fix: adding date utils

* a simp0le scaffold

* feature: simple npm setup

* feature: testing setup and dummy scenarios

* chore: dumping kafak just via backend

* chore: dcleaning gitignore

* features: boilerplate fixtures and stuff

* test: extra tests

* chore: update the test suite to be callable via makefile

* chore: cleaning

* chore: updating interactions setup

* small cleaning

* chore: cleaning shitty code

Makefile

@@ -48,8 +48,10 @@ test.backend: $(VENV)
 test.e2e:
 	@cd tests/e2e && npm install
 	@cd tests/e2e && npx playwright install chromium
+	@test -f tests/e2e/.env || cp tests/e2e/.env.example tests/e2e/.env
 	@timeout 30 bash -c 'until curl -sf http://localhost:5000/health > /dev/null 2>&1; do sleep 1; done' || (echo "Backend not ready" && exit 1)
 	@timeout 30 bash -c 'until curl -sf http://localhost:3000 > /dev/null 2>&1; do sleep 1; done' || (echo "Web app not ready" && exit 1)
+	@timeout 30 bash -c 'until curl -sf http://localhost:8085/health > /dev/null 2>&1; do sleep 1; done' || (echo "Airflow not ready" && exit 1)
 	@cd tests/e2e && npm test
 
 .PHONY: test.all

backend/provider/app.py

@@ -47,53 +47,52 @@ def health() -> dict:
 
 @app.get("/api/{mode}/price/{productId}", response_model=PriceResponse)
 def get_price(mode: Literal['hotel', 'airline'], productId: str, sessionId: Optional[str] = Query(None), experimentId: Optional[str] = Query(None)):
+    """
+    THIS is the fast lookup service (mechanism).
+    Priority: session-keyed price > global optimal price > base price
+    """
     product = supabase.table(f'{mode}_products').select("metadata").eq('id', productId).execute().data[0]
     if not product: raise HTTPException(404, f"Product {productId} not found")
 
     metadata = product['metadata']
     base_price = metadata.get('base_price', 100.0)
 
-    # fetch pre-computed prices from registry
+    # PRIORITY 1: session-aware price (computed by Airflow worker)
+    if sessionId:
+        session_price = registry.get_session_price(sessionId, productId)
+        if session_price is not None:
+            return PriceResponse(
+                productId=productId,
+                price=session_price,
+                base_price=base_price,
+                markup=session_price/base_price,
+                elasticity=None,
+                model_version='session-aware'
+            )
+
+    # PRIORITY 2: global pre-computed prices (surge pricing)
     prices_df = registry.get_prices('latest')
-    elasticity_df = registry.get_elasticity('latest')
+    if prices_df is not None:
-
-    if prices_df is None:
-        # fallback: no pre-computed prices available
-        return PriceResponse(
-            productId=productId,
-            price=base_price,
-            base_price=base_price,
-            markup=1.0,
-            elasticity=None
-        )
-
-    # lookup pre-computed price for this product
         product_price_row = prices_df[prices_df['productId'] == productId]
-    if product_price_row.empty:
+        if not product_price_row.empty:
-        # product not in pre-computed prices, fallback to base
+            optimal_price = float(product_price_row['optimal_price'].iloc[0])
-        return PriceResponse(
-            productId=productId,
-            price=base_price,
-            base_price=base_price,
-            markup=1.0,
-            elasticity=None
-        )
-
-    optimal_price = float(product_price_row['optimal_price'].iloc[0]) # TODO: use optimal_price everywhere as  aresult
-
-    # get elasticity if available
-    product_elasticity = 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,
                 price=optimal_price,
                 base_price=base_price,
                 markup=optimal_price/base_price,
-        elasticity=product_elasticity
+                elasticity=None,
+                model_version='surge'
+            )
+
+    # PRIORITY 3: fallback to base price
+    return PriceResponse(
+        productId=productId,
+        price=base_price,
+        base_price=base_price,
+        markup=1.0,
+        elasticity=None,
+        model_version='base'
     )
 
 @app.get("/models")

backend/server/app.py

@@ -198,12 +198,16 @@ def dump_logs(
             auto_offset_reset='earliest',
             enable_auto_commit=False,
             value_deserializer=lambda x: json.loads(x.decode('utf-8')),
-            consumer_timeout_ms=5000
+            consumer_timeout_ms=30000,
+            fetch_max_wait_ms=10000,
+            max_poll_records=1000
         )
 
         events = []
         for msg in consumer:
             events.append(msg.value)
+            if last_n and len(events) >= last_n * 2:
+                break
 
         consumer.close()
 

docker-compose.yml

@@ -1,8 +1,17 @@
 services:
-
+  tensorboard-rl:
-  tensorboard:
     image: tensorflow/tensorflow:latest
-    container_name: "PHANTOM-tensorboard"
+    container_name: "PHANTOM-tensorboard-rl"
+    ports:
+      - "6007:6006"
+    volumes:
+      - ./sim/rl/runs:/logs
+    command: tensorboard --logdir=/logs --host=0.0.0.0 --port=6006
+    restart: unless-stopped
+
+  tensorboard-ml:
+    image: tensorflow/tensorflow:latest
+    container_name: "PHANTOM-tensorboard-ml"
     ports:
       - "6006:6006"
     volumes:
@@ -103,11 +112,14 @@ services:
     depends_on:
       - postgres
     environment:
-      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
       - AIRFLOW__CORE__LOAD_EXAMPLES=false
       - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__CORE__PARALLELISM=16
+      - AIRFLOW__CORE__DAG_CONCURRENCY=8
+      - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=4
       - _AIRFLOW_DB_MIGRATE=true
       - _AIRFLOW_WWW_USER_CREATE=true
       - _AIRFLOW_WWW_USER_USERNAME=admin
@@ -127,14 +139,20 @@ services:
       - airflow-init
       - redis
     environment:
-      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
       - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=true
       - AIRFLOW__CORE__LOAD_EXAMPLES=false
       - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__CORE__PARALLELISM=16
+      - AIRFLOW__CORE__DAG_CONCURRENCY=8
+      - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=4
+      - AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL=30
+      - AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL=60
       - AIRFLOW__WEBSERVER__EXPOSE_CONFIG=true
       - AIRFLOW__WEBSERVER__SECRET_KEY=${AIRFLOW_SECRET_KEY}
+      - AIRFLOW__API__AUTH_BACKENDS=airflow.api.auth.backend.basic_auth
       - KAFKA_HOST=kafka
       - KAFKA_PORT=29092
       - BACKEND_URL=http://backend:5000
@@ -164,13 +182,20 @@ services:
       redis:
         condition: service_started
     environment:
-      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
       - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=true
       - AIRFLOW__CORE__LOAD_EXAMPLES=false
       - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__CORE__PARALLELISM=16
+      - AIRFLOW__CORE__DAG_CONCURRENCY=8
+      - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=4
+      - AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL=30
+      - AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL=60
+      - AIRFLOW__SCHEDULER__PARSING_PROCESSES=2
       - AIRFLOW__WEBSERVER__SECRET_KEY=${AIRFLOW_SECRET_KEY}
+      - AIRFLOW__API__AUTH_BACKENDS=airflow.api.auth.backend.basic_auth
       - KAFKA_HOST=kafka
       - KAFKA_PORT=29092
       - BACKEND_URL=http://backend:5000

experiments/airflow/dags/surge_pricing_factory.py

@@ -1,3 +1,4 @@
+from pandas.core.algorithms import factorize_array
 from airflow import DAG
 from airflow.operators.python import PythonOperator
 from airflow.utils.dates import days_ago
@@ -208,3 +209,12 @@ def create_surge_pricing_dag(store_mode: str) -> DAG:
 # instantiate DAGs for Airflow to discover
 dag_airline = create_surge_pricing_dag('airline')
 dag_hotel = create_surge_pricing_dag('hotel')
+
+# TODO: Refactor this factory from a surge pricing factory to a general pricing factory
+# We will do this by passing a pricing strategy class to the factory, since the generic pipeline is:
+# take all interaction data, group by sessionId and assign a new price vector to each session
+# in the grouping we get a subset of the interactions per sessionId and we can map that to some Features
+# we define a custom _get_features(interactions .) methodin the strategy class
+# we then run only the inference which is the .predict(trajectory) per-session which will give us a new price vector
+# this we then publish for each sessionId group
+# this might include no deleting most of the pricers we have defined and starting with a super simple surge-pricing algorithm that is no-fit only predict. This we can then test end-to-end and observe changes to prices according to a desired strategy - we have to define this one as a very short term strategy because we run sessions that take only a few minutes.

experiments/airflow/dags/surge_pricing_pipeline.py

@@ -120,15 +120,31 @@ def apply_surge_pricing(**kwargs):
     # rename demand_score to demand for pricer compatibility
     data = product_features.rename(columns={'demand_score': 'demand'})
 
+    high_thresh = dag_conf.get('high_threshold', 10)
+    low_thresh = dag_conf.get('low_threshold', 2)
+    surge_mult = dag_conf.get('surge_multiplier', 1.2)
+    discount_mult = dag_conf.get('discount_multiplier', 0.9)
+
+    logging.info(f"Surge pricing config: high_thresh={high_thresh}, low_thresh={low_thresh}, surge_mult={surge_mult}, discount_mult={discount_mult}")
+    logging.info(f"Demand stats: min={data['demand'].min():.2f}, max={data['demand'].max():.2f}, mean={data['demand'].mean():.2f}")
+    logging.info(f"Products with high demand (>={high_thresh}): {(data['demand'] >= high_thresh).sum()}")
+    logging.info(f"Products with low demand (<={low_thresh}): {(data['demand'] <= low_thresh).sum()}")
+
     surge_pricer = SimpleSurgePricer(
-        high_threshold=dag_conf.get('high_threshold', 10),
+        high_threshold=high_thresh,
-        low_threshold=dag_conf.get('low_threshold', 2),
+        low_threshold=low_thresh,
-        surge_multiplier=dag_conf.get('surge_multiplier', 1.2),
+        surge_multiplier=surge_mult,
-        discount_multiplier=dag_conf.get('discount_multiplier', 0.9)
+        discount_multiplier=discount_mult
     )
     surge_pricer.fit(data)
     data['optimal_price'] = surge_pricer.predict()
 
+    base_avg = data['base_price'].mean()
+    optimal_avg = data['optimal_price'].mean()
+    price_change_pct = ((optimal_avg - base_avg) / base_avg) * 100
+
+    logging.info(f"Price adjustment: base_avg={base_avg:.2f}, optimal_avg={optimal_avg:.2f}, change={price_change_pct:+.1f}%")
+
     prices_df = data[['productId', 'price', 'base_price', 'optimal_price', 'demand']].rename(columns={
         'price': 'current_price',
         'demand': 'demand_score'

experiments/procesing/pricers/base.py

@@ -7,15 +7,6 @@ import pandas as pd
 class PricingFunction(ABC):
     """
     Abstract base for pricing functions.
-
-    Defines mapping: f(Q_t, P_t, S_t, H_t) -> P_{t+1}
-
-    Where:
-        Q_t ∈ R^n: demand vector at time t
-        P_t ∈ R^n: price vector at time t
-        S_t: session features (behavioral signals, interactions)
-        H_t = {Q_{t-k}, P_{t-k}, S_{t-k}}: historical state trajectory
-
     Objective:
         maximize E[R_T] = E[Σ P_t^T · Q_t]
         subject to:
@@ -28,10 +19,10 @@ class PricingFunction(ABC):
     def fit(self, *kwargs):
         """
         Offline training on historical data.
+        This is where we can think about some maximization of expected revenue
+        over historical trajectories to learn parameters of the pricing function.
+        (This however we cover move in the RL side of things)
 
-        Args:
-            historical_data: DataFrame with elasticity, prices, demand signals
-            **kwargs: additional training parameters
         """
         pass
 
@@ -39,12 +30,18 @@ class PricingFunction(ABC):
     def predict(self, *kwargs) -> np.ndarray:
         """
         Generate optimal prices given current state.
+        This is an abstract method that transitions from τ -> P*
+        which is the mapping from the trajectory to optimal prices under
+        some subset of session grouping (so, per sessionId)
+        """
+        pass
 
-        Args:
+    @abstractmethod
-            state_space: StateSpace object containing Q_t, P_t, S_t, H_t
+    def _get_features(self, *kwargs) -> np.ndarray:
-
+        """
+        Extract features from trajectory for pricing decision.
         Returns:
-            P_{t+1}: price vector in R^n
+            np.ndarray of shape (n_products, n_features)
         """
         pass
 

experiments/procesing/pricers/elasticity.py

@@ -57,3 +57,13 @@ class ElasticityBasedPricer(PricingFunction):
         # enforce bounds
         prices = np.clip(prices, self.price_floor, self.price_ceil)
         return prices
+
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract elasticity, demand, and demand deviation for each product"""
+        if state_space is None or self.elasticity is None:
+            n = len(self.elasticity) if self.elasticity is not None else 0
+            return np.zeros((n, 3))
+
+        demand = np.asarray(state_space.demand)
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+        return np.column_stack([self.elasticity, demand, demand_dev])

experiments/procesing/pricers/session_aware.py

@@ -107,6 +107,36 @@ class SessionAwarePricer(PricingFunction):
 
         return prices
 
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract elasticity, demand, and session features"""
+        if state_space is None or self.elasticity is None:
+            n = len(self.elasticity) if self.elasticity is not None else 0
+            return np.zeros((n, 5))
+
+        demand = np.asarray(state_space.demand)
+        n_products = len(demand)
+
+        # extract session features
+        velocity = 0.0
+        view_depth = 0.0
+        cart_to_view = 0.0
+
+        if not state_space.session_features.empty:
+            sf = state_space.session_features.iloc[0]
+            velocity = sf.get('interaction_velocity', 0.0)
+            view_depth = sf.get('product_view_depth', 0.0)
+            cart_to_view = sf.get('cart_to_view_ratio', 0.0)
+
+        # broadcast session features to all products
+        features = np.column_stack([
+            self.elasticity,
+            demand,
+            np.full(n_products, velocity),
+            np.full(n_products, view_depth),
+            np.full(n_products, cart_to_view)
+        ])
+        return features
+
 
 class ProductSpecificSessionPricer(PricingFunction):
     """
@@ -170,3 +200,12 @@ class ProductSpecificSessionPricer(PricingFunction):
 
         prices = np.clip(base_prices, self.price_floor, self.price_ceil)
         return prices
+
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract elasticity and demand features for product-specific pricing"""
+        if state_space is None or self.elasticity is None:
+            n = len(self.elasticity) if self.elasticity is not None else 0
+            return np.zeros((n, 2))
+
+        demand = np.asarray(state_space.demand)
+        return np.column_stack([self.elasticity, demand])

experiments/procesing/pricers/simple.py

@@ -3,6 +3,46 @@ import pandas as pd
 from procesing.pricers.base import PricingFunction
 
 
+def session_features_to_demand(session_features: pd.DataFrame) -> float:
+    """
+    Map session behavioral features to demand proxy.
+    THIS is the critical θ̂ → D transformation for rule-based pricing.
+
+    Logic:
+      - High velocity → agent behavior → price up (revenue recovery)
+      - High cart ratio → purchase intent → price up
+      - Low activity → discount to convert
+
+    Returns: demand proxy score (0-20 range, higher = more demand)
+    """
+    if session_features.empty:
+        return 1.0
+
+    feat = session_features.iloc[0] if len(session_features) > 0 else {}
+
+    velocity = feat.get('interaction_velocity', 0)
+    cart_ratio = feat.get('cart_to_view_ratio', 0)
+    item_views = feat.get('item_views', 0)
+    cart_adds = feat.get('cart_adds', 0)
+
+    # baseline demand
+    demand = 1.0
+
+    # agent detection: high velocity → treat as high "demand" to price up
+    if velocity > 2.0:
+        demand += 10.0  # strong agent signal
+
+    # conversion intent: cart interaction → price up
+    if cart_ratio > 0.1 or cart_adds > 0:
+        demand += 5.0
+
+    # browsing depth: many views → interest signal
+    if item_views > 3:
+        demand += min(item_views, 5.0)
+
+    return min(demand, 20.0)  # cap at 20
+
+
 class StaticPricer(PricingFunction):
     """Static pricing: always return fixed base prices"""
 
@@ -25,6 +65,11 @@ class StaticPricer(PricingFunction):
             raise ValueError("Must call fit() or provide base_prices in constructor")
         return self.base_prices.copy()
 
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Static pricer uses no features, returns empty array"""
+        n = len(self.base_prices) if self.base_prices is not None else 0
+        return np.zeros((n, 0))
+
 
 class RandomPricer(PricingFunction):
     """Random pricing within bounds (for baseline comparison)"""
@@ -47,6 +92,11 @@ class RandomPricer(PricingFunction):
             self.n_products = len(state_space.demand)
         return self.rng.uniform(self.price_min, self.price_max, size=self.n_products)
 
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Random pricer uses no features"""
+        n = self.n_products if self.n_products else 0
+        return np.zeros((n, 0))
+
 
 class SimpleSurgePricer(PricingFunction):
     """
@@ -70,18 +120,22 @@ class SimpleSurgePricer(PricingFunction):
     def fit(self, market_data: pd.DataFrame):
         """Extract base prices from product catalog or historical averages"""
         self.base_prices = market_data['base_price'].to_numpy() if 'base_price' in market_data.columns else market_data['price'].values
-        self.demand_history = market_data['demand'].to_numpy() if 'demand' in market_data.columns else np.zeros_like(self.base_prices)
+        return self
 
-    def predict(self) -> np.ndarray:
+    def predict(self, state_space) -> np.ndarray:
         """
         Adjust prices based on current demand using surge rules.
-        state_space.demand: demand counts per product
+        state_space.demand: demand proxy per product (from session features)
-        state_space.prices: current prices (fallback if base_prices not set)
+        state_space.prices: base prices
         """
-        current_prices = self.base_prices if self.base_prices is not None else np.ones_like(demand_vector) * 99.99
+        demand = np.asarray(state_space.demand) if state_space and hasattr(state_space, 'demand') else np.array([0])
-        demand = self.demand_history if self.demand_history is not None else np.zeros_like(current_prices)
+        base = np.asarray(state_space.prices) if state_space and hasattr(state_space, 'prices') else self.base_prices
-        new_prices = current_prices.copy()
 
+        if base is None:
+            base = np.ones(len(demand)) * 99.99
+
+        # ensure float dtype to allow multiplication by float multipliers
+        new_prices = base.astype(np.float64).copy()
         high_mask = demand >= self.high_threshold
         new_prices[high_mask] *= self.surge_multiplier
 
@@ -89,3 +143,16 @@ class SimpleSurgePricer(PricingFunction):
         new_prices[low_mask] *= self.discount_multiplier
 
         return new_prices
+
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract demand and base price features for each product"""
+        if state_space is None:
+            n = len(self.base_prices) if self.base_prices is not None else 0
+            return np.zeros((n, 2))
+
+        demand = np.asarray(state_space.demand) if hasattr(state_space, 'demand') else np.array([0])
+        base = np.asarray(state_space.prices) if hasattr(state_space, 'prices') else self.base_prices
+        if base is None:
+            base = np.ones(len(demand)) * 99.99
+
+        return np.column_stack([demand, base])

experiments/procesing/steps/session.py

@@ -135,6 +135,7 @@ class ExtractSessionFeaturesStep(BaseContextStep):
     Vectorized session feature extraction - replaces O(n^2) per-row loop.
     Input: interactions_df
     Output: session-level feature matrix
+    THIS is our main mapping from tau (trajectory) to some features vector theta - we need to do this very well. This is what will go into demand esimation.
     """
 
     def transform(self, X: pd.DataFrame) -> pd.DataFrame:

lib/model_registry.py

@@ -178,3 +178,49 @@ class ModelRegistry:
             return True
         except:
             return False
+
+    def set_session_prices(self, session_id: str, prices: Dict[str, float], ttl: int = 1800):
+        """
+        Store prices for a specific session.
+        THIS is the write path for session-aware pricing.
+
+        Args:
+            session_id: session identifier
+            prices: dict of {productId: price}
+            ttl: time-to-live in seconds (default 30min)
+        """
+        if not prices:
+            return
+
+        key = f"session:{session_id}:prices"
+        # use Redis hash for O(1) lookup per product
+        self.redis_client.hset(key, mapping={k: str(v) for k, v in prices.items()})
+        self.redis_client.expire(key, ttl)
+
+    def get_session_price(self, session_id: str, product_id: str) -> Optional[float]:
+        """
+        Lookup price for (sessionId, productId).
+        THIS is the read path for fast provider lookup.
+
+        Returns: price or None if not found
+        """
+        key = f"session:{session_id}:prices"
+        price_str = self.redis_client.hget(key, product_id)
+
+        if price_str is None:
+            return None
+
+        return float(price_str.decode('utf-8') if isinstance(price_str, bytes) else price_str)
+
+    def get_session_all_prices(self, session_id: str) -> Dict[str, float]:
+        """Get all prices for a session."""
+        key = f"session:{session_id}:prices"
+        prices_raw = self.redis_client.hgetall(key)
+
+        if not prices_raw:
+            return {}
+
+        return {
+            (k.decode('utf-8') if isinstance(k, bytes) else k): float(v.decode('utf-8') if isinstance(v, bytes) else v)
+            for k, v in prices_raw.items()
+        }

sim/rl/behavior_loader/loader.py

@@ -0,0 +1,63 @@
+import os
+from pydantic import BaseModel as Base
+import json
+
+class PayloadModel(Base):
+    sessionId: str
+    experimentId: str | None
+    eventName: str
+    page: str | None
+    productId: str | None
+    metadata: dict
+    storeMode: str
+    userAgent: str
+    ts: str
+
+class ValueModel(Base):
+    payload: PayloadModel
+    encoding: str
+    isPayloadNull: bool
+    schemaId: int
+    size: int
+
+class InteractionModel(Base):
+    partitionID: int
+    offset: int
+    timestamp: int
+    compression: str
+    isTransactional: bool
+    headers: list
+    key: dict
+    value: ValueModel
+
+class Loader:
+    def __init__(self, src_dir: str):
+        self.src_dir = src_dir
+        self.entries = os.listdir(src_dir)
+        if not self.entries: raise ValueError("empty directory")
+        self.data = self._load_sessions()
+
+    def _is_admin_page(self, interaction: InteractionModel) -> bool:
+        page = interaction.value.payload.page
+        return page and page.startswith("/admin/")
+
+    def _load_sessions(self) -> dict:
+        sessions = {}
+        for entry in self.entries:
+            int_path = f"{self.src_dir}/{entry}/int.json"
+            raw = json.load(open(int_path))
+            ints = [InteractionModel(**i) for i in raw]
+            sessions[entry] = [i for i in ints if not self._is_admin_page(i)]
+        return sessions
+
+    def get_data(self) -> dict:
+        return self.data
+
+    def get_entries(self) -> tuple[list[str], int]:
+        return self.entries, len(self.entries)
+
+if __name__ == "__main__":
+    DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
+    loader = Loader(DIR)
+    _, n = loader.get_entries()
+    print(f"Loaded {n} sessions from {DIR}")

sim/rl/behavior_loader/models.py

@@ -0,0 +1,144 @@
+from loader import Loader
+from collections import defaultdict
+from typing import Dict, List, Tuple, Set
+import numpy as np
+import graphviz
+
+DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
+
+class BehaviorModel:
+    def __init__(self, src_dir: str = DIR):
+        self.loader = Loader(src_dir)
+        self.data = self.loader.get_data()
+        self.entries, self.num_entries = self.loader.get_entries()
+        self.mdp = None
+
+    def _state_repr(self, evt) -> str:
+        p = evt.value.payload
+        return f"{p.page or 'unk'}|{p.productId or 'none'}|{p.eventName}"
+
+    def _extract_sessions(self):
+        # transform raw events into sequential state trajectories per session
+        trajectories = []
+        for sid, evts in self.data.items():
+            if len(evts) < 2: continue
+            states = [self._state_repr(e) for e in sorted(evts, key=lambda x: x.timestamp)]
+            trajectories.append(states)
+        return trajectories
+
+    def _calc_transitions(self, trajectories: List[List[str]]) -> Tuple[Dict, Set]:
+        trans = defaultdict(lambda: defaultdict(int))
+        states = set()
+        for traj in trajectories:
+            for i in range(len(traj) - 1):
+                s, s_next = traj[i], traj[i+1]
+                trans[s][s_next] += 1
+                states.update([s, s_next])
+        return trans, states
+
+    def _calc_rewards(self, trajectories: List[List[str]]) -> Dict:
+        # reward based on session progression depth
+        rwd = defaultdict(list)
+        for traj in trajectories:
+            n = len(traj)
+            for i, s in enumerate(traj):
+                rwd[s].append(i / n)
+        return rwd
+
+    def _normalize_trans(self, counts: Dict) -> Dict:
+        return {s: {s_n: cnt/sum(nxt.values()) for s_n, cnt in nxt.items()}
+                for s, nxt in counts.items()}
+
+    def build_MDP(self) -> Dict:
+        trajs = self._extract_sessions()
+        trans_cnt, states = self._calc_transitions(trajs)
+        trans_prob = self._normalize_trans(trans_cnt)
+        state_rwd = self._calc_rewards(trajs)
+        state_val = {s: np.mean(r) for s, r in state_rwd.items()}
+
+        self.mdp = {
+            'states': sorted(list(states)),
+            'num_states': len(states),
+            'transitions': trans_prob,
+            'state_values': state_val,
+            'state_rewards': state_rwd,
+            'trans_counts': trans_cnt,
+        }
+        return self.mdp
+
+    def transition_prob(self, s: str, s_next: str) -> float:
+        if not self.mdp: raise ValueError("build MDP first")
+        return self.mdp['transitions'].get(s, {}).get(s_next, 0.0)
+
+    def state_value(self, s: str) -> float:
+        if not self.mdp: raise ValueError("build MDP first")
+        return self.mdp['state_values'].get(s, 0.0)
+
+    def sample_traj(self, start: str, max_len: int = 50) -> List[str]:
+        if not self.mdp: raise ValueError("build MDP first")
+        path = [start]
+        curr = start
+        for _ in range(max_len):
+            nxt = self.mdp['transitions'].get(curr, {})
+            if not nxt: break
+            curr = np.random.choice(list(nxt.keys()), p=list(nxt.values()))
+            path.append(curr)
+        return path
+
+def visualize_mdp(model: BehaviorModel, threshold: float = 0.05, output: str = "mdp_graph", fmt: str = "svg", view: bool = False, export_dot: bool = False):
+    """visualize MDP as directed graph using graphviz, aggregated by event type"""
+    if not model.mdp: raise ValueError("build MDP first")
+
+    # aggregate transitions by event type
+    evt_trans = defaultdict(lambda: defaultdict(float))
+    for s, trans in model.mdp['transitions'].items():
+        evt_src = s.split('|')[2]
+        for s_next, prob in trans.items():
+            evt_dst = s_next.split('|')[2]
+            evt_trans[evt_src][evt_dst] += prob
+
+    # normalize aggregated transitions
+    for evt_src in evt_trans:
+        total = sum(evt_trans[evt_src].values())
+        if total > 0:
+            for evt_dst in evt_trans[evt_src]:
+                evt_trans[evt_src][evt_dst] /= total
+
+    g = graphviz.Digraph(format=fmt)
+    g.attr(rankdir='LR', size='30')
+    g.attr('node', shape='circle', width='1', height='1')
+
+    # collect all event types
+    events = set(evt_trans.keys())
+    for trans in evt_trans.values():
+        events.update(trans.keys())
+
+    # add nodes for each event type
+    for evt in events:
+        g.node(evt)
+
+    # add edges above threshold
+    for evt_src in evt_trans:
+        for evt_dst, prob in evt_trans[evt_src].items():
+            if prob > threshold:
+                g.edge(evt_src, evt_dst, label=f'{prob:.2f}')
+
+    g.render(output, view=view, cleanup=True)
+    print(f"Saved MDP graph to {output}.{fmt}")
+
+    if export_dot:
+        dot_file = f"{output}.dot"
+        with open(dot_file, 'w') as f:
+            f.write(g.source)
+        print(f"Exported DOT source to {dot_file}")
+
+    return g
+
+if __name__ == "__main__":
+    model = BehaviorModel(DIR)
+    mdp = model.build_MDP()
+    print(f"Built MDP: {mdp['num_states']} states, {sum(len(t) for t in mdp['transitions'].values())} transitions")
+    if not mdp['states']:
+        print("No states found")
+        exit(1)
+    visualize_mdp(model, threshold=0.05, output="mdp_viz", fmt="pdf", export_dot=True)

sim/rl/engine.py

@@ -0,0 +1,227 @@
+from os import kill
+import numpy as np
+import pandas as pd
+from abc import ABC, abstractmethod
+from typing import Dict, Any
+from environment import BusinessLogicConstraints
+
+
+"""
+An angine by default should have its own demand estimation mechanism from the observed observations whihc are the computer feature.
+From these features we then follow the researc hstructure of q -> p with a testable and must be updatable mechanism.
+"""
+
+class BasePricingEngine(ABC):
+    """base interface for all pricing engines"""
+    def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
+        self.c = constraints
+        self.rng = np.random.default_rng(seed)
+        self.step_count = 0
+
+
+    @abstractmethod
+    def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
+        """compute new prices given current state and observation from environment
+
+        args:
+            current_prices: current price vector [N]
+            observation: dict containing 'price', 'demand', and possibly interaction data
+
+        returns:
+            new_prices: updated price vector [N]
+        """
+        pass
+
+    @abstractmethod
+    def update(obs, reward, done, info):
+        pass
+
+
+
+    def reset(self):
+        """reset engine state for new episode"""
+        self.step_count = 0
+
+
+class WildPricingEngine(BasePricingEngine):
+    """production-like pricing using online elasticity estimation via EWMA regression"""
+    def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
+        super().__init__(constraints, seed)
+        # per-product unit costs (unknown to customers; known to platform)
+        self.unit_cost = self.rng.uniform(8.0, 40.0, size=self.c.product_catelogue_size).astype(np.float32)
+        # online elasticity estimate (start moderately elastic)
+        self.e_hat = np.full((self.c.product_catelogue_size,), -1.3, dtype=np.float32)
+        # EWMA state for log-log regression
+        self.mu_logp = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        self.mu_logq = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        self.cov_pq  = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        self.var_p   = np.ones(self.c.product_catelogue_size, dtype=np.float32)
+        # knobs typical in production
+        self.lr = 0.08
+        self.ewma = 0.05
+        self.eps_explore = 0.03
+        self.explore_scale = 0.03
+
+    def _safe_elasticity(self, e: np.ndarray) -> np.ndarray:
+        return np.clip(e, -5.0, -1.05)
+
+    def reset(self):
+        super().reset()
+        self.e_hat = np.full((self.c.product_catelogue_size,), -1.3, dtype=np.float32)
+        self.mu_logp = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        self.mu_logq = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        self.cov_pq = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        self.var_p = np.ones(self.c.product_catelogue_size, dtype=np.float32)
+
+    def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
+        self.step_count += 1
+        # extract demand signal (from env observation) as proxy for sales
+        demand = observation.get('demand', np.zeros(self.c.product_catelogue_size, dtype=np.float32))
+        return self._update_from_demand(current_prices, demand)
+
+    def _update_from_demand(self, prices: np.ndarray, sold: np.ndarray) -> np.ndarray:
+        # log transforms (add 1 to handle zeros)
+        logp = np.log(np.clip(prices, 1e-3, None)).astype(np.float32)
+        logq = np.log(sold + 1.0).astype(np.float32)
+        # EWMA moments for per-product regression: logq ≈ a + e*logp
+        a = self.ewma
+        dp = logp - self.mu_logp
+        dq = logq - self.mu_logq
+        self.mu_logp = (1 - a) * self.mu_logp + a * logp
+        self.mu_logq = (1 - a) * self.mu_logq + a * logq
+        self.cov_pq = (1 - a) * self.cov_pq + a * (dp * dq)
+        self.var_p = (1 - a) * self.var_p + a * (dp * dp + 1e-6)
+        e_new = self.cov_pq / (self.var_p + 1e-6)
+        self.e_hat = self._safe_elasticity(0.9 * self.e_hat + 0.1 * e_new)
+        # profit-optimal price for isoelastic demand (if e < -1)
+        e = self.e_hat
+        p_star = self.unit_cost * (e / (e + 1.0))
+        # smooth toward p_star
+        new_prices = (1 - self.lr) * prices + self.lr * p_star
+        # exploration (small random perturbations)
+        if self.rng.random() < self.eps_explore:
+            noise = self.rng.normal(0.0, self.explore_scale, size=new_prices.shape).astype(np.float32)
+            new_prices = new_prices * (1.0 + noise)
+        # apply business guardrails (max change + bounds)
+        max_adj = self.c.max_price_adjustment
+        ratio = np.clip(new_prices / (prices + 1e-6), 1 - max_adj, 1 + max_adj)
+        new_prices = prices * ratio
+        new_prices = np.clip(new_prices, self.c.system_min_price, self.c.system_max_price).astype(np.float32)
+        return new_prices
+
+
+class StaticPricingEngine(BasePricingEngine):
+    """baseline: fixed prices throughout episode"""
+    def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
+        super().__init__(constraints, seed)
+        self.fixed_prices = None
+
+    def reset(self):
+        super().reset()
+        self.fixed_prices = None
+
+    def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
+        self.step_count += 1
+        if self.fixed_prices is None:
+            self.fixed_prices = current_prices.copy()
+        return self.fixed_prices.copy()
+
+
+class SimpleDemandEngine(BasePricingEngine):
+    """demand-driven pricing: increase price when demand rises, decrease when it falls"""
+    def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
+        super().__init__(constraints, seed)
+        self.prev_demand = None
+        self.lr = 0.05
+
+    def reset(self):
+        super().reset()
+        self.prev_demand = None
+
+    def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
+        self.step_count += 1
+        demand = observation.get('demand', np.zeros(self.c.product_catelogue_size, dtype=np.float32))
+        if self.prev_demand is None:
+            self.prev_demand = demand.copy()
+            return current_prices.copy()
+        # simple rule: if demand increases, raise price; if decreases, lower price
+        delta_d = demand - self.prev_demand
+        price_adj = self.lr * np.sign(delta_d) * np.abs(delta_d) / (np.abs(self.prev_demand) + 1.0)
+        new_prices = current_prices * (1.0 + price_adj)
+        self.prev_demand = demand.copy()
+        # apply constraints
+        max_adj = self.c.max_price_adjustment
+        ratio = np.clip(new_prices / (current_prices + 1e-6), 1 - max_adj, 1 + max_adj)
+        new_prices = current_prices * ratio
+        return np.clip(new_prices, self.c.system_min_price, self.c.system_max_price).astype(np.float32)
+
+
+class RandomWalkEngine(BasePricingEngine):
+    """random walk pricing with mean reversion"""
+    def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
+        super().__init__(constraints, seed)
+        self.target_price = None
+        self.volatility = 0.02
+
+    def reset(self):
+        super().reset()
+        self.target_price = None
+
+    def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
+        self.step_count += 1
+        if self.target_price is None:
+            self.target_price = current_prices.copy()
+        # random walk with mean reversion toward target
+        noise = self.rng.normal(0.0, self.volatility, size=current_prices.shape).astype(np.float32)
+        reversion = 0.01 * (self.target_price - current_prices)
+        new_prices = current_prices * (1.0 + noise) + reversion
+        # apply constraints
+        max_adj = self.c.max_price_adjustment
+        ratio = np.clip(new_prices / (current_prices + 1e-6), 1 - max_adj, 1 + max_adj)
+        new_prices = current_prices * ratio
+        return np.clip(new_prices, self.c.system_min_price, self.c.system_max_price).astype(np.float32)
+
+
+class ThompsonSamplingEngine(BasePricingEngine):
+    """bayesian bandit approach per product treating price as discrete action"""
+    def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
+        super().__init__(constraints, seed)
+        self.n_price_levels = 5
+        self.alpha = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
+        self.beta = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
+        self.price_grid = None
+        self.last_actions = None
+
+    def reset(self):
+        super().reset()
+        self.alpha = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
+        self.beta = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
+        self.price_grid = None
+        self.last_actions = None
+
+    def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
+        self.step_count += 1
+        if self.price_grid is None:
+            # define price grid per product
+            lo = current_prices * 0.7
+            hi = current_prices * 1.3
+            self.price_grid = np.linspace(lo, hi, self.n_price_levels).T
+        demand = observation.get('demand', np.zeros(self.c.product_catelogue_size, dtype=np.float32))
+        # update beliefs based on last action
+        if self.last_actions is not None:
+            for i in range(self.c.product_catelogue_size):
+                a = self.last_actions[i]
+                reward = demand[i]
+                if reward > 0.5:
+                    self.alpha[i, a] += reward
+                else:
+                    self.beta[i, a] += 1.0
+        # thompson sampling: sample from posterior, pick best
+        new_prices = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
+        actions = np.zeros(self.c.product_catelogue_size, dtype=int)
+        for i in range(self.c.product_catelogue_size):
+            theta = self.rng.beta(self.alpha[i], self.beta[i]).astype(np.float32)
+            actions[i] = int(np.argmax(theta))
+            new_prices[i] = self.price_grid[i, actions[i]]
+        self.last_actions = actions
+        return np.clip(new_prices, self.c.system_min_price, self.c.system_max_price).astype(np.float32)

sim/rl/environment.py

@@ -0,0 +1,320 @@
+from sys import intern
+import gymnasium as gym
+from gymnasium import spaces
+from matplotlib import interactive
+import numpy as np
+from dataclasses import dataclass
+import pandas as pd
+from typing import Callable, Optional, Dict, Any, List
+
+# "learner"  agent learning to optimize pricing
+# "agent"  part of environment creating demand signals that learner processes
+
+@dataclass
+class BusinessLogicConstraints():
+    max_price_adjustment: float = 0.30
+    system_max_price: float = 500.0
+    system_min_price: float = 1.0
+    product_catelogue_size: int = 100
+    episode_length: int = 200
+    sessions_per_step: int = 250
+    agent_share: float = 0.25
+    agent_recon_multiplier: float = 6.0
+    agent_purchase_probability: float = 0.20
+    coi_strength: float = 0.25
+    coi_threshold: float = 4.0
+    coi_sigmoid_temp: float = 1.25
+    base_human_demand: float = 0.08
+    base_agent_demand: float = 0.05
+    human_price_elasticity: float = -1.2 # assumptions here
+    agent_price_elasticity: float = -0.6
+    w_agent_loss: float = 1.0
+    w_volatility: float = 5.0
+    w_estimation_error: float = 0.25
+    seed: int = 7
+
+
+def _sigmoid(x: np.ndarray) -> np.ndarray:
+    return 1.0 / (1.0 + np.exp(-x))
+
+class CommercePlatform:
+    """
+    This is just an extension of the state management for the environment, it does not implement anything dynamic just helps us simulate demand.
+    """
+    def __init__(self,
+                 product_catelogue_size: int,
+                 max_price: float,
+                 min_price: float,
+                 constraints: BusinessLogicConstraints):
+        self.product_catelogue_size = product_catelogue_size
+        self.product_supply = np.random.uniform(low=10, high=50, size=(self.product_catelogue_size,))
+        self.max_price = max_price
+        self.min_price = min_price
+        self.constraints = constraints
+        self.simulation_history: List[Dict[str, Any]] = []
+        self._rng = np.random.default_rng(constraints.seed)
+        self._last_interaction_df: pd.DataFrame = pd.DataFrame()
+
+
+    def setup_true_demand(self, prices: np.ndarray) -> Dict[str, np.ndarray]:
+        # ground truth purchase propensities
+        p = np.clip(prices, self.min_price, self.max_price)
+        pn = p / self.max_price
+        human_prob = self.constraints.base_human_demand * (pn ** self.constraints.human_price_elasticity)
+        agent_prob = self.constraints.base_agent_demand * (pn ** self.constraints.agent_price_elasticity)
+        return {
+            "human_purchase_prob": np.clip(human_prob, 0.0, 0.95),
+            "agent_purchase_prob": np.clip(agent_prob, 0.0, 0.95)
+        }
+
+    def _load_behavioral_profile(actor : str, demand_forcing):
+        """
+        This returns a markov chain with average weights which we get from interaction data of our experiments.
+        This defines transition probabilities between different events:
+        search -> view_item_price_binN: 0.7
+        view_item_price_binN -> add_to_cart: 0.2
+        we also must reweight with the demand_forcing vector or purchase probabilities per-product
+        """
+
+
+    def _simulate_sessions(self, base_prices: np.ndarray) -> pd.DataFrame:
+        demand = self.setup_true_demand(base_prices)
+        human_pprob = demand["human_purchase_prob"]
+        agent_pprob = demand["agent_purchase_prob"]
+        events: List[Dict[str, Any]] = []
+        T = self.constraints.sessions_per_step
+        n_agent_sessions = int(round(T * self.constraints.agent_share))
+        n_human_sessions = T - n_agent_sessions
+        n_agent_ids = max(1, n_agent_sessions // 2)
+        session_map = {
+            'humans': n_human_sessions,
+            'agents': n_agent_ids
+        }
+        pprob_map = {
+            'humans': human_pprob,
+            'agents': agent_pprob
+        }
+        joint_events = []
+        for actor, n_sessions in session_map.items():
+            bp = _load_behavioral_profile(actor, pprob_map[actor])
+            counter = 0
+            events = []
+            while counter < n_sessions:
+                session_events = []
+                while len(session_events) == 0 or session_events[-1]['action'] == 'checkout':
+                    interaction_event = bp.sample(self._rng)
+                    interaction_event['session_id'] = f'{actor}_{counter:06d}'
+                    # TODO any other assignments
+                    session_events.append(interaction_event)
+                events.extend(session_events)
+                counter += 1
+            joint_events.extend(events)
+
+        return pd.DataFrame(joint_events)
+
+    def compute_interaction_features(self, interaction_df: pd.DataFrame) -> Dict[str, float]:
+        if interaction_df.empty:
+            return {"mean_sale_price": 0.0, "look_to_book": 0.0}
+        purchases = interaction_df[interaction_df["action"] == "purchase"]
+        mean_sale_price = float(purchases["price_paid"].mean()) if not purchases.empty else 0.0
+        views = float((interaction_df["action"] == "view").sum())
+        buys = float((interaction_df["action"] == "purchase").sum())
+        return {"mean_sale_price": mean_sale_price, "look_to_book": float(views / (buys + 1e-6))}
+
+    def _session_feature_table(self, df: pd.DataFrame) -> pd.DataFrame:
+        # TODO: adapt this
+        if df.empty:
+            return pd.DataFrame()
+        g = df.groupby("session_id", sort=False)
+        session_duration = g["t"].max() - g["t"].min()
+        total_interactions = g.size()
+        avg_time_between = g["t"].apply(lambda x: float(np.diff(np.sort(x.to_numpy())).mean()) if len(x) > 1 else 0.0)
+        interaction_velocity = total_interactions / (session_duration + 1e-6)
+        views = g.apply(lambda x: int((x["action"] == "view").sum()), include_groups=False)
+        cart_adds = g.apply(lambda x: int((x["action"] == "cart").sum()), include_groups=False)
+        purchases = g.apply(lambda x: int((x["action"] == "purchase").sum()), include_groups=False)
+        conversion_rate = purchases / (views + 1e-6)
+        is_agent = g["actor"].apply(lambda s: bool((s == "agent").any()), include_groups=False)
+
+        return pd.DataFrame({
+            "session_duration_sec": session_duration.astype(float),
+            "avg_time_between_events": avg_time_between.astype(float),
+            "total_interactions": total_interactions.astype(int),
+            "interaction_velocity": interaction_velocity.astype(float),
+            "item_views": views.astype(int),
+            "cart_adds": cart_adds.astype(int),
+            "purchases": purchases.astype(int),
+            "conversion_rate": conversion_rate.astype(float),
+            "is_agent": is_agent.astype(bool),
+        }).reset_index()
+
+    def get_interaction_data(self) -> np.ndarray:
+        if self._last_interaction_df.empty:
+            return np.array([], dtype=object)
+        return self._last_interaction_df.to_dict(orient="records")
+
+
+class PHANTOMEnv(gym.Env):
+    metadata = {"render_modes": []}
+
+    def __init__(self, constraints):
+        super().__init__()
+        self.constraints = BusinessLogicConstraints()
+        self.action_space = spaces.Box(low=-self.constraints.max_price_adjustment,
+                                       high=self.constraints.max_price_adjustment,
+                                       shape=(self.constraints.product_catelogue_size,), dtype=np.float32)
+        self.observation_space = spaces.Dict({
+            "elasticity": spaces.Dict({
+                "price": spaces.Box(
+                    low=np.full((self.constraints.product_catelogue_size,), self.constraints.system_min_price, dtype=np.float32),
+                    high=np.full((self.constraints.product_catelogue_size,), self.constraints.system_max_price, dtype=np.float32),
+                    dtype=np.float32),
+                "demand": spaces.Box(
+                    low=np.zeros((self.constraints.product_catelogue_size,), dtype=np.float32),
+                    high=np.full((self.constraints.product_catelogue_size,), 1e6, dtype=np.float32),
+                    dtype=np.float32),
+            })
+            # TODO: define more features that we compute from the interaction data
+        })
+        self.commerce_platform = CommercePlatform(
+            product_catelogue_size=self.constraints.product_catelogue_size,
+            max_price=self.constraints.system_max_price,
+            min_price=self.constraints.system_min_price,
+            constraints=self.constraints)
+        self._rng = np.random.default_rng(self.constraints.seed)
+        self.t = 0
+        self._prev_prices: Optional[np.ndarray] = None
+        self.state: Dict[str, Any] = {}
+
+    def reset(self, seed: Optional[int] = None, options: Optional[dict] = None):
+        super().reset(seed=seed)
+        if seed is not None:
+            self._rng = np.random.default_rng(seed)
+            self.commerce_platform._rng = np.random.default_rng(seed)
+        self.t = 0
+        init_prices = self._rng.uniform(low=60.0, high=140.0, size=(self.constraints.product_catelogue_size,)).astype(np.float32)
+        self._prev_prices = init_prices.copy()
+        self.state = {
+            "elasticity": {
+                "price": init_prices,
+                "demand": np.zeros((self.constraints.product_catelogue_size,), dtype=np.float32),
+            }
+        }
+        return self.state, {}
+
+    def step(self, action: np.ndarray):
+        self.t += 1
+        base_prices = self.state["elasticity"]["price"].astype(np.float32)
+        new_prices = np.clip(base_prices * (1.0 + action.astype(np.float32)),
+                           self.constraints.system_min_price,
+                           self.constraints.system_max_price).astype(np.float32)
+
+        self.state["elasticity"]["price"] = new_prices
+        # TODO: use the commerce platform to simulate sessions
+        interactions_df = self.commerce_platform._simulate_sessions(new_prices)
+        result = self.commerce_platform.compute_interaction_features(interactions_df)
+        # TODO: implement COI computation to use in reward
+        COI = 0.0
+
+        volatility = 0.0 if self._prev_prices is None else \
+            float(np.mean(np.abs((new_prices - self._prev_prices) / (self._prev_prices + 1e-6))))
+        self._prev_prices = new_prices.copy()
+
+        revenue_observed = float(result["revenue_observed"])
+        agent_loss = float(result["agent_loss"])
+
+        reward = (revenue_observed
+                  - COI
+                  - self.constraints.w_agent_loss * agent_loss
+                  - self.constraints.w_volatility * volatility
+                  - self.constraints.w_estimation_error
+                  )
+
+        terminated = self.t >= self.constraints.episode_length
+        info = {
+            "t": self.t,
+            "revenue_observed": revenue_observed,
+            "revenue_oracle": float(result["revenue_oracle"]),
+            "agent_loss": agent_loss,
+            "ux_volatility": volatility,
+            "mean_internal_error": err_mean,
+            "look_to_book": float(result["interaction_features"].get("look_to_book", 0.0)),
+            "mean_sale_price": float(result["interaction_features"].get("mean_sale_price", 0.0)),
+            "true_human_purchases_total": float(np.sum(result["true_human_demand"])),
+            "true_agent_purchases_total": float(np.sum(result["true_agent_purchases"])),
+        }
+        return self.state, float(reward), terminated, False, info
+
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+    from collections import defaultdict
+
+    runs = {}
+    for use_defense in (False, True):
+        env = PHANTOMEnv(use_defense=use_defense)
+        obs, _ = env.reset(seed=42)
+        metrics = defaultdict(list)
+        total_reward = 0.0
+        done = False
+
+        while not done:
+            action = env.action_space.sample()
+            obs, reward, done, _, info = env.step(action)
+            total_reward += reward
+            p_mean = float(np.mean(obs["elasticity"]["price"]))
+            q_mean = float(np.mean(obs["elasticity"]["demand"]))
+            p_std = float(np.std(obs["elasticity"]["price"]))
+
+            metrics['t'].append(info['t'])
+            metrics['price_mean'].append(p_mean)
+            metrics['price_std'].append(p_std)
+            metrics['demand_mean'].append(q_mean)
+            metrics['revenue_observed'].append(info['revenue_observed'])
+            metrics['revenue_oracle'].append(info['revenue_oracle'])
+            metrics['agent_loss'].append(info['agent_loss'])
+            metrics['ux_volatility'].append(info['ux_volatility'])
+            metrics['look_to_book'].append(info['look_to_book'])
+            metrics['reward'].append(reward)
+            metrics['human_purchases'].append(info['true_human_purchases_total'])
+            metrics['agent_purchases'].append(info['true_agent_purchases_total'])
+
+            if info['t'] % 20 == 0 or done:
+                print(f"defense={'ON ' if use_defense else 'OFF'} t={info['t']:03d} p={p_mean:6.2f}±{p_std:4.2f} "
+                      f"q={q_mean:6.2f} rev={info['revenue_observed']:7.2f} oracle={info['revenue_oracle']:7.2f} "
+                      f"loss={info['agent_loss']:6.2f} ux={info['ux_volatility']:.3f} "
+                      f"ltb={info['look_to_book']:5.2f} r={reward:7.2f}")
+
+        runs[use_defense] = metrics
+        print(f"defense={'ON ' if use_defense else 'OFF'} total_reward={total_reward:.2f}\n")
+
+    fig, axes = plt.subplots(3, 3, figsize=(15, 12))
+    fig.suptitle('PHANTOM Environment: Defense OFF vs ON', fontsize=14, fontweight='bold')
+
+    plot_configs = [
+        ('price_mean', 'Mean Price', 'Price'),
+        ('demand_mean', 'Mean Demand Estimate', 'Demand'),
+        ('revenue_observed', 'Revenue (Observed)', 'Revenue'),
+        ('agent_loss', 'Agent Loss (Oracle - Observed)', 'Loss'),
+        ('ux_volatility', 'UX Volatility (Price Change)', 'Volatility'),
+        ('look_to_book', 'Look-to-Book Ratio', 'Ratio'),
+        ('reward', 'Step Reward', 'Reward'),
+        ('human_purchases', 'Human Purchases', 'Count'),
+        ('agent_purchases', 'Agent Purchases', 'Count'),
+    ]
+
+    for idx, (key, title, ylabel) in enumerate(plot_configs):
+        ax = axes[idx // 3, idx % 3]
+        for use_defense, label, color in [(False, 'No Defense', 'red'), (True, 'With Defense', 'blue')]:
+            m = runs[use_defense]
+            ax.plot(m['t'], m[key], label=label, color=color, alpha=0.7, linewidth=1.5)
+        ax.set_xlabel('Step')
+        ax.set_ylabel(ylabel)
+        ax.set_title(title, fontsize=10, fontweight='bold')
+        ax.legend(loc='best', fontsize=8)
+        ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig('phantom_env_comparison.png', dpi=150, bbox_inches='tight')
+    print("Plot saved to phantom_env_comparison.png")
+    plt.show()

sim/rl/train.py

@@ -0,0 +1,149 @@
+import numpy as np
+import logging
+from pathlib import Path
+from typing import Dict, Type, Optional
+import pickle
+from torch import neg_
+from torch.utils.tensorboard import SummaryWriter
+from environment import PHANTOMEnv, FastTrainingConstraints, BusinessLogicConstraints
+from engine import (BasePricingEngine, WildPricingEngine, StaticPricingEngine,
+                   SimpleDemandEngine, RandomWalkEngine, ThompsonSamplingEngine)
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
+logger = logging.getLogger(__name__)
+
+
+
+"""
+Target training loop:
+have base prices p0 from env reset and run the env step, collect reward and metrics
+pass this to the pricing engine which computes the price action to take based on previous reward by learning
+the new action gets passed to the step
+so we alternate, step -> reward -> engine (produces price delta) -> step with price delta -> reward
+to make sure the reinforcement learning inside the engine can learn we need to have trajectory of prices
+CURRENT SOLUTION BELOW does not implement correct learning or updates.
+"""
+
+class EngineTrainer:
+    """wrapper to run pricing engines through episodes and collect metrics"""
+    def __init__(self, engine: BasePricingEngine, env: PHANTOMEnv,
+                 tb_writer: Optional[SummaryWriter] = None):
+        self.engine = engine
+        self.env = env
+        self.episode_metrics = []
+        self.tb_writer = tb_writer
+        self.global_step = 0
+
+    def train(self, n_episodes: int, seed: int = 42):
+
+        obs, _ = self.env.reset(seed=seed)
+        prices = None
+        for ep in range(n_episodes):
+            prices = self.engine.compute_prices(prices, obs)
+            obs, reward, done, _, info = self.env.step(prices)
+            self.engine.update(obs, reward, done, info)
+        return self
+
+
+
+
+
+
+        return self.episode_metrics
+
+    def evaluate(self, n_episodes: int = 10, seed: int = 100) -> Dict:
+        """evaluate trained engine"""
+        results = {k: [] for k in ['total_reward', 'revenue_observed', 'revenue_oracle',
+                                   'agent_loss', 'ux_volatility', 'look_to_book']}
+        for ep in range(n_episodes):
+            metrics = self.run_episode(seed=seed + ep)
+            for k in results:                results[k].append(metrics[k])
+        return {k: (np.mean(v), np.std(v)) for k, v in results.items()}
+
+
+def make_env(fast: bool = True):
+    constraints = FastTrainingConstraints() if fast else BusinessLogicConstraints()
+    return PHANTOMEnv(constraints=constraints)
+
+
+def train_engine(engine_cls: Type[BasePricingEngine], env: PHANTOMEnv,
+                n_episodes: int, seed: int = 42,
+                tb_writer: Optional[SummaryWriter] = None) -> EngineTrainer:
+    constraints = env.constraints
+    engine = engine_cls(constraints=constraints, seed=seed)
+    trainer = EngineTrainer(engine, env, tb_writer=tb_writer)
+    trainer.train(n_episodes, seed=seed)
+    return trainer
+
+
+def save_trainer(trainer: EngineTrainer, path: Path):
+    """save engine state and metrics"""
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with open(path, 'wb') as f:
+        pickle.dump({
+            'engine': trainer.engine,
+            'metrics': trainer.episode_metrics
+        }, f)
+    logger.info(f"Saved trainer to {path}")
+
+
+def load_trainer(path: Path, env: PHANTOMEnv,
+                 tb_writer: Optional[SummaryWriter] = None) -> EngineTrainer:
+    """load saved engine"""
+    with open(path, 'rb') as f:
+        data = pickle.load(f)
+    trainer = EngineTrainer(data['engine'], env, tb_writer=tb_writer)
+    trainer.episode_metrics = data['metrics']
+    return trainer
+
+
+if __name__ == "__main__":
+    base_dir = Path("./runs")
+    base_dir.mkdir(exist_ok=True)
+
+    engines = {
+        "Wild": WildPricingEngine,
+        "Static": StaticPricingEngine,
+#        "SimpleDemand": SimpleDemandEngine,
+        "RandomWalk": RandomWalkEngine,
+        "ThompsonSampling": ThompsonSamplingEngine,
+    }
+    defenses = [False, True]
+    n_train_episodes = 50
+    n_eval_episodes = 10
+    seed = 42
+    fast_mode = True
+
+    logger.info(f"Training config: {n_train_episodes} episodes per engine, fast_mode={fast_mode}")
+
+    trained_trainers = {}
+
+    for engine_name, engine_cls in engines.items():
+        for use_defense in defenses:
+            defense_label = "defense_on" if use_defense else "defense_off"
+            run_name = f"{engine_name}_{defense_label}"
+            log_dir = base_dir / run_name
+            log_dir.mkdir(parents=True, exist_ok=True)
+
+            logger.info(f"Training {engine_name} with defense={use_defense}")
+            logger.info(f"Log directory: {log_dir}")
+
+            env = make_env(fast=fast_mode)
+            tb_writer = SummaryWriter(log_dir=str(log_dir))
+            trainer = train_engine(engine_cls, env, n_train_episodes, seed, tb_writer=tb_writer)
+            tb_writer.close()
+
+            save_path = log_dir / "trainer.pkl"
+            save_trainer(trainer, save_path)
+
+            trained_trainers[run_name] = (trainer, env)
+
+    logger.info("Starting evaluation")
+
+    for run_name, (trainer, env) in trained_trainers.items():
+        logger.info(f"Evaluating {run_name}")
+        results = trainer.evaluate(n_episodes=n_eval_episodes, seed=seed + 1000)
+        for metric, (mean, std) in results.items():
+            logger.info(f"  {metric:20s}: {mean:10.2f} ± {std:6.2f}")
+
+    logger.info(f"Results saved to: {base_dir}")

tests/e2e/helpers/kafka.ts

@@ -9,8 +9,8 @@ interface InteractionEvent {
 const dumpKafkaTopic = async (backendUrl: string, topic: string) => {
   const resp = await fetch(`${backendUrl}/api/kafka/dump?topic=${topic}`);
   if (!resp.ok) throw new Error(`Kafka dump failed: ${resp.status}`);
-  const { messages = [] } = await resp.json();
+  const { data = [] } = await resp.json();
-  return messages as any[];
+  return data as any[];
 };
 
 export const waitForInteractionEvent = async (

tests/e2e/playwright.config.ts

@@ -5,14 +5,14 @@ export default defineConfig({
   fullyParallel: true,
   forbidOnly: !!process.env.CI,
   retries: 0,
-  workers: 5,
+  workers: 1,
   reporter: 'list',
   use: {
     baseURL: process.env.WEB_URL || 'http://localhost:3000',
     trace: 'retain-on-failure',
     screenshot: 'only-on-failure',
   },
-  timeout: 60000,
+  timeout: 180000,
   expect: {
     timeout: 10000,
   },

tests/e2e/scenarios/session-aware.spec.ts

@@ -9,6 +9,7 @@ import {
   addToCart,
 } from '../helpers/interactions';
 import { getSessionEvents } from '../helpers/kafka';
+import { runSessionPricing } from '../helpers/airflow';
 
 test.describe('SessionAwarePricer E2E', () => {
   const STORE_TYPE = 'hotel';
@@ -23,6 +24,9 @@ test.describe('SessionAwarePricer E2E', () => {
     await page.waitForTimeout(1500);
 
     const productId2 = await humanLikeViewProduct(page, STORE_TYPE);
+
+    await runSessionPricing(STORE_TYPE);
+
     const secondPrice = await getPriceFromDOM(page);
     expect(await verifySessionConsistency(page, sessionId)).toBeTruthy();
 
@@ -40,11 +44,13 @@ test.describe('SessionAwarePricer E2E', () => {
     await rapidViewProductViaFlow(page, 8, 100, STORE_TYPE);
     expect(await verifySessionConsistency(page, sessionId)).toBeTruthy();
 
-    await page.waitForTimeout(2500);
+    await page.waitForTimeout(1000);
 
     const events = await getSessionEvents(backendUrl, sessionId);
     expect(events.length).toBeGreaterThanOrEqual(8);
 
+    await runSessionPricing(STORE_TYPE);
+
     await page.goto(`/products/${productId}`);
     await page.waitForLoadState('networkidle');
     const agentPrice = await getPriceFromDOM(page);
@@ -59,14 +65,12 @@ test.describe('SessionAwarePricer E2E', () => {
     const productId = await viewProductViaFlow(page, STORE_TYPE);
     const baselinePrice = await getPriceFromDOM(page);
 
-    const startTime = Date.now();
     await rapidViewProductViaFlow(page, 10, 80, STORE_TYPE);
-    const duration = (Date.now() - startTime) / 1000;
 
-    const eventsPerSec = 10 / duration;
+    const events = await getSessionEvents(backendUrl, sessionId);
-    expect(eventsPerSec).toBeGreaterThan(2.0);
+    expect(events.length).toBeGreaterThanOrEqual(10);
 
-    await page.waitForTimeout(2000);
+    await runSessionPricing(STORE_TYPE);
 
     await page.goto(`/products/${productId}`);
     await page.waitForLoadState('networkidle');
@@ -105,8 +109,11 @@ test.describe('SessionAwarePricer E2E', () => {
 
     await rapidViewProductViaFlow(page, 2, 150, STORE_TYPE);
 
-    await page.waitForTimeout(1500);
+    await page.waitForTimeout(1000);
     await humanLikeViewProduct(page, STORE_TYPE);
+
+    await runSessionPricing(STORE_TYPE);
+
     const finalPrice = await getPriceFromDOM(page);
 
     expect(Math.abs(finalPrice - baselinePrice) / baselinePrice).toBeLessThan(0.3);

tests/e2e/scenarios/surge-pricing.spec.ts

@@ -7,6 +7,7 @@ import {
   verifySessionConsistency,
 } from '../helpers/interactions';
 import { waitForInteractionEvent, countProductViews } from '../helpers/kafka';
+import { runSurgePricing } from '../helpers/airflow';
 
 test.describe('SimpleSurgePricer E2E', () => {
   const STORE_TYPE = 'hotel';
@@ -29,7 +30,7 @@ test.describe('SimpleSurgePricer E2E', () => {
 
     await rapidViewProductViaFlow(page, 5, 200, STORE_TYPE);
 
-    await page.waitForTimeout(2000);
+    await page.waitForTimeout(1000);
 
     const evt = await waitForInteractionEvent(backendUrl, sessionId, 'view_item_page');
     expect(evt).not.toBeNull();
@@ -37,6 +38,8 @@ test.describe('SimpleSurgePricer E2E', () => {
     const viewCount = await countProductViews(backendUrl, productId);
     expect(viewCount).toBeGreaterThanOrEqual(5);
 
+    await runSurgePricing(STORE_TYPE, 3, 1);
+
     await page.goto(`/products/${productId}`);
     await page.waitForLoadState('networkidle');
     const surgedPrice = await getPriceFromDOM(page);
@@ -72,7 +75,9 @@ test.describe('SimpleSurgePricer E2E', () => {
 
     await rapidViewProductViaFlow(page, 5, 150, STORE_TYPE);
 
-    await page.waitForTimeout(1500);
+    await page.waitForTimeout(1000);
+
+    await runSurgePricing(STORE_TYPE, 3, 1);
 
     await page.goto(`/products/${productId}`);
     await page.waitForLoadState('networkidle');
@@ -81,6 +86,8 @@ test.describe('SimpleSurgePricer E2E', () => {
 
     await page.waitForTimeout(12000);
 
+    await runSurgePricing(STORE_TYPE, 3, 1);
+
     await page.goto(`/products/${productId}`);
     await page.waitForLoadState('networkidle');
     const decayedPrice = await getPriceFromDOM(page);

web/src/app/api/pricing/route.ts

@@ -30,6 +30,8 @@ export async function GET(req: NextRequest) {
     const providerUrl = process.env.PRICING_PROVIDER_URL || 'http://localhost:5001';
     try {
         const queryParams = new URLSearchParams();
+        // THIS is our entry point into the dynamic pricing where we reference the context of the sesion and experiment and ask for a price to assign to the trajectory which is expressed
+        // The whole pipeline gets triggered from here.
         if (sessionId) queryParams.append('sessionId', sessionId);
         if (experimentId) queryParams.append('experimentId', experimentId);
 
@@ -55,11 +57,11 @@ export async function GET(req: NextRequest) {
         price = Math.round(randomBase * 100) / 100;
     }
 
-    // log price to kafka for elasticity computation
+    // log price to kafka asynchronously (non-blocking)
     if (sessionId) {
         const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
-        try {
+        // fire and forget - don't await to avoid blocking response
-            await fetch(`${backendUrl}/api/kafka/price-log`, {
+        fetch(`${backendUrl}/api/kafka/price-log`, {
             method: 'POST',
             headers: { 'Content-Type': 'application/json' },
             body: JSON.stringify({
@@ -70,10 +72,11 @@ export async function GET(req: NextRequest) {
                 storeMode,
                 ts: timestamp,
             }),
-            });
+        }).catch(err => {
-        } catch (err) {
+            if (process.env.NODE_ENV === 'development') {
                 console.error('[price-log-error]', err);
             }
+        });
     }
 
     if (process.env.NODE_ENV === 'development') {

web/src/app/cart/page.tsx

@@ -32,7 +32,8 @@ export default function CartPage() {
                     {itemCount > 0 && (
                         <button
                             onClick={clearCart}
-                            className="text-sm text-red-600 hover:underline"
+                            className="text-sm hover:underline"
+                            style={{ color: 'var(--accent-warning)' }}
                         >
                             Clear cart
                         </button>
@@ -42,7 +43,7 @@ export default function CartPage() {
                 {itemCount === 0 ? (
                     <div className="text-center py-12">
                         <p className="text-gray-500 mb-4">Your cart is empty</p>
-                        <a href="/" className="text-blue-600 hover:underline">Browse our selection</a>
+                        <a href="/" className="hover:underline" style={{ color: 'var(--text-accent)' }}>Browse our selection</a>
                     </div>
                 ) : (
                     <>
@@ -54,15 +55,11 @@ export default function CartPage() {
                                 >
                                     <div className="flex-1">
                                         <div className="flex items-center gap-2 mb-1">
-                                            <span className="px-2 py-0.5 text-xs font-medium rounded bg-blue-100 text-blue-800">
-                                                {item.type}
-                                            </span>
                                             <h3 className="font-semibold">{item.name}</h3>
                                         </div>
 
                                         {item.type === 'hotel' && (
                                             <div className="text-sm text-gray-600">
-                                                <p>{String(item.metadata.roomType)}</p>
                                                 <p>{String(item.metadata.checkIn)} - {String(item.metadata.checkOut)}</p>
                                                 <p>{String(item.metadata.nights)} night{Number(item.metadata.nights) > 1 ? 's' : ''}</p>
                                             </div>
@@ -81,7 +78,8 @@ export default function CartPage() {
                                         <p className="text-xl font-bold mb-2">${item.price}</p>
                                         <button
                                             onClick={() => handleRemove(item.id, item.type)}
-                                            className="text-sm text-red-600 hover:underline"
+                                            className="text-sm hover:underline"
+                                            style={{ color: 'var(--accent-warning)' }}
                                         >
                                             Remove
                                         </button>
@@ -100,7 +98,7 @@ export default function CartPage() {
                                     dispatchInteraction('checkout_start', undefined, { total, itemCount });
                                     window.location.href = '/checkout';
                                 }}
-                                className="w-full py-3 bg-blue-600 hover:bg-blue-700 text-white rounded-lg font-medium transition-colors"
+                                className="btn-primary w-full"
                             >
                                 Proceed to Checkout
                             </button>

web/src/app/globals.css

@@ -8,6 +8,9 @@
   --bg-secondary: #f5f5f5;
   --text-primary: #333333;
   --text-secondary: #666666;
+  --accent-primary: #007aff;
+  --accent-primary-hover: #0051d5;
+  --accent-primary-light: #e6f2ff;
   --spacing-sm: 8px;
   --spacing-md: 16px;
   --spacing-lg: 32px;

web/src/app/layout.tsx

@@ -15,8 +15,8 @@ const geistMono = Geist_Mono({
 });
 
 export const metadata: Metadata = {
-  title: "Create Next App",
+  title: "Travel Booking Platform",
-  description: "Generated by create next app",
+  description: "Book flights and hotels with dynamic pricing",
 };
 
 export default function RootLayout({

web/src/components/feats/hotel/HotelCard.tsx

@@ -2,6 +2,7 @@
 
 import type { EventName } from '@/lib/events';
 import type { Hotel } from '@/lib/hotel-utils';
+import { getHotelImageUrl } from '@/lib/hotel-utils';
 import { useHoverTracking } from '@/hooks/useHoverTracking';
 import PriceDisplay from '@/components/ui/PriceDisplay';
 
@@ -47,8 +48,6 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
         window.location.href = `/hotel/products/${hotel.id}`;
     };
 
-    const imageUrl = `https://images.unsplash.com/photo-1551882547-ff40c63fe5fa?w=400&h=300&fit=crop`;
-
     return (
         <div
             className="hotel-card cursor-pointer"
@@ -56,7 +55,7 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
         >
             <div className="hotel-image relative overflow-hidden">
                 <img
-                    src={imageUrl}
+                    src={getHotelImageUrl(hotel.id, { w: 400, h: 300 })}
                     alt={hotel.name}
                     className="w-full h-full object-cover"
                     onError={(e) => {

web/src/components/feats/hotel/HotelDetails.tsx

@@ -2,6 +2,7 @@
 
 import { useState, useEffect } from 'react';
 import type { Hotel } from '@/lib/hotel-utils';
+import { getHotelImageUrl } from '@/lib/hotel-utils';
 import PriceDisplay from '@/components/ui/PriceDisplay';
 
 interface HotelDetailsProps {
@@ -43,13 +44,11 @@ const PriceTotalDisplay = ({ productId, nights }: { productId: string; nights: n
 };
 
 export default function HotelDetails({ product, onAddToCart, addedToCart }: HotelDetailsProps) {
-  const imageUrl = `https://images.unsplash.com/photo-1566073771259-6a8506099945?w=800&h=600&fit=crop`;
-
   return (
     <div className="w-full flex flex-col lg:flex-row gap-12 py-8">
       <div className="w-full lg:w-1/2 rounded-lg aspect-[4/3] overflow-hidden shrink-0">
         <img
-          src={imageUrl}
+          src={getHotelImageUrl(product.id, { w: 800, h: 600 })}
           alt={product.name}
           className="w-full h-full object-cover"
           onError={(e) => {

web/src/components/ui/Navigation.tsx

@@ -20,7 +20,7 @@ const NavLink = ({ href, children }: { href: string; children: React.ReactNode }
       href={href}
       className={`px-4 py-2 rounded-md transition-colors ${
         isActive
-          ? 'bg-[var(--accent-primary)] font-semibold'
+          ? 'bg-[var(--accent-primary)] text-white font-semibold'
           : 'hover:bg-[var(--accent-primary-light)] text-[var(--text-primary)]'
       }`}
     >

web/src/lib/airline-utils.ts

@@ -31,7 +31,7 @@ export interface Flight {
   availability: number;
 }
 
-const EPOCH = new Date(0);
+import { dateToDaysFromToday, dateToIndex, todayIndex } from './date-utils';
 
 export const transformProduct = (p: AirlineProduct): Flight => {
   const { id, flight_type, date_index, metadata, availability } = p;
@@ -52,24 +52,4 @@ export const transformProduct = (p: AirlineProduct): Flight => {
   };
 };
 
-// convert date string to days from today
+export { dateToDaysFromToday, dateToIndex, todayIndex };
-export const dateToDaysFromToday = (dateStr: string): number => {
-  const target = new Date(dateStr);
-  target.setHours(0, 0, 0, 0);
-  const today = new Date();
-  today.setHours(0, 0, 0, 0);
-  return Math.floor((target.getTime() - today.getTime()) / 86400000);
-};
-
-// convert date string to date_index (days since epoch)
-export const dateToIndex = (dateStr: string): number => {
-  const d = new Date(dateStr);
-  return Math.floor((d.getTime() - EPOCH.getTime()) / 86400000);
-};
-
-// get current date_index
-export const todayIndex = (): number => {
-  const now = new Date();
-  now.setHours(0, 0, 0, 0);
-  return Math.floor((now.getTime() - EPOCH.getTime()) / 86400000);
-};

web/src/lib/date-utils.ts

@@ -0,0 +1,23 @@
+const EPOCH = new Date(0);
+const MS_PER_DAY = 86400000;
+
+export const dateToDaysFromToday = (dateStr: string): number => {
+  const target = new Date(dateStr);
+  target.setHours(0, 0, 0, 0);
+  const today = new Date();
+  today.setHours(0, 0, 0, 0);
+  return Math.floor((target.getTime() - today.getTime()) / MS_PER_DAY);
+};
+
+export const dateToIndex = (dateStr: string): number => {
+  const d = new Date(dateStr);
+  return Math.floor((d.getTime() - EPOCH.getTime()) / MS_PER_DAY);
+};
+
+export const todayIndex = (): number => {
+  const now = new Date();
+  now.setHours(0, 0, 0, 0);
+  return Math.floor((now.getTime() - EPOCH.getTime()) / MS_PER_DAY);
+};
+
+export { EPOCH, MS_PER_DAY };

web/src/lib/hotel-utils.ts

@@ -25,7 +25,7 @@ export interface Hotel {
   nights: number;
 }
 
-const EPOCH = new Date(0);
+import { EPOCH, MS_PER_DAY, dateToDaysFromToday, dateToIndex, todayIndex } from './date-utils';
 
 export const transformProduct = (p: HotelProduct): Hotel => {
   const { id, room_type, date_index, metadata } = p;
@@ -37,14 +37,14 @@ export const transformProduct = (p: HotelProduct): Hotel => {
     // legacy: treat as offset from today
     const today = new Date();
     today.setHours(0, 0, 0, 0);
-    checkIn = new Date(today.getTime() + date_index * 86400000);
+    checkIn = new Date(today.getTime() + date_index * MS_PER_DAY);
   } else {
     // proper: days since epoch
-    checkIn = new Date(EPOCH.getTime() + date_index * 86400000);
+    checkIn = new Date(EPOCH.getTime() + date_index * MS_PER_DAY);
   }
 
   const nights = 1;
-  const checkOut = new Date(checkIn.getTime() + nights * 86400000);
+  const checkOut = new Date(checkIn.getTime() + nights * MS_PER_DAY);
 
   const formatOpts: Intl.DateTimeFormatOptions = {
     month: 'short',
@@ -65,24 +65,34 @@ export const transformProduct = (p: HotelProduct): Hotel => {
   };
 };
 
-// convert date string to days from today
+const hotelImagePool = [
-export const dateToDaysFromToday = (dateStr: string): number => {
+  'photo-1566073771259-6a8506099945',
-  const target = new Date(dateStr);
+  'photo-1551882547-ff40c63fe5fa',
-  target.setHours(0, 0, 0, 0);
+  'photo-1590490360182-c33d57733427',
-  const today = new Date();
+  'photo-1582719478250-c89cae4dc85b',
-  today.setHours(0, 0, 0, 0);
+  'photo-1596701062351-8c2c14d1fdd0',
-  return Math.floor((target.getTime() - today.getTime()) / 86400000);
+  'photo-1631049307264-da0ec9d70304',
+  'photo-1578683010236-d716f9a3f461',
+  'photo-1540518614846-7eded433c457',
+  'photo-1505693416388-ac5ce068fe85',
+  'photo-1522771739844-6a9f6d5f14af',
+  'photo-1562438668-bcf0ca6578f0',
+  'photo-1595576508898-0ad5c879a061',
+];
+
+const hashString = (s: string): number => {
+  let h = 0;
+  for (let i = 0; i < s.length; i++) {
+    h = ((h << 5) - h) + s.charCodeAt(i);
+    h = h & h;
+  }
+  return Math.abs(h);
 };
 
-// convert date string to date_index (days since epoch)
+export const getHotelImageUrl = (hotelId: string, size: { w: number; h: number } = { w: 400, h: 300 }): string => {
-export const dateToIndex = (dateStr: string): number => {
+  const idx = hashString(hotelId) % hotelImagePool.length;
-  const d = new Date(dateStr);
+  const photoId = hotelImagePool[idx];
-  return Math.floor((d.getTime() - EPOCH.getTime()) / 86400000);
+  return `https://images.unsplash.com/${photoId}?w=${size.w}&h=${size.h}&fit=crop`;
 };
 
-// get current date_index
+export { dateToDaysFromToday, dateToIndex, todayIndex };
-export const todayIndex = (): number => {
-  const now = new Date();
-  now.setHours(0, 0, 0, 0);
-  return Math.floor((now.getTime() - EPOCH.getTime()) / 86400000);
-};