
 
+    def publish_prices(self,
+                      prices_df: pd.DataFrame,
+                      model_name: str = 'latest',
+                      metadata: Optional[Dict[str, Any]] = None):
+        """Store predicted prices in registry.
+
+        Args:
+            prices_df: df with [productId, predicted_price, ...]
+            model_name: identifier for this price snapshot
+            metadata: additional info
+        """
+        key = f"{self.prices_prefix}{model_name}"
+        data_json = prices_df.to_json(orient='records')
+
+        self.redis_client.set(key, data_json)
+
+        meta = metadata or {}
+        meta.update({
+            'n_products': len(prices_df),
+            'model_type': 'predicted_prices'
+        })
+
+        meta_key = f"{self.metadata_prefix}prices_{model_name}"
+        self.redis_client.set(meta_key, json.dumps(meta))
+
+        log.info(f"Published prices '{model_name}' for {len(prices_df)} products")
+
+    def get_prices(self, model_name: str = 'latest') -> Optional[pd.DataFrame]:
+        """Retrieve predicted prices from registry."""
+        key = f"{self.prices_prefix}{model_name}"
+        data_json = self.redis_client.get(key)
+
+        if data_json is None:
+            return None
+
+        if isinstance(data_json, bytes):
+            data_json = data_json.decode('utf-8')
+
+        return pd.read_json(data_json, orient='records')
+
     def health_check(self) -> bool:
         """Check if Redis connection is alive."""
         try: