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https://github.com/velocitatem/PHANTOM.git
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2 Commits
first-pric
...
22-create-
| Author | SHA1 | Date | |
|---|---|---|---|
| 5469edfa98 | |||
| bf42fe2d60 |
@@ -11,7 +11,6 @@ from kafka import KafkaProducer, KafkaAdminClient, KafkaConsumer
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from kafka.admin import NewTopic
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from kafka.errors import TopicAlreadyExistsError
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from dotenv import load_dotenv
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from supabase import create_client, Client
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load_dotenv()
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app = FastAPI()
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@@ -19,19 +18,6 @@ app = FastAPI()
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# kafka producer - lazy init
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_producer: Optional[KafkaProducer] = None
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# supabase client - lazy init
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_supabase: Optional[Client] = None
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def get_supabase() -> Client:
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global _supabase
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if _supabase is None:
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url = os.getenv('NEXT_PUBLIC_SUPABASE_URL')
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key = os.getenv('NEXT_PUBLIC_SUPABASE_ANON_KEY')
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if not url or not key:
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raise ValueError("Supabase credentials not configured")
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_supabase = create_client(url, key)
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return _supabase
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def get_producer() -> KafkaProducer:
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global _producer
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if _producer is None:
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@@ -64,14 +50,6 @@ class EventPayload(BaseModel):
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userAgent: Optional[str] = None
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ts: Optional[str] = None
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class PriceLogPayload(BaseModel):
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productId: str
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price: float
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sessionId: str
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experimentId: Optional[str] = None
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storeMode: str
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ts: Optional[str] = None
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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@@ -95,8 +73,7 @@ async def startup_event():
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)
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topics = [
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NewTopic(name='user-interactions', num_partitions=3, replication_factor=1),
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NewTopic(name='price-logs', num_partitions=3, replication_factor=1)
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NewTopic(name='user-interactions', num_partitions=3, replication_factor=1)
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]
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admin.create_topics(new_topics=topics, validate_only=False)
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@@ -148,52 +125,26 @@ async def ingest_logs(event: EventPayload):
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print(traceback.format_exc())
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/kafka/price-log")
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async def ingest_price_log(price_log: PriceLogPayload):
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try:
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if not price_log.ts:
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price_log.ts = datetime.utcnow().isoformat() + 'Z'
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producer = get_producer()
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future = producer.send(
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'price-logs',
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key=price_log.productId,
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value=price_log.model_dump()
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)
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future.add_errback(lambda e: print(f"[KAFKA_PRICE_LOG_ERROR] {e}"))
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return {"success": True}
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except Exception as e:
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import traceback
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print(f"[PRICE_LOG_ERROR] {e}")
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print(traceback.format_exc())
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/kafka/dump")
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def dump_logs(
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topic: str = 'user-interactions',
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last_n: Optional[int] = None,
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t_start: Optional[str] = None,
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t_end: Optional[str] = None
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):
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"""dump all messages from specified kafka topic
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"""dump all messages from user-interactions topic
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params:
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topic: kafka topic to dump (default: user-interactions)
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last_n: return only last n messages (default: all)
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t_start: filter by start timestamp iso format
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t_end: filter by end timestamp iso format
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t_start: filter by start timestamp iso format (future use)
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t_end: filter by end timestamp iso format (future use)
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"""
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if topic not in ['user-interactions', 'price-logs']:
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raise HTTPException(status_code=400, detail="Invalid topic")
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host = os.getenv('KAFKA_HOST', 'localhost')
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port = os.getenv('KAFKA_PORT', '9092')
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broker = f'{host}:{port}'
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try:
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consumer = KafkaConsumer(
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topic,
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'user-interactions',
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bootstrap_servers=[broker],
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auto_offset_reset='earliest',
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enable_auto_commit=False,
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@@ -209,6 +160,7 @@ def dump_logs(
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# apply filters
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if t_start or t_end:
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# filter by timestamp range if provided
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filtered = []
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for e in events:
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ts = e.get('ts')
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@@ -231,130 +183,6 @@ def dump_logs(
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print(traceback.format_exc())
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/products/{product_id}")
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async def get_product_by_id(product_id: str):
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"""fetch single product by id from either hotel_products or airline_products"""
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try:
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supabase = get_supabase()
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# try hotel_products first
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response = supabase.table('hotel_products').select('*').eq('id', product_id).execute()
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if response.data and len(response.data) > 0:
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return {"success": True, "data": response.data[0]}
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# try airline_products
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response = supabase.table('airline_products').select('*').eq('id', product_id).execute()
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if response.data and len(response.data) > 0:
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return {"success": True, "data": response.data[0]}
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raise HTTPException(status_code=404, detail="Product not found")
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except HTTPException:
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raise
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except Exception as e:
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import traceback
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print(f"[PRODUCT_BY_ID_ERROR] {e}")
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print(traceback.format_exc())
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/products/type/{product_type}")
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async def get_products(
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product_type: str,
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dateIndex: Optional[int] = None,
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origin: Optional[str] = None,
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destination: Optional[str] = None,
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tripType: Optional[str] = None,
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adults: Optional[int] = None,
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children: Optional[int] = None,
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infants: Optional[int] = None,
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rooms: Optional[int] = None
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):
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"""fetch products from supabase based on type (hotel or airline)
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params:
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product_type: either 'hotel' or 'airline'
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dateIndex: optional days offset from today (e.g., 0=today, 1=tomorrow, -1=yesterday)
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origin: (airline) departure airport code
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destination: (airline/hotel) arrival airport or hotel location
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tripType: (airline) roundtrip, oneway, multicity
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adults, children, infants: passenger counts
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rooms: (hotel) number of rooms
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"""
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if product_type not in ['hotel', 'airline']:
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raise HTTPException(status_code=400, detail="product_type must be 'hotel' or 'airline'")
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try:
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supabase = get_supabase()
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table = f'{product_type}_products'
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query = supabase.table(table).select('*')
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# filter by exact date_index if provided
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if dateIndex is not None:
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query = query.eq('date_index', dateIndex)
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response = query.execute()
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results = response.data
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# apply in-memory filters based on metadata for airline products
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if product_type == 'airline' and results:
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filtered = []
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for product in results:
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metadata = product.get('metadata', {})
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# filter by origin airport
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if origin:
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dep = metadata.get('departure', {})
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if dep.get('airport') != origin:
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continue
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# filter by destination airport
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if destination:
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arr = metadata.get('arrival', {})
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if arr.get('airport') != destination:
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continue
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# passenger count validation (ensure total capacity)
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if adults is not None or children is not None or infants is not None:
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total_pax = (adults or 0) + (children or 0) + (infants or 0)
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avail = product.get('availability', 0)
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if avail < total_pax:
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continue
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filtered.append(product)
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results = filtered
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# apply in-memory filters for hotel products
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elif product_type == 'hotel' and results:
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filtered = []
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for product in results:
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metadata = product.get('metadata', {})
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# filter by occupancy capacity
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if adults is not None:
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max_occ = metadata.get('max_occupancy', 2)
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if max_occ < adults:
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continue
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# filter by room availability
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if rooms is not None:
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avail = product.get('availability', 0)
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if avail < rooms:
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continue
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filtered.append(product)
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results = filtered
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return {"success": True, "count": len(results), "data": results}
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except Exception as e:
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import traceback
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print(f"[PRODUCTS_ERROR] {e}")
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print(traceback.format_exc())
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raise HTTPException(status_code=500, detail=str(e))
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|
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if __name__ == "__main__":
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@@ -3,4 +3,3 @@ uvicorn[standard]==0.24.0
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kafka-python==2.0.2
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pydantic==2.5.0
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python-dotenv==1.0.0
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supabase==2.9.1
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@@ -9,9 +9,6 @@ services:
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environment:
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- KAFKA_HOST=kafka
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- KAFKA_PORT=29092
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- BACKEND_PORT=5000
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- NEXT_PUBLIC_SUPABASE_URL=${NEXT_PUBLIC_SUPABASE_URL}
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- NEXT_PUBLIC_SUPABASE_ANON_KEY=${NEXT_PUBLIC_SUPABASE_ANON_KEY}
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depends_on:
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- kafka
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restart: unless-stopped
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@@ -38,10 +38,7 @@ def get_agent(agent_type: AgentTypes, **kwargs) -> Agent:
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if __name__ == "__main__":
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import asyncio
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JTBD= "Find me the cheapest room in Madrid for 2 people in the next two days, review each hotel room in detail and then add it to cart."
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agent = get_agent(AgentTypes.GENERIC_BROWSER_USE_AGENT,
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goal=JTBD,
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url="http://localhost:3000/start-task?uuid=d10f5ab3-a7b7-4e97-8d94-ab06f1537c0a",
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timeout=300)
|
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JTBD= "Name all the products on this site and try to find out more about each product by clicking into them (they might not open)"
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agent = get_agent(AgentTypes.GENERIC_BROWSER_USE_AGENT, goal=JTBD, url="http://localhost:3000/products", timeout=300)
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R=asyncio.run(agent.act())
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print(R)
|
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|
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957
experiments/data_export.ipynb
Normal file
957
experiments/data_export.ipynb
Normal file
@@ -0,0 +1,957 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "62eafcd9-5462-4063-8873-0e7fb9add907",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from kafka import KafkaConsumer\n",
|
||||
"import pandas as pd\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"from dotenv import load_dotenv\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from IPython.display import display, SVG, Image\n",
|
||||
"load_dotenv()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "4af65cb4-e8cf-4877-b2db-13ac19f3838f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"RangeIndex: 73 entries, 0 to 72\n",
|
||||
"Data columns (total 13 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 sessionId 73 non-null object \n",
|
||||
" 1 eventName 73 non-null object \n",
|
||||
" 2 page 73 non-null object \n",
|
||||
" 3 productId 67 non-null object \n",
|
||||
" 4 storeMode 73 non-null object \n",
|
||||
" 5 userAgent 73 non-null object \n",
|
||||
" 6 ts 73 non-null object \n",
|
||||
" 7 metadata_referrer 6 non-null object \n",
|
||||
" 8 metadata_roomType 45 non-null object \n",
|
||||
" 9 metadata_price 45 non-null float64\n",
|
||||
" 10 metadata_nights 45 non-null float64\n",
|
||||
" 11 metadata_elementText 22 non-null object \n",
|
||||
" 12 metadata_dwellTime 22 non-null float64\n",
|
||||
"dtypes: float64(3), object(10)\n",
|
||||
"memory usage: 7.5+ KB\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"KAFKA_PORT=os.getenv(\"KAFKA_PORT\", 9092)\n",
|
||||
"topic = \"user-interactions\"\n",
|
||||
"consumer = KafkaConsumer(\n",
|
||||
" topic, \n",
|
||||
" enable_auto_commit=True,\n",
|
||||
" value_deserializer=lambda x: json.loads(x.decode('utf-8')),\n",
|
||||
" auto_offset_reset='earliest', \n",
|
||||
" bootstrap_servers=['localhost:9092'])\n",
|
||||
"messages=consumer.poll(timeout_ms=1000,max_records=10000)\n",
|
||||
"df = []\n",
|
||||
"for m in messages.values():\n",
|
||||
" for i in m:\n",
|
||||
" df.append(i.value)\n",
|
||||
"df = pd.DataFrame(df)\n",
|
||||
"# explode metadata col json\n",
|
||||
"df = df.join(pd.json_normalize(df.pop(\"metadata\"), sep=\".\").add_prefix(\"metadata_\"))\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "f6819a1c-32ab-49c7-845b-5df7bf60f561",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>sessionId</th>\n",
|
||||
" <th>eventName</th>\n",
|
||||
" <th>page</th>\n",
|
||||
" <th>productId</th>\n",
|
||||
" <th>storeMode</th>\n",
|
||||
" <th>userAgent</th>\n",
|
||||
" <th>ts</th>\n",
|
||||
" <th>metadata_referrer</th>\n",
|
||||
" <th>metadata_roomType</th>\n",
|
||||
" <th>metadata_price</th>\n",
|
||||
" <th>metadata_nights</th>\n",
|
||||
" <th>metadata_elementText</th>\n",
|
||||
" <th>metadata_dwellTime</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>d176d7c9-4027-4702-9e31-2a71395cdda0</td>\n",
|
||||
" <td>page_view</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
||||
" <td>2025-11-14T13:23:46.270Z</td>\n",
|
||||
" <td></td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>f0317a5d-e424-44e9-b784-c8f7291ffe31</td>\n",
|
||||
" <td>page_view</td>\n",
|
||||
" <td>/</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck...</td>\n",
|
||||
" <td>2025-11-14T13:26:00.291Z</td>\n",
|
||||
" <td></td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>f0317a5d-e424-44e9-b784-c8f7291ffe31</td>\n",
|
||||
" <td>page_view</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck...</td>\n",
|
||||
" <td>2025-11-14T13:26:07.769Z</td>\n",
|
||||
" <td></td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>f0317a5d-e424-44e9-b784-c8f7291ffe31</td>\n",
|
||||
" <td>view_item_page</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck...</td>\n",
|
||||
" <td>2025-11-14T13:26:15.010Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Premium Room</td>\n",
|
||||
" <td>269.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
|
||||
" <td>page_view</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
|
||||
" <td>2025-11-14T13:27:15.457Z</td>\n",
|
||||
" <td></td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
|
||||
" <td>view_item_page</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
|
||||
" <td>2025-11-14T13:27:15.591Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Premium Room</td>\n",
|
||||
" <td>264.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>432</th>\n",
|
||||
" <td>214d9fad-9b00-40c3-bd0e-7739b6acd654</td>\n",
|
||||
" <td>click</td>\n",
|
||||
" <td>1762448192425</td>\n",
|
||||
" <td>DIV</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>/</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>1623.0</td>\n",
|
||||
" <td>493.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
|
||||
" <td>view_item_page</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
|
||||
" <td>2025-11-14T13:27:21.483Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Premium Room</td>\n",
|
||||
" <td>264.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
|
||||
" <td>hover_over_title</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
|
||||
" <td>2025-11-14T13:27:22.646Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Grand Plaza Hotel</td>\n",
|
||||
" <td>1200.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
|
||||
" <td>view_item_page</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
|
||||
" <td>2025-11-14T13:27:25.889Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Premium Room</td>\n",
|
||||
" <td>264.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>35</th>\n",
|
||||
" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
|
||||
" <td>page_view</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
||||
" <td>2025-11-14T13:53:59.993Z</td>\n",
|
||||
" <td></td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>36</th>\n",
|
||||
" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
|
||||
" <td>view_item_page</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
||||
" <td>2025-11-14T13:54:10.705Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Premium Room</td>\n",
|
||||
" <td>223.0</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>37</th>\n",
|
||||
" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
|
||||
" <td>hover_over_title</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-0</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
||||
" <td>2025-11-14T13:54:11.771Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>416.0</td>\n",
|
||||
" <td>397.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Grand Plaza Hotel</td>\n",
|
||||
" <td>1200.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>38</th>\n",
|
||||
" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
|
||||
" <td>view_item_page</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-1</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
||||
" <td>2025-11-14T13:54:29.772Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Standard Room</td>\n",
|
||||
" <td>267.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>39</th>\n",
|
||||
" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
|
||||
" <td>hover_over_title</td>\n",
|
||||
" <td>/products</td>\n",
|
||||
" <td>htl-1</td>\n",
|
||||
" <td>hotel</td>\n",
|
||||
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
||||
" <td>2025-11-14T13:54:30.833Z</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>Seaside Resort</td>\n",
|
||||
" <td>1200.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" sessionId eventName page \\\n",
|
||||
"0 d176d7c9-4027-4702-9e31-2a71395cdda0 page_view /products \n",
|
||||
"1 f0317a5d-e424-44e9-b784-c8f7291ffe31 page_view / \n",
|
||||
"2 f0317a5d-e424-44e9-b784-c8f7291ffe31 page_view /products \n",
|
||||
"3 f0317a5d-e424-44e9-b784-c8f7291ffe31 view_item_page /products \n",
|
||||
"4 238dc588-a7ab-4c0e-bccd-6abca5076c66 page_view /products \n",
|
||||
"5 238dc588-a7ab-4c0e-bccd-6abca5076c66 view_item_page /products \n",
|
||||
"6 238dc588-a7ab-4c0e-bccd-6abca5076c66 view_item_page /products \n",
|
||||
"7 238dc588-a7ab-4c0e-bccd-6abca5076c66 hover_over_title /products \n",
|
||||
"8 238dc588-a7ab-4c0e-bccd-6abca5076c66 view_item_page /products \n",
|
||||
"35 013fc334-4045-4d5a-8739-dd0a8766a63b page_view /products \n",
|
||||
"36 013fc334-4045-4d5a-8739-dd0a8766a63b view_item_page /products \n",
|
||||
"37 013fc334-4045-4d5a-8739-dd0a8766a63b hover_over_title /products \n",
|
||||
"38 013fc334-4045-4d5a-8739-dd0a8766a63b view_item_page /products \n",
|
||||
"39 013fc334-4045-4d5a-8739-dd0a8766a63b hover_over_title /products \n",
|
||||
"\n",
|
||||
" productId storeMode userAgent \\\n",
|
||||
"0 None hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
||||
"1 None hotel Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck... \n",
|
||||
"2 None hotel Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck... \n",
|
||||
"3 htl-0 hotel Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck... \n",
|
||||
"4 None hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
||||
"5 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
||||
"6 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
||||
"7 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
||||
"8 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
||||
"35 None hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
||||
"36 htl-0 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
||||
"37 htl-0 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
||||
"38 htl-1 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
||||
"39 htl-1 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
||||
"\n",
|
||||
" ts metadata_referrer metadata_roomType \\\n",
|
||||
"0 2025-11-14T13:23:46.270Z NaN \n",
|
||||
"1 2025-11-14T13:26:00.291Z NaN \n",
|
||||
"2 2025-11-14T13:26:07.769Z NaN \n",
|
||||
"3 2025-11-14T13:26:15.010Z NaN Premium Room \n",
|
||||
"4 2025-11-14T13:27:15.457Z NaN \n",
|
||||
"5 2025-11-14T13:27:15.591Z NaN Premium Room \n",
|
||||
"6 2025-11-14T13:27:21.483Z NaN Premium Room \n",
|
||||
"7 2025-11-14T13:27:22.646Z NaN NaN \n",
|
||||
"8 2025-11-14T13:27:25.889Z NaN Premium Room \n",
|
||||
"35 2025-11-14T13:53:59.993Z NaN \n",
|
||||
"36 2025-11-14T13:54:10.705Z NaN Premium Room \n",
|
||||
"37 2025-11-14T13:54:11.771Z NaN NaN \n",
|
||||
"38 2025-11-14T13:54:29.772Z NaN Standard Room \n",
|
||||
"39 2025-11-14T13:54:30.833Z NaN NaN \n",
|
||||
"\n",
|
||||
" metadata_price metadata_nights metadata_elementText metadata_dwellTime \n",
|
||||
"0 NaN NaN NaN NaN \n",
|
||||
"1 NaN NaN NaN NaN \n",
|
||||
"2 NaN NaN NaN NaN \n",
|
||||
"3 269.0 1.0 NaN NaN \n",
|
||||
"4 NaN NaN NaN NaN \n",
|
||||
"5 264.0 2.0 NaN NaN \n",
|
||||
"6 264.0 2.0 NaN NaN \n",
|
||||
"7 NaN NaN Grand Plaza Hotel 1200.0 \n",
|
||||
"8 264.0 2.0 NaN NaN \n",
|
||||
"35 NaN NaN NaN NaN \n",
|
||||
"36 223.0 3.0 NaN NaN \n",
|
||||
"37 NaN NaN Grand Plaza Hotel 1200.0 \n",
|
||||
"38 267.0 5.0 NaN NaN \n",
|
||||
"39 NaN NaN Seaside Resort 1200.0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.groupby('sessionId').head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "380eca5f-8304-4fb2-be32-e8bcfd312085",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['013fc334-4045-4d5a-8739-dd0a8766a63b',\n",
|
||||
" '238dc588-a7ab-4c0e-bccd-6abca5076c66',\n",
|
||||
" 'd176d7c9-4027-4702-9e31-2a71395cdda0',\n",
|
||||
" 'f0317a5d-e424-44e9-b784-c8f7291ffe31']"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sessions = list(set(df['sessionId'])); sessions # 238dc588-a7ab-4c0e-bccd-6abca5076c66"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "f4ae6f81-dcb8-44be-aee7-30dbc3a6bae1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# map sessions to experiments"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "050d90a4-20a9-47f5-b998-c31178a54cb3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def build_transition_prob_matrix(df: pd.DataFrame):\n",
|
||||
" df = df.dropna(subset=['eventName'])\n",
|
||||
" events = df['eventName'].tolist()\n",
|
||||
" labels = pd.Index(events).unique().tolist()\n",
|
||||
" idx = {e:i for i,e in enumerate(labels)}\n",
|
||||
" M = np.zeros((len(labels), len(labels)), dtype=float)\n",
|
||||
" for a, b in zip(events, events[1:]):\n",
|
||||
" M[idx[a], idx[b]] += 1\n",
|
||||
" row_sums = M.sum(axis=1, keepdims=True)\n",
|
||||
" with np.errstate(divide='ignore', invalid='ignore'):\n",
|
||||
" P = np.divide(M, row_sums, where=row_sums>0) # row-normalized\n",
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" return P, labels"
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]
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},
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"# https://medium.com/data-science/time-series-data-markov-transition-matrices-7060771e362b\n",
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"from graphviz import Digraph\n",
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"import numpy as np\n",
|
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"import pandas as pd\n",
|
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"\n",
|
||||
"def _as_prob_df(matrix, labels=None):\n",
|
||||
" \"\"\"Return a square DataFrame with index=columns=labels.\"\"\"\n",
|
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" if isinstance(matrix, pd.DataFrame):\n",
|
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" # Ensure square and aligned\n",
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" assert (matrix.index == matrix.columns).all(), \"Index/columns must match.\"\n",
|
||||
" return matrix\n",
|
||||
" matrix = np.asarray(matrix, dtype=float)\n",
|
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" assert matrix.shape[0] == matrix.shape[1], \"Matrix must be square.\"\n",
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" if labels is None:\n",
|
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" raise ValueError(\"labels are required when matrix is not a DataFrame\")\n",
|
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" assert len(labels) == matrix.shape[0], \"labels length must match matrix size.\"\n",
|
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" return pd.DataFrame(matrix, index=list(labels), columns=list(labels))\n",
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"\n",
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"def _df_to_edgelist(P: pd.DataFrame, threshold=0.0, round_digits=2):\n",
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" \"\"\"Build weighted edges > threshold.\"\"\"\n",
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" edges = []\n",
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" for src in P.index:\n",
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" for dst in P.columns:\n",
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" w = float(P.loc[src, dst])\n",
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" if w > threshold:\n",
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" edges.append((str(src), str(dst), f\"{w:.{round_digits}f}\"))\n",
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" return edges\n",
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"\n",
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"def render_graph(fname, matrix, ls_index=None, threshold=0.0, fmt=\"svg\", view=False):\n",
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" \"\"\"\n",
|
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" fname: output file stem (no extension)\n",
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" matrix: NumPy array or pandas DataFrame of transition PROBABILITIES\n",
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" ls_index: ordered labels (required if matrix is not a DataFrame)\n",
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" threshold: hide edges with weight <= threshold\n",
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" fmt: 'svg'|'png'|'pdf' etc.\n",
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" view: open after rendering\n",
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" \"\"\"\n",
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" P = _as_prob_df(matrix, labels=ls_index)\n",
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" edges = _df_to_edgelist(P, threshold=threshold)\n",
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"\n",
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" g = Digraph(format=fmt)\n",
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" g.attr(rankdir=\"LR\", size=\"30\")\n",
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" g.attr(\"node\", shape=\"circle\")\n",
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"\n",
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" # ensure isolated nodes appear\n",
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" for node in P.index:\n",
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" g.node(str(node), width=\"1\", height=\"1\")\n",
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"\n",
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" for src, dst, label in edges:\n",
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" g.edge(src, dst, label=label)\n",
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"\n",
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" g.render(fname, view=view, cleanup=True)\n",
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" return g\n"
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]
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" print(session_id)\n",
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" display(g)\n",
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" return P\n",
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"for session in sessions:\n",
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" print(explore_session(session))"
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]
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"kernelspec": {
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"name": "phantom"
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbformat_minor": 5
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@@ -1,19 +0,0 @@
|
||||
from .extract import (
|
||||
KafkaDataFetcher,
|
||||
ExperimentJoiner,
|
||||
EventTitleAugmenter,
|
||||
)
|
||||
from .demand import DemandEstimator
|
||||
from .mapping import SessionTransitionProbMatrixTransformer, render_graph
|
||||
from .pipeline import etl_pipeline, pricing_pipeline
|
||||
|
||||
__all__ = [
|
||||
'KafkaDataFetcher',
|
||||
'ExperimentJoiner',
|
||||
'EventTitleAugmenter',
|
||||
'DemandEstimator',
|
||||
'SessionTransitionProbMatrixTransformer',
|
||||
'render_graph',
|
||||
'etl_pipeline',
|
||||
'pricing_pipeline',
|
||||
]
|
||||
@@ -1,119 +0,0 @@
|
||||
from sklearn.base import BaseEstimator, TransformerMixin
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from supabase import create_client, Client
|
||||
from typing import Optional, Literal
|
||||
import os
|
||||
import logging
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
|
||||
SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
|
||||
|
||||
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
|
||||
|
||||
class ChunkInteractionsIntoSteps(BaseEstimator, TransformerMixin):
|
||||
"""
|
||||
Split interaction data into time windows for temporal analysis.
|
||||
Returns a list of dataframes, one per time window.
|
||||
"""
|
||||
def __init__(self,
|
||||
window_size:str='1h',
|
||||
ts_col:str='ts',
|
||||
return_metadata:bool=True):
|
||||
"""
|
||||
Args:
|
||||
window_size: pandas freq string ('1h', '30T', '1D', etc)
|
||||
ts_col: timestamp column name
|
||||
return_metadata: if True, return dict with metadata per chunk
|
||||
"""
|
||||
self.window_size = window_size
|
||||
self.ts_col = ts_col
|
||||
self.return_metadata = return_metadata
|
||||
|
||||
def fit(self, X):
|
||||
return self
|
||||
|
||||
def transform(self, interactions: pd.DataFrame):
|
||||
"""
|
||||
Returns:
|
||||
if return_metadata=False: list of dataframes, one per window
|
||||
if return_metadata=True: list of dicts with keys:
|
||||
- 'data': dataframe for this window
|
||||
- 'window_start': start timestamp
|
||||
- 'window_end': end timestamp
|
||||
- 'window_idx': integer index
|
||||
"""
|
||||
if interactions.empty:
|
||||
return []
|
||||
|
||||
df = interactions.copy()
|
||||
|
||||
# ensure timestamp is datetime
|
||||
if not pd.api.types.is_datetime64_any_dtype(df[self.ts_col]):
|
||||
df[self.ts_col] = pd.to_datetime(df[self.ts_col])
|
||||
|
||||
# sort by time
|
||||
df = df.sort_values(self.ts_col)
|
||||
|
||||
# assign window
|
||||
df['_window'] = df[self.ts_col].dt.floor(self.window_size)
|
||||
|
||||
# group by window
|
||||
chunks = []
|
||||
for idx, (window_start, group) in enumerate(df.groupby('_window')):
|
||||
chunk_data = group.drop(columns=['_window'])
|
||||
|
||||
if self.return_metadata:
|
||||
chunks.append({
|
||||
'data': chunk_data,
|
||||
'window_start': window_start,
|
||||
'window_end': window_start + pd.Timedelta(self.window_size),
|
||||
'window_idx': idx
|
||||
})
|
||||
else:
|
||||
chunks.append(chunk_data)
|
||||
|
||||
return chunks
|
||||
|
||||
|
||||
class DemandEstimator(BaseEstimator, TransformerMixin):
|
||||
def __init__(self,
|
||||
store_mode:str='hotel',
|
||||
session_filter:str="",
|
||||
experiment_filter:str=""):
|
||||
self.store=store_mode
|
||||
self.session_filter=session_filter if len(session_filter)>0 else None
|
||||
self.experiment_filter=experiment_filter if len(experiment_filter)>0 else None
|
||||
def fit(self, X):
|
||||
return self
|
||||
|
||||
def transform(self, interactions : pd.DataFrame):
|
||||
if interactions.empty:
|
||||
return pd.DataFrame(columns=["productId", "demand_score"])
|
||||
if self.session_filter:
|
||||
interactions = interactions[interactions['sessionId'] == self.session_filter]
|
||||
if self.experiment_filter:
|
||||
interactions = interactions[interactions['experimentId'] == self.experiment_filter]
|
||||
|
||||
products=supabase.table(f'{self.store}_products').select("id, room_type, date_index, metadata, availability").execute()
|
||||
products = pd.DataFrame(products.data)
|
||||
unique_products = products['id'].unique()
|
||||
log.info(f"Demand estimator found {len(unique_products)} in data")
|
||||
|
||||
# filter out rows without productId
|
||||
interactions_with_products = interactions.dropna(subset=['productId'])
|
||||
|
||||
if interactions_with_products.empty:
|
||||
# no interactions with products, return all zeros
|
||||
return pd.DataFrame({
|
||||
'productId': unique_products,
|
||||
'demand_score': 0
|
||||
})
|
||||
|
||||
# TODO: improve demand score calculation rather than just counting interactions (use weights..)
|
||||
# while maintaining simplicity of a simple cross tab approach
|
||||
product_demand = pd.crosstab(interactions_with_products['productId'], "no_of_interactions")
|
||||
product_demand = product_demand.reindex(unique_products, fill_value=0).reset_index()
|
||||
product_demand.columns = ['productId', 'demand_score']
|
||||
return product_demand
|
||||
@@ -1,333 +0,0 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from typing import List, Dict, Optional
|
||||
from sklearn.base import BaseEstimator, TransformerMixin
|
||||
from supabase import create_client, Client
|
||||
import os
|
||||
|
||||
SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
|
||||
SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
|
||||
|
||||
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
|
||||
|
||||
class TemporalElasticityEstimator(BaseEstimator, TransformerMixin):
|
||||
"""
|
||||
Compute price elasticity from time-series demand and price data.
|
||||
|
||||
Elasticity = (% change in quantity) / (% change in price)
|
||||
|
||||
Works with chunked time-window data from ChunkInteractionsIntoSteps.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
method:str='point',
|
||||
min_observations:int=2,
|
||||
smooth_window:Optional[int]=None):
|
||||
"""
|
||||
Args:
|
||||
method: 'point' (point elasticity) or 'arc' (arc elasticity)
|
||||
min_observations: min data points needed per product
|
||||
smooth_window: if set, apply rolling avg smoothing to time series
|
||||
"""
|
||||
self.method = method
|
||||
self.min_observations = min_observations
|
||||
self.smooth_window = smooth_window
|
||||
|
||||
def fit(self, X):
|
||||
return self
|
||||
|
||||
def transform(self,
|
||||
demand_chunks: List[Dict],
|
||||
price_chunks: List[Dict],
|
||||
store_mode: str = 'hotel') -> pd.DataFrame:
|
||||
"""
|
||||
Args:
|
||||
demand_chunks: list from ChunkInteractionsIntoSteps + DemandEstimator
|
||||
each item: {'window_start', 'window_end', 'demand_vector'}
|
||||
price_chunks: list of dicts with {'window_start', 'window_end', 'price_vector'}
|
||||
store_mode: 'hotel' or 'airline' to fetch all products
|
||||
|
||||
Returns:
|
||||
df with [productId, elasticity, std_error, n_observations]
|
||||
"""
|
||||
# fetch all products from database
|
||||
all_products = supabase.table(f'{store_mode}_products').select("id").execute()
|
||||
all_product_ids = [p['id'] for p in all_products.data]
|
||||
|
||||
aligned = self._align_chunks(demand_chunks, price_chunks)
|
||||
if not aligned:
|
||||
# return all products with zero elasticity
|
||||
return pd.DataFrame({
|
||||
'productId': all_product_ids,
|
||||
'elasticity': 0.0,
|
||||
'std_error': 0.0,
|
||||
'n_obs': 0
|
||||
})
|
||||
|
||||
# build time series per product
|
||||
product_series = self._build_product_timeseries(aligned)
|
||||
|
||||
# compute elasticity per product
|
||||
elasticities = []
|
||||
for pid, series in product_series.items():
|
||||
if len(series) < self.min_observations:
|
||||
# assign 0 elasticity for products with insufficient data
|
||||
elasticities.append({
|
||||
'productId': pid,
|
||||
'elasticity': 0.0,
|
||||
'std_error': 0.0,
|
||||
'n_obs': len(series)
|
||||
})
|
||||
continue
|
||||
|
||||
# apply smoothing if requested
|
||||
if self.smooth_window and len(series) >= self.smooth_window:
|
||||
series = self._smooth_series(series, self.smooth_window)
|
||||
|
||||
elast = self._compute_elasticity(series)
|
||||
elasticities.append({
|
||||
'productId': pid,
|
||||
'elasticity': elast['value'],
|
||||
'std_error': elast.get('std_error', 0.0),
|
||||
'n_obs': len(series)
|
||||
})
|
||||
|
||||
result_df = pd.DataFrame(elasticities)
|
||||
|
||||
# fill in missing products with zero elasticity
|
||||
observed_pids = set(result_df['productId'].unique())
|
||||
missing_pids = [pid for pid in all_product_ids if pid not in observed_pids]
|
||||
|
||||
if missing_pids:
|
||||
missing_df = pd.DataFrame({
|
||||
'productId': missing_pids,
|
||||
'elasticity': 0.0,
|
||||
'std_error': 0.0,
|
||||
'n_obs': 0
|
||||
})
|
||||
result_df = pd.concat([result_df, missing_df], ignore_index=True)
|
||||
|
||||
return result_df
|
||||
|
||||
def _align_chunks(self, demand_chunks, price_chunks):
|
||||
"""Align demand and price data by matching time windows."""
|
||||
aligned = []
|
||||
|
||||
# create lookup for price chunks by window_start
|
||||
price_lookup = {chunk['window_start']: chunk for chunk in price_chunks}
|
||||
|
||||
for demand_chunk in demand_chunks:
|
||||
window_start = demand_chunk['window_start']
|
||||
if window_start in price_lookup:
|
||||
aligned.append({
|
||||
'window_start': window_start,
|
||||
'window_end': demand_chunk['window_end'],
|
||||
'demand': demand_chunk['demand_vector'],
|
||||
'prices': price_lookup[window_start]['price_vector']
|
||||
})
|
||||
|
||||
return aligned
|
||||
|
||||
def _build_product_timeseries(self, aligned_chunks):
|
||||
"""Build time series [price, quantity] per product."""
|
||||
series_by_product = {}
|
||||
|
||||
for chunk in aligned_chunks:
|
||||
demand_df = chunk['demand']
|
||||
price_df = chunk['prices']
|
||||
|
||||
# merge on productId
|
||||
merged = demand_df.merge(price_df, on='productId', how='inner')
|
||||
|
||||
for _, row in merged.iterrows():
|
||||
pid = row['productId']
|
||||
if pid not in series_by_product:
|
||||
series_by_product[pid] = []
|
||||
|
||||
series_by_product[pid].append({
|
||||
'timestamp': chunk['window_start'],
|
||||
'price': row['price'],
|
||||
'quantity': row['demand_score']
|
||||
})
|
||||
|
||||
return series_by_product
|
||||
|
||||
def _smooth_series(self, series, window):
|
||||
"""Apply rolling average smoothing."""
|
||||
df = pd.DataFrame(series)
|
||||
df['price_smooth'] = df['price'].rolling(window=window, center=True).mean()
|
||||
df['quantity_smooth'] = df['quantity'].rolling(window=window, center=True).mean()
|
||||
df = df.dropna()
|
||||
|
||||
return [{'timestamp': row['timestamp'],
|
||||
'price': row['price_smooth'],
|
||||
'quantity': row['quantity_smooth']}
|
||||
for _, row in df.iterrows()]
|
||||
|
||||
def _compute_elasticity(self, series):
|
||||
"""Compute elasticity from time series."""
|
||||
if len(series) < 2:
|
||||
return {'value': 0.0, 'std_error': 0.0}
|
||||
|
||||
prices = np.array([s['price'] for s in series])
|
||||
quantities = np.array([s['quantity'] for s in series])
|
||||
|
||||
# filter out zero/negative values
|
||||
valid = (prices > 0) & (quantities > 0)
|
||||
if valid.sum() < 2:
|
||||
return {'value': 0.0, 'std_error': 0.0}
|
||||
|
||||
prices = prices[valid]
|
||||
quantities = quantities[valid]
|
||||
|
||||
if self.method == 'point':
|
||||
return self._point_elasticity(prices, quantities)
|
||||
elif self.method == 'arc':
|
||||
return self._arc_elasticity(prices, quantities)
|
||||
else:
|
||||
raise ValueError(f"Unknown method: {self.method}")
|
||||
|
||||
def _point_elasticity(self, prices, quantities):
|
||||
"""
|
||||
Point elasticity using log-log regression.
|
||||
log(Q) = a + b*log(P), elasticity = b
|
||||
"""
|
||||
if len(prices) < 2:
|
||||
return {'value': 0.0, 'std_error': 0.0}
|
||||
|
||||
log_p = np.log(prices)
|
||||
log_q = np.log(quantities)
|
||||
|
||||
# simple linear regression
|
||||
if log_p.std() == 0:
|
||||
return {'value': 0.0, 'std_error': 0.0}
|
||||
|
||||
cov = np.cov(log_p, log_q)[0, 1]
|
||||
var = np.var(log_p)
|
||||
b = cov / var
|
||||
|
||||
# std error estimate (avoid div by zero)
|
||||
if len(prices) <= 2:
|
||||
se_b = 0.0
|
||||
else:
|
||||
residuals = log_q - (log_q.mean() + b * (log_p - log_p.mean()))
|
||||
mse = (residuals ** 2).sum() / (len(prices) - 2)
|
||||
se_b = np.sqrt(mse / (len(prices) * var))
|
||||
|
||||
return {'value': b, 'std_error': se_b}
|
||||
|
||||
def _arc_elasticity(self, prices, quantities):
|
||||
"""
|
||||
Arc elasticity: average of period-over-period elasticities.
|
||||
E_t = (ΔQ/Q_avg) / (ΔP/P_avg)
|
||||
"""
|
||||
elasticities = []
|
||||
|
||||
for i in range(1, len(prices)):
|
||||
p1, p2 = prices[i-1], prices[i]
|
||||
q1, q2 = quantities[i-1], quantities[i]
|
||||
|
||||
p_avg = (p1 + p2) / 2
|
||||
q_avg = (q1 + q2) / 2
|
||||
|
||||
if p_avg == 0 or q_avg == 0:
|
||||
continue
|
||||
|
||||
delta_p = p2 - p1
|
||||
delta_q = q2 - q1
|
||||
|
||||
if delta_p == 0:
|
||||
continue
|
||||
|
||||
e = (delta_q / q_avg) / (delta_p / p_avg)
|
||||
elasticities.append(e)
|
||||
|
||||
if not elasticities:
|
||||
return None
|
||||
|
||||
return {
|
||||
'value': np.mean(elasticities),
|
||||
'std_error': np.std(elasticities) / np.sqrt(len(elasticities))
|
||||
}
|
||||
|
||||
|
||||
def aggregate_price_logs(price_logs: pd.DataFrame,
|
||||
window_size: str = '1H',
|
||||
ts_col: str = 'ts',
|
||||
store_mode : str = 'hotel') -> List[Dict]:
|
||||
"""
|
||||
Recover price vectors treating prices as persistent state changes.
|
||||
|
||||
Prices are set-operations that persist until next change. For each window:
|
||||
- If price logs exist: average all changes within window
|
||||
- If no logs: carry forward last price before window end
|
||||
|
||||
Args:
|
||||
price_logs: df with [productId, price, ts, ...]
|
||||
window_size: time window size matching ChunkInteractionsIntoSteps
|
||||
ts_col: timestamp column name
|
||||
|
||||
Returns:
|
||||
list of dicts with {'window_start', 'window_end', 'price_vector'}
|
||||
where price_vector is df with [productId, price]
|
||||
"""
|
||||
if price_logs.empty:
|
||||
return []
|
||||
|
||||
df = price_logs.copy()
|
||||
|
||||
if not pd.api.types.is_datetime64_any_dtype(df[ts_col]):
|
||||
df[ts_col] = pd.to_datetime(df[ts_col])
|
||||
|
||||
df = df.sort_values([ts_col, 'productId'])
|
||||
all_products=supabase.table(f'{store_mode}_products').select("id, room_type, date_index, metadata, availability").execute()
|
||||
all_products = pd.DataFrame(all_products.data)
|
||||
unique_products = all_products['id'].unique()
|
||||
|
||||
# generate windows across data range
|
||||
min_time, max_time = df[ts_col].min(), df[ts_col].max()
|
||||
windows = pd.date_range(
|
||||
start=min_time.floor(window_size),
|
||||
end=max_time,
|
||||
freq=window_size
|
||||
)
|
||||
|
||||
chunks = []
|
||||
|
||||
for window_start in windows:
|
||||
window_end = window_start + pd.Timedelta(window_size)
|
||||
price_vector = []
|
||||
|
||||
# all products with price history by window_end
|
||||
#historical_products = df[df[ts_col] < window_end]['productId'].unique()
|
||||
historical_products = unique_products.tolist()
|
||||
|
||||
for pid in historical_products:
|
||||
product_data = df[df['productId'] == pid]
|
||||
|
||||
# logs within window
|
||||
in_window = product_data[
|
||||
(product_data[ts_col] >= window_start) &
|
||||
(product_data[ts_col] < window_end)
|
||||
]
|
||||
|
||||
if not in_window.empty:
|
||||
# average changes within window
|
||||
price = in_window['price'].mean()
|
||||
else:
|
||||
# carry forward: last price before window end
|
||||
before_window = product_data[product_data[ts_col] < window_end]
|
||||
if before_window.empty:
|
||||
continue
|
||||
price = before_window['price'].iloc[-1]
|
||||
|
||||
price_vector.append({'productId': pid, 'price': price})
|
||||
|
||||
if price_vector:
|
||||
chunks.append({
|
||||
'window_start': window_start,
|
||||
'window_end': window_end,
|
||||
'price_vector': pd.DataFrame(price_vector)
|
||||
})
|
||||
|
||||
return chunks
|
||||
@@ -6,7 +6,6 @@ import requests
|
||||
from dotenv import load_dotenv
|
||||
from sklearn.base import BaseEstimator, TransformerMixin
|
||||
from supabase import create_client, Client
|
||||
from typing import Tuple, List, Dict
|
||||
load_dotenv()
|
||||
|
||||
BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:5000")
|
||||
@@ -16,192 +15,106 @@ N_PRICE_BUCKETS = 5
|
||||
|
||||
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
|
||||
|
||||
def get_data_from_kafka() -> pd.DataFrame:
|
||||
"""fetch all events from backend dump endpoint"""
|
||||
resp = requests.get(f"{BACKEND_URL}/api/kafka/dump")
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
class KafkaDataFetcher(BaseEstimator, TransformerMixin):
|
||||
def __init__(self, topic: str = "user-interactions"):
|
||||
self.topic = topic # also can be price-logs
|
||||
if not data.get('success') or not data.get('data'):
|
||||
return pd.DataFrame()
|
||||
|
||||
df = pd.DataFrame(data['data'])
|
||||
# explode metadata col json
|
||||
if 'metadata' in df.columns:
|
||||
df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
|
||||
df = df.dropna(subset=['eventName'])
|
||||
return df
|
||||
|
||||
|
||||
def join_with_experiments(df: pd.DataFrame) -> pd.DataFrame:
|
||||
if df.empty or 'experimentId' not in df.columns:
|
||||
return df
|
||||
|
||||
unique_exp_ids = df['experimentId'].dropna().unique()
|
||||
if len(unique_exp_ids) == 0:
|
||||
return df
|
||||
|
||||
resp = supabase.table('experiments').select(
|
||||
'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, task:tasks(task_name, task_description, task_def_of_done)'
|
||||
).in_('id', unique_exp_ids.tolist()).execute()
|
||||
|
||||
if not resp.data:
|
||||
return df
|
||||
|
||||
exp_df = pd.DataFrame(resp.data)
|
||||
|
||||
# flatten task nested object if present
|
||||
if 'task' in exp_df.columns and exp_df['task'].notnull().any():
|
||||
task_normalized = pd.json_normalize(exp_df['task'].dropna())
|
||||
task_normalized.index = exp_df[exp_df['task'].notnull()].index
|
||||
exp_df = exp_df.drop(columns=['task']).join(task_normalized, rsuffix='_task')
|
||||
|
||||
# rename experiment columns for clarity
|
||||
exp_df = exp_df.rename(columns={
|
||||
'id': 'experimentId',
|
||||
'subject_name': 'exp_subject',
|
||||
'xp_human_only': 'exp_human_only',
|
||||
'xp_market_mode': 'exp_market_mode',
|
||||
'xp_task_id': 'exp_task_id'
|
||||
})
|
||||
|
||||
df = df.merge(exp_df, on='experimentId', how='left')
|
||||
return df
|
||||
|
||||
|
||||
def augment_event_titles(df: pd.DataFrame) -> pd.DataFrame:
|
||||
# from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
|
||||
# we want metadata schema to create product specific event names
|
||||
|
||||
# only create price buckets if we have enough unique prices
|
||||
if df["metadata_price"].notnull().sum() > 0:
|
||||
try:
|
||||
price_buckets = pd.qcut(
|
||||
df["metadata_price"],
|
||||
q=N_PRICE_BUCKETS,
|
||||
labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
|
||||
duplicates='drop' # handle duplicate bin edges
|
||||
)
|
||||
except ValueError:
|
||||
# fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
|
||||
price_buckets = df["metadata_price"].apply(lambda x: f"P_{int(x)}" if pd.notnull(x) else "")
|
||||
else:
|
||||
price_buckets = pd.Series([""] * len(df), index=df.index)
|
||||
|
||||
# metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
|
||||
# TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
|
||||
df["metadata_schema"] = np.where(
|
||||
df["productId"].notnull() & df["metadata_price"].notnull(),
|
||||
"_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
|
||||
""
|
||||
)
|
||||
df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
|
||||
return df
|
||||
|
||||
|
||||
def extract() -> pd.DataFrame:
|
||||
df = get_data_from_kafka()
|
||||
df = join_with_experiments(df)
|
||||
df = augment_event_titles(df)
|
||||
return df
|
||||
|
||||
|
||||
class DataExtractor(BaseEstimator, TransformerMixin):
|
||||
def fit(self, X=None, y=None):
|
||||
return self
|
||||
|
||||
def transform(self, X=None):
|
||||
resp = requests.get(f"{BACKEND_URL}/api/kafka/dump?topic={self.topic}")
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
if not data.get('success') or not data.get('data'):
|
||||
return pd.DataFrame()
|
||||
|
||||
df = pd.DataFrame(data['data'])
|
||||
if self.topic == 'user-interactions':
|
||||
if 'metadata' in df.columns: # explode metadata col json
|
||||
df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
|
||||
df = df.dropna(subset=['eventName'])
|
||||
# remape dateIndex
|
||||
df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
|
||||
return df
|
||||
return extract()
|
||||
|
||||
|
||||
class ExperimentJoiner(BaseEstimator, TransformerMixin):
|
||||
def fit(self, X=None, y=None):
|
||||
return self
|
||||
|
||||
def transform(self, df):
|
||||
if df.empty or 'experimentId' not in df.columns:
|
||||
return df
|
||||
|
||||
unique_exp_ids = df['experimentId'].dropna().unique()
|
||||
if len(unique_exp_ids) == 0:
|
||||
return df
|
||||
|
||||
resp = supabase.table('experiments').select(
|
||||
'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, task:tasks(task_name, task_description, task_def_of_done)'
|
||||
).in_('id', unique_exp_ids.tolist()).execute()
|
||||
|
||||
if not resp.data:
|
||||
return df
|
||||
|
||||
exp_df = pd.DataFrame(resp.data)
|
||||
|
||||
# flatten task nested object if present
|
||||
if 'task' in exp_df.columns and exp_df['task'].notnull().any():
|
||||
task_normalized = pd.json_normalize(exp_df['task'].dropna())
|
||||
task_normalized.index = exp_df[exp_df['task'].notnull()].index
|
||||
exp_df = exp_df.drop(columns=['task']).join(task_normalized, rsuffix='_task')
|
||||
|
||||
# rename experiment columns for clarity
|
||||
exp_df = exp_df.rename(columns={
|
||||
'id': 'experimentId',
|
||||
'subject_name': 'exp_subject',
|
||||
'xp_human_only': 'exp_human_only',
|
||||
'xp_market_mode': 'exp_market_mode',
|
||||
'xp_task_id': 'exp_task_id'
|
||||
})
|
||||
|
||||
df = df.merge(exp_df, on='experimentId', how='left')
|
||||
return df
|
||||
|
||||
|
||||
class EventTitleAugmenter(BaseEstimator, TransformerMixin):
|
||||
def fit(self, X=None, y=None):
|
||||
return self
|
||||
|
||||
def transform(self, df):
|
||||
# from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
|
||||
# we want metadata schema to create product specific event names
|
||||
|
||||
# only create price buckets if we have enough unique prices
|
||||
if df["metadata_price"].notnull().sum() > 0:
|
||||
try:
|
||||
price_buckets = pd.qcut(
|
||||
df["metadata_price"],
|
||||
q=N_PRICE_BUCKETS,
|
||||
labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
|
||||
duplicates='drop' # handle duplicate bin edges
|
||||
)
|
||||
except ValueError:
|
||||
# fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
|
||||
price_buckets = df["metadata_price"].apply(lambda x: f"P_{int(x)}" if pd.notnull(x) else "")
|
||||
else:
|
||||
price_buckets = pd.Series([""] * len(df), index=df.index)
|
||||
|
||||
# metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
|
||||
# TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
|
||||
df["metadata_schema"] = np.where(
|
||||
df["productId"].notnull() & df["metadata_price"].notnull(),
|
||||
"_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
|
||||
""
|
||||
)
|
||||
df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
|
||||
return df
|
||||
|
||||
|
||||
def chunk_shared_data(interactions_df: pd.DataFrame,
|
||||
price_logs_df: pd.DataFrame,
|
||||
window_size: str = '30s',
|
||||
ts_col: str = 'ts') -> Tuple[List[Dict], List[Dict]]:
|
||||
"""
|
||||
Chunk interaction and price data into aligned time windows.
|
||||
|
||||
Args:
|
||||
interactions_df: interaction data with timestamp column
|
||||
price_logs_df: price log data with timestamp column
|
||||
window_size: pandas freq string ('30s', '1min', '1h', etc)
|
||||
ts_col: name of timestamp column
|
||||
|
||||
Returns:
|
||||
tuple of (interaction_chunks, price_chunks) where each is list of dicts:
|
||||
{
|
||||
'window_start': timestamp,
|
||||
'window_end': timestamp,
|
||||
'data': dataframe for this window
|
||||
}
|
||||
"""
|
||||
if interactions_df.empty and price_logs_df.empty:
|
||||
return [], []
|
||||
|
||||
# convert timestamps to datetime
|
||||
interactions_df = interactions_df.copy()
|
||||
price_logs_df = price_logs_df.copy()
|
||||
|
||||
if not interactions_df.empty:
|
||||
if not pd.api.types.is_datetime64_any_dtype(interactions_df[ts_col]):
|
||||
interactions_df[ts_col] = pd.to_datetime(interactions_df[ts_col])
|
||||
|
||||
if not price_logs_df.empty:
|
||||
if not pd.api.types.is_datetime64_any_dtype(price_logs_df[ts_col]):
|
||||
price_logs_df[ts_col] = pd.to_datetime(price_logs_df[ts_col])
|
||||
|
||||
# find global time bounds
|
||||
times = []
|
||||
if not interactions_df.empty:
|
||||
times.extend([interactions_df[ts_col].min(), interactions_df[ts_col].max()])
|
||||
if not price_logs_df.empty:
|
||||
times.extend([price_logs_df[ts_col].min(), price_logs_df[ts_col].max()])
|
||||
|
||||
if not times:
|
||||
return [], []
|
||||
|
||||
earliest = min(times)
|
||||
latest = max(times)
|
||||
|
||||
# create shared time windows
|
||||
windows = pd.date_range(start=earliest, end=latest, freq=window_size)
|
||||
|
||||
if len(windows) < 2:
|
||||
return [], []
|
||||
|
||||
# chunk both datasets
|
||||
interaction_chunks = []
|
||||
price_chunks = []
|
||||
|
||||
for i in range(len(windows) - 1):
|
||||
window_start = windows[i]
|
||||
window_end = windows[i + 1]
|
||||
|
||||
# filter interactions in this window
|
||||
if not interactions_df.empty:
|
||||
mask = (interactions_df[ts_col] >= window_start) & (interactions_df[ts_col] < window_end)
|
||||
interaction_chunk = interactions_df[mask]
|
||||
else:
|
||||
interaction_chunk = pd.DataFrame()
|
||||
|
||||
interaction_chunks.append({
|
||||
'window_start': window_start,
|
||||
'window_end': window_end,
|
||||
'data': interaction_chunk
|
||||
})
|
||||
|
||||
# filter price logs in this window
|
||||
if not price_logs_df.empty:
|
||||
mask = (price_logs_df[ts_col] >= window_start) & (price_logs_df[ts_col] < window_end)
|
||||
price_chunk = price_logs_df[mask]
|
||||
else:
|
||||
price_chunk = pd.DataFrame()
|
||||
|
||||
price_chunks.append({
|
||||
'window_start': window_start,
|
||||
'window_end': window_end,
|
||||
'data': price_chunk
|
||||
})
|
||||
|
||||
return interaction_chunks, price_chunks
|
||||
if __name__ == "__main__":
|
||||
df = extract()
|
||||
print(df.head())
|
||||
print(df.tail())
|
||||
print(df.info())
|
||||
|
||||
@@ -1,90 +1,15 @@
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import pandas as pd
|
||||
import logging
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
from extract import KafkaDataFetcher, ExperimentJoiner, EventTitleAugmenter, chunk_shared_data
|
||||
from extract import DataExtractor
|
||||
from mapping import SessionTransitionProbMatrixTransformer, render_graph
|
||||
from demand import DemandEstimator, ChunkInteractionsIntoSteps
|
||||
from elasticity import TemporalElasticityEstimator, aggregate_price_logs
|
||||
|
||||
|
||||
|
||||
# elasticity pipeline components (not sklearn compatible, manual orchestration)
|
||||
def elasticity_pipeline(interactions_df, price_logs_df, window_size='30s', store_mode='hotel'):
|
||||
"""
|
||||
Compute price elasticity from interaction and price data.
|
||||
|
||||
Args:
|
||||
interactions_df: raw interaction data from demand_data_pipeline
|
||||
price_logs_df: price log data from price_data_pipeline
|
||||
window_size: time window for chunking
|
||||
store_mode: 'hotel' or 'airline'
|
||||
|
||||
Returns:
|
||||
df with [productId, elasticity, std_error, n_obs]
|
||||
"""
|
||||
# step 1: chunk interactions into time windows
|
||||
chunker = ChunkInteractionsIntoSteps(window_size=window_size, return_metadata=True)
|
||||
interaction_chunks = chunker.transform(interactions_df)
|
||||
log.info(f"Chunked interactions into {len(interaction_chunks)} windows of size {window_size}")
|
||||
|
||||
if not interaction_chunks:
|
||||
return None
|
||||
|
||||
# step 2: compute demand per window
|
||||
demand_estimator = DemandEstimator(store_mode=store_mode)
|
||||
demand_chunks = []
|
||||
for chunk in interaction_chunks:
|
||||
demand_vector = demand_estimator.transform(chunk['data'])
|
||||
demand_chunks.append({
|
||||
'window_start': chunk['window_start'],
|
||||
'window_end': chunk['window_end'],
|
||||
'demand_vector': demand_vector # each has a full list of all products, even if demand is 0
|
||||
})
|
||||
# [q_chunk1, q_chunk2, ...]
|
||||
|
||||
# step 3: aggregate price logs into windows
|
||||
price_chunks = aggregate_price_logs(price_logs_df, window_size=window_size)
|
||||
|
||||
# step 4: compute elasticity
|
||||
elasticity_estimator = TemporalElasticityEstimator(method='point', min_observations=2)
|
||||
elasticity_df = elasticity_estimator.transform(demand_chunks, price_chunks, store_mode=store_mode)
|
||||
|
||||
return elasticity_df
|
||||
|
||||
|
||||
# exposable pipelines
|
||||
interaction_pipeline = Pipeline([
|
||||
('kafka_fetch', KafkaDataFetcher(topic='user-interactions')),
|
||||
('experiment_join', ExperimentJoiner()),
|
||||
('event_augment', EventTitleAugmenter()),
|
||||
])
|
||||
|
||||
price_data_pipeline = Pipeline([
|
||||
('kafka_fetch', KafkaDataFetcher(topic='price-logs')),
|
||||
])
|
||||
|
||||
# interaction_data + price_data -> elasticity (demand)
|
||||
# elasticity -> pricing
|
||||
|
||||
pricing_pipeline = Pipeline([
|
||||
('demand_estimation', DemandEstimator()),
|
||||
])
|
||||
if __name__ == "__main__":
|
||||
# fetch both datasets
|
||||
interaction_data = interaction_pipeline.fit_transform(None)
|
||||
pricing_data = price_data_pipeline.fit_transform(None)
|
||||
if interaction_data.empty or pricing_data.empty:
|
||||
print("Insufficient data for elasticity computation"); exit(0)
|
||||
# compute elasticity via unified pipeline
|
||||
window_size = "30s"
|
||||
elasticity_results = elasticity_pipeline(interaction_data, pricing_data, window_size=window_size)
|
||||
elasticity_value_array = elasticity_results['elasticity'].values if elasticity_results is not None else np.array([])
|
||||
print(elasticity_value_array)
|
||||
|
||||
if elasticity_results is not None and not elasticity_results.empty:
|
||||
print(elasticity_results.to_string(index=False))
|
||||
else:
|
||||
print("\nInsufficient data for elasticity computation")
|
||||
steps = [
|
||||
('data_extraction', DataExtractor()),
|
||||
#('transition_matrix', SessionTransitionProbMatrixTransformer(threshold=0.05)),
|
||||
]
|
||||
pipeline = Pipeline(steps)
|
||||
result = pipeline.fit_transform(None)
|
||||
print(result)
|
||||
print(result.info())
|
||||
|
||||
@@ -1,153 +0,0 @@
|
||||
r"""
|
||||
Our state space comes as:
|
||||
$Q_t in R^n$ - our demand at a time t
|
||||
$P_t in R^n$ - prices at time t
|
||||
$S_t$ some form of interaction session features
|
||||
|
||||
This is a single sate which we map under
|
||||
|
||||
$f: (Q, S, H) \to P_{t+1}$
|
||||
|
||||
With:
|
||||
|
||||
$H_t = \{Q_{t-k}, P_{t-k}, S_{t-k}\}$
|
||||
|
||||
|
||||
We can have f be literally anything, analytical or learned or rule based or an RL policy.
|
||||
|
||||
Our goal is to mazimize the expected revenue:
|
||||
|
||||
$E[R_T] = E[\sum_{t=1}^T P_t^T \dot Q_t]$
|
||||
|
||||
subject to Q_t = g(P_t, S_t) : demand response to price (estimated via elasticity) and P_t ≥ C : prices above cost floor and additionally minimizing the following:
|
||||
|
||||
$L_{agent} = R_{oracle} - R_{observed}
|
||||
|
||||
where: R_oracle = revenue if we knew agent intentions (from recon session) and R_observed = revenue under current pricing policy f
|
||||
|
||||
I would start be defning a pricing function interface and standardizing how to train that based on historical data and define how to make it behave for online training (if we do that)
|
||||
|
||||
We also need to develop a solid benchmark with mapping revenue and full KPIs from session interactions to measure differences between different price learning methods
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from sklearn.base import BaseEstimator, TransformerMixin
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import os
|
||||
from supabase import create_client, Client
|
||||
from pipeline import interaction_pipeline, price_data_pipeline, elasticity_pipeline
|
||||
|
||||
SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
|
||||
SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
|
||||
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
|
||||
|
||||
def expected_revenue(prices: np.ndarray, demand: np.ndarray) -> float:
|
||||
"""Returns: expected revenue R_t = P_t^T * Q_t"""
|
||||
return float(np.dot(prices, demand))
|
||||
|
||||
class StateSpace:
|
||||
def __init__(self,
|
||||
demand : np.ndarray, # at time t, only values (assuming aligned by productId order)
|
||||
prices : np.ndarray, # at time t, only values (assuming aligned by productId order)
|
||||
session_features : pd.DataFrame):
|
||||
self.demand = demand # Q_t
|
||||
self.prices = prices # P_t
|
||||
self.session_features = session_features # S_t
|
||||
self.history = [] # H_t
|
||||
|
||||
class PricingFunction(BaseEstimator, TransformerMixin, ABC):
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def fit(self, historical_data):
|
||||
"""
|
||||
Train the pricing function based on historical data.
|
||||
historical_data: list of StateSpace instances with known outcomes
|
||||
"""
|
||||
raise NotImplementedError("Train method must be implemented by subclass.")
|
||||
|
||||
def transform(self, state_space) -> np.ndarray:
|
||||
"""
|
||||
Predict the next prices given the current state space.
|
||||
state_space: StateSpace instance
|
||||
Returns: predicted prices P_{t+1}
|
||||
"""
|
||||
raise NotImplementedError("Predict method must be implemented by subclass.")
|
||||
|
||||
|
||||
class SimpleLinearPricingFunction(PricingFunction):
|
||||
def __init__(self, price_sensitivity: float = -0.1):
|
||||
super().__init__()
|
||||
self.price_sensitivity = price_sensitivity # simple coefficient
|
||||
|
||||
def fit(self, historical_data):
|
||||
return self
|
||||
|
||||
def transform(self, state_space: StateSpace) -> np.ndarray:
|
||||
# Simple linear adjustment: P_{t+1} = P_t + sensitivity * Q_t
|
||||
new_prices = state_space.prices + self.price_sensitivity * state_space.demand # this is not great
|
||||
return np.maximum(new_prices, 0)
|
||||
|
||||
# Example usage:
|
||||
if __name__ == "__main__":
|
||||
store_mode = 'hotel'
|
||||
interaction_data = interaction_pipeline.fit_transform(None)
|
||||
price_data = price_data_pipeline.fit_transform(None)
|
||||
|
||||
elasticity_df = elasticity_pipeline(interaction_data, price_data, window_size="30s", store_mode=store_mode)
|
||||
|
||||
# fetch all products with base prices from database
|
||||
products_resp = supabase.table(f'{store_mode}_products').select("id, metadata").execute()
|
||||
products_df = pd.DataFrame(products_resp.data)
|
||||
|
||||
# extract base_price from metadata
|
||||
products_df['base_price'] = products_df['metadata'].apply(lambda m: m.get('base_price', 0) if isinstance(m, dict) else 0)
|
||||
products_df = products_df.rename(columns={'id': 'productId'})[['productId', 'base_price']]
|
||||
|
||||
# override with logged prices where available
|
||||
if not price_data.empty:
|
||||
if 'ts' in price_data.columns and not pd.api.types.is_datetime64_any_dtype(price_data['ts']):
|
||||
price_data['ts'] = pd.to_datetime(price_data['ts'])
|
||||
|
||||
# get latest logged price per product
|
||||
price_logs_agg = price_data.sort_values('ts').groupby('productId', as_index=False).last()
|
||||
|
||||
# merge: start with all products (base prices), override with logged prices
|
||||
products_df = products_df.merge(
|
||||
price_logs_agg[['productId', 'price']],
|
||||
on='productId',
|
||||
how='left'
|
||||
)
|
||||
products_df['final_price'] = products_df['price'].fillna(products_df['base_price'])
|
||||
else:
|
||||
products_df['final_price'] = products_df['base_price']
|
||||
|
||||
# merge with elasticity
|
||||
if elasticity_df is not None and not elasticity_df.empty:
|
||||
price_data_merged = products_df[['productId', 'final_price']].merge(
|
||||
elasticity_df[['productId', 'elasticity']],
|
||||
on='productId',
|
||||
how='left'
|
||||
).fillna({'elasticity': 0.0})
|
||||
|
||||
prices = price_data_merged['final_price'].values
|
||||
elasticities = price_data_merged['elasticity'].values
|
||||
else:
|
||||
prices = np.array([])
|
||||
elasticities = np.array([])
|
||||
|
||||
print(elasticities)
|
||||
print(prices)
|
||||
|
||||
state_space = StateSpace(
|
||||
demand=elasticities,
|
||||
prices=prices,
|
||||
session_features=interaction_data
|
||||
)
|
||||
|
||||
pricing_function = SimpleLinearPricingFunction(price_sensitivity=-0.05)
|
||||
pricing_function.fit([]) # No training data for simple model
|
||||
predicted_prices = pricing_function.transform(state_space)
|
||||
|
||||
print("Predicted Prices:", predicted_prices)
|
||||
@@ -1,125 +0,0 @@
|
||||
import random
|
||||
import json
|
||||
import os
|
||||
import logging
|
||||
from dotenv import load_dotenv
|
||||
from supabase import create_client, Client
|
||||
from tqdm import tqdm
|
||||
|
||||
load_dotenv()
|
||||
|
||||
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL")
|
||||
SUPABASE_SERVICE_KEY = os.getenv("SUPABASE_SERVICE_ROLE_KEY")
|
||||
|
||||
if not SUPABASE_SERVICE_KEY:
|
||||
log.error("SUPABASE_SERVICE_ROLE_KEY not found in environment")
|
||||
raise ValueError("Missing SUPABASE_SERVICE_ROLE_KEY - required for admin operations")
|
||||
|
||||
supabase: Client = create_client(SUPABASE_URL, SUPABASE_SERVICE_KEY)
|
||||
|
||||
DAYS = 14
|
||||
|
||||
# hotel room configurations
|
||||
ROOMS = {
|
||||
"Presidential Suite": {'amenities': ['ocean_view', 'balcony', 'jacuzzi', 'butler_service', 'premium_minibar'], 'total': 1, 'image_url': "", "base_price": 450, 'name': 'Presidential Suite', 'refundable': True, 'max_occupancy': 4},
|
||||
"Executive Suite": {'amenities': ['city_view', 'balcony', 'workspace', 'lounge_access'], 'total': 2, 'image_url': "", "base_price": 280, 'name': 'Executive Suite', 'refundable': True, 'max_occupancy': 3},
|
||||
"Junior Suite": {'amenities': ['garden_view', 'mini_fridge', 'coffee_maker'], 'total': 5, 'image_url': "", "base_price": 180, 'name': 'Junior Suite', 'refundable': True, 'max_occupancy': 2},
|
||||
"Deluxe Room": {'amenities': ['city_view', 'work_desk', 'coffee_maker'], 'total': 8, 'image_url': "", "base_price": 140, 'name': 'Deluxe Room', 'refundable': False, 'max_occupancy': 2},
|
||||
"Superior Room": {'amenities': ['wifi', 'tv', 'safe'], 'total': 12, 'image_url': "", "base_price": 110, 'name': 'Superior Room', 'refundable': False, 'max_occupancy': 2},
|
||||
"Standard Room": {'amenities': ['wifi', 'tv'], 'total': 20, 'image_url': "", "base_price": 85, 'name': 'Standard Room', 'refundable': False, 'max_occupancy': 2},
|
||||
}
|
||||
|
||||
# flight configurations
|
||||
FLIGHTS = {
|
||||
"JFK-LAX-Economy": {'departure': {'time': '08:00', 'airport': 'JFK'}, 'arrival': {'time': '11:30', 'airport': 'LAX'}, 'duration': '5h 30m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'standard', 'refundable': False, 'total': 180, 'base_price': 250},
|
||||
"JFK-LAX-Business": {'departure': {'time': '08:00', 'airport': 'JFK'}, 'arrival': {'time': '11:30', 'airport': 'LAX'}, 'duration': '5h 30m', 'stops': 0, 'cabin_class': 'business', 'fare_rule': 'flexible', 'refundable': True, 'total': 30, 'base_price': 850},
|
||||
"ORD-MIA-Economy": {'departure': {'time': '14:15', 'airport': 'ORD'}, 'arrival': {'time': '18:45', 'airport': 'MIA'}, 'duration': '3h 30m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'basic', 'refundable': False, 'total': 200, 'base_price': 180},
|
||||
"SFO-SEA-Premium": {'departure': {'time': '06:30', 'airport': 'SFO'}, 'arrival': {'time': '08:45', 'airport': 'SEA'}, 'duration': '2h 15m', 'stops': 0, 'cabin_class': 'premium', 'fare_rule': 'standard', 'refundable': False, 'total': 60, 'base_price': 420},
|
||||
"ATL-DFW-First": {'departure': {'time': '16:00', 'airport': 'ATL'}, 'arrival': {'time': '17:30', 'airport': 'DFW'}, 'duration': '2h 30m', 'stops': 0, 'cabin_class': 'first', 'fare_rule': 'flexible', 'refundable': True, 'total': 12, 'base_price': 1600},
|
||||
"LAX-SFO-Economy": {'departure': {'time': '10:00', 'airport': 'LAX'}, 'arrival': {'time': '11:30', 'airport': 'SFO'}, 'duration': '1h 30m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'standard', 'refundable': False, 'total': 150, 'base_price': 120},
|
||||
"MIA-ATL-Premium": {'departure': {'time': '19:00', 'airport': 'MIA'}, 'arrival': {'time': '20:45', 'airport': 'ATL'}, 'duration': '1h 45m', 'stops': 0, 'cabin_class': 'premium', 'fare_rule': 'standard', 'refundable': True, 'total': 50, 'base_price': 380},
|
||||
"DFW-ORD-Economy": {'departure': {'time': '07:30', 'airport': 'DFW'}, 'arrival': {'time': '10:15', 'airport': 'ORD'}, 'duration': '2h 45m', 'stops': 0, 'cabin_class': 'economy', 'fare_rule': 'basic', 'refundable': False, 'total': 190, 'base_price': 160},
|
||||
"SEA-LAX-Business": {'departure': {'time': '13:00', 'airport': 'SEA'}, 'arrival': {'time': '15:30', 'airport': 'LAX'}, 'duration': '2h 30m', 'stops': 0, 'cabin_class': 'business', 'fare_rule': 'flexible', 'refundable': True, 'total': 40, 'base_price': 720},
|
||||
"LAX-JFK-First": {'departure': {'time': '18:00', 'airport': 'LAX'}, 'arrival': {'time': '02:15', 'airport': 'JFK'}, 'duration': '5h 15m', 'stops': 0, 'cabin_class': 'first', 'fare_rule': 'flexible', 'refundable': True, 'total': 16, 'base_price': 1850},
|
||||
}
|
||||
|
||||
def gen_hotel_products():
|
||||
"""generate hotel room products for next DAYS days"""
|
||||
data = []
|
||||
for day in range(DAYS):
|
||||
for room_type, rdata in ROOMS.items():
|
||||
data.append({
|
||||
'room_type': room_type,
|
||||
'date_index': day + 1,
|
||||
'metadata': rdata,
|
||||
'availability': random.randint(0, rdata['total'])
|
||||
})
|
||||
return data
|
||||
|
||||
def gen_airline_products():
|
||||
"""generate flight products for next DAYS days"""
|
||||
data = []
|
||||
for day in range(DAYS):
|
||||
for flight_type, fdata in FLIGHTS.items():
|
||||
data.append({
|
||||
'flight_type': flight_type,
|
||||
'date_index': day + 1,
|
||||
'metadata': fdata,
|
||||
'availability': random.randint(0, fdata['total'])
|
||||
})
|
||||
return data
|
||||
|
||||
def clear_table(table_name: str):
|
||||
"""clear all records from a table"""
|
||||
try:
|
||||
resp = supabase.table(table_name).select('id').execute()
|
||||
if resp.data:
|
||||
ids = [row['id'] for row in resp.data]
|
||||
chunk_size = 100
|
||||
for i in tqdm(range(0, len(ids), chunk_size), desc=f"Clearing {table_name}", unit="chunk"):
|
||||
chunk = ids[i:i+chunk_size]
|
||||
supabase.table(table_name).delete().in_('id', chunk).execute()
|
||||
log.info(f"Deleted {len(ids)} records from {table_name}")
|
||||
else:
|
||||
log.info(f"{table_name} already empty")
|
||||
except Exception as e:
|
||||
log.error(f"Failed to clear {table_name}: {e}")
|
||||
raise
|
||||
|
||||
def seed_table(table_name: str, data: list[dict]):
|
||||
"""insert records into a table"""
|
||||
try:
|
||||
chunk_size = 100
|
||||
total = len(data)
|
||||
for i in tqdm(range(0, total, chunk_size), desc=f"Seeding {table_name}", unit="chunk"):
|
||||
chunk = data[i:i+chunk_size]
|
||||
supabase.table(table_name).insert(chunk).execute()
|
||||
log.info(f"Inserted {total} records into {table_name}")
|
||||
except Exception as e:
|
||||
log.error(f"Failed to seed {table_name}: {e}")
|
||||
raise
|
||||
|
||||
def main():
|
||||
|
||||
log.info("Generating hotel products...")
|
||||
hotel_products = gen_hotel_products()
|
||||
log.info(f"Generated {len(hotel_products)} hotel products")
|
||||
|
||||
log.info("Generating airline products...")
|
||||
airline_products = gen_airline_products()
|
||||
log.info(f"Generated {len(airline_products)} airline products\n")
|
||||
|
||||
log.info("Clearing existing products...")
|
||||
clear_table('hotel_products')
|
||||
clear_table('airline_products')
|
||||
|
||||
log.info("Seeding products...")
|
||||
seed_table('hotel_products', hotel_products)
|
||||
seed_table('airline_products', airline_products)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -21,10 +21,7 @@ add_file() {
|
||||
# Add section header and code listing (no language-specific highlighting)
|
||||
echo "\\subsection{${escaped_path}}" >> "$OUTPUT_FILE"
|
||||
echo "\\begin{lstlisting}[caption={${escaped_path}}]" >> "$OUTPUT_FILE"
|
||||
# Convert to ASCII: transliterate what's possible, drop the rest
|
||||
# LC_ALL=C forces ASCII locale for consistent behavior across environments
|
||||
LC_ALL=C iconv -f UTF-8 -t ASCII//TRANSLIT//IGNORE "$filepath" 2>/dev/null >> "$OUTPUT_FILE" || \
|
||||
LC_ALL=C tr -cd '\11\12\15\40-\176' < "$filepath" >> "$OUTPUT_FILE"
|
||||
cat "$filepath" >> "$OUTPUT_FILE"
|
||||
echo "" >> "$OUTPUT_FILE"
|
||||
echo "\\end{lstlisting}" >> "$OUTPUT_FILE"
|
||||
echo "" >> "$OUTPUT_FILE"
|
||||
|
||||
@@ -1,106 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import { useState, useEffect } from 'react';
|
||||
import { useParams, useRouter } from 'next/navigation';
|
||||
import { Navigation } from '@/components/ui';
|
||||
import { useCart } from '@/contexts/CartContext';
|
||||
import AirlineDetails from '@/components/feats/airline/AirlineDetails';
|
||||
import { transformProduct, type Flight, type AirlineProduct } from '@/lib/airline-utils';
|
||||
import type { EventName } from '@/lib/events';
|
||||
|
||||
const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
|
||||
const e = new CustomEvent('definedInteraction', {
|
||||
detail: { eventName, productId, metadata },
|
||||
});
|
||||
document.dispatchEvent(e);
|
||||
};
|
||||
|
||||
export default function AirlineProductPage() {
|
||||
const params = useParams();
|
||||
const router = useRouter();
|
||||
const { addItem } = useCart();
|
||||
const [product, setProduct] = useState<Flight | null>(null);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
const [added, setAdded] = useState(false);
|
||||
|
||||
const productId = params.id as string;
|
||||
|
||||
useEffect(() => {
|
||||
const fetchProduct = async () => {
|
||||
try {
|
||||
const res = await fetch(`/api/products/${productId}`);
|
||||
if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
|
||||
const json = await res.json();
|
||||
const transformed = transformProduct(json.data as AirlineProduct);
|
||||
setProduct(transformed);
|
||||
|
||||
// fire learn_more_about_item event when product loads
|
||||
dispatchInteraction('learn_more_about_item', productId, {
|
||||
type: 'airline',
|
||||
dateIndex: transformed.dateIndex,
|
||||
flightType: transformed.flightType,
|
||||
});
|
||||
} catch (e) {
|
||||
setError(e instanceof Error ? e.message : 'Failed to load product');
|
||||
console.error('[FETCH_FLIGHT_ERROR]', e);
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
};
|
||||
fetchProduct();
|
||||
}, [productId]);
|
||||
|
||||
const handleAddToCart = () => {
|
||||
if (!product) return;
|
||||
|
||||
addItem({
|
||||
id: productId,
|
||||
type: 'airline',
|
||||
name: product.flightType,
|
||||
price: product.basePrice,
|
||||
metadata: {
|
||||
departure: product.departure,
|
||||
arrival: product.arrival,
|
||||
duration: product.duration,
|
||||
cabinClass: product.cabinClass,
|
||||
},
|
||||
dateIndex: product.dateIndex,
|
||||
});
|
||||
|
||||
dispatchInteraction('add_item_to_cart', productId, {
|
||||
type: 'airline',
|
||||
price: product.basePrice,
|
||||
});
|
||||
|
||||
setAdded(true);
|
||||
setTimeout(() => setAdded(false), 2000);
|
||||
};
|
||||
|
||||
return (
|
||||
<>
|
||||
<Navigation />
|
||||
<main className="max-w-4xl mx-auto px-4 py-8">
|
||||
{loading && <div className="text-center py-8">Loading flight details...</div>}
|
||||
{error && <div className="text-red-500 text-center py-8">{error}</div>}
|
||||
|
||||
{!loading && !error && product && (
|
||||
<>
|
||||
<button
|
||||
onClick={() => router.back()}
|
||||
className="mt-6 text-blue-600 hover:underline"
|
||||
>
|
||||
← Back to flights
|
||||
</button>
|
||||
<AirlineDetails
|
||||
product={product}
|
||||
onAddToCart={handleAddToCart}
|
||||
addedToCart={added}
|
||||
/>
|
||||
|
||||
</>
|
||||
)}
|
||||
</main>
|
||||
</>
|
||||
);
|
||||
}
|
||||
@@ -1,69 +1,73 @@
|
||||
'use client';
|
||||
|
||||
import { useState, useEffect, Suspense } from 'react';
|
||||
import { useSearchParams } from 'next/navigation';
|
||||
import { Navigation } from '@/components/ui';
|
||||
import AirlineCard from '@/components/feats/airline/AirlineCard';
|
||||
import { transformProduct, type Flight, type AirlineProduct } from '@/lib/airline-utils';
|
||||
|
||||
function FlightsList() {
|
||||
const searchParams = useSearchParams();
|
||||
const [flights, setFlights] = useState<Flight[]>([]);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
type CabinClass = 'economy' | 'premium' | 'business' | 'first';
|
||||
type FareRule = 'flexible' | 'standard' | 'basic';
|
||||
|
||||
useEffect(() => {
|
||||
const fetchFlights = async () => {
|
||||
try {
|
||||
const url = new URL('/api/products', window.location.origin);
|
||||
url.searchParams.set('type', 'airline');
|
||||
interface Flight {
|
||||
id: string;
|
||||
departure: { time: string; airport: string };
|
||||
arrival: { time: string; airport: string };
|
||||
duration: string;
|
||||
stops: number;
|
||||
cabinClass: CabinClass;
|
||||
fareRule: FareRule;
|
||||
refundable: boolean;
|
||||
basePrice: number;
|
||||
}
|
||||
|
||||
// forward all relevant search params to the API
|
||||
const params = ['dateIndex', 'origin', 'destination', 'tripType', 'adults', 'children', 'infants'];
|
||||
params.forEach(param => {
|
||||
const val = searchParams.get(param);
|
||||
if (val) url.searchParams.set(param, val);
|
||||
});
|
||||
const genRandomFlights = (): Flight[] => {
|
||||
const airports = ['JFK', 'LAX', 'ORD', 'ATL', 'DFW', 'SFO', 'SEA', 'MIA'];
|
||||
const cabins: CabinClass[] = ['economy', 'premium', 'business', 'first'];
|
||||
const fareRules: FareRule[] = ['flexible', 'standard', 'basic'];
|
||||
|
||||
const res = await fetch(url.toString());
|
||||
if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
|
||||
const json = await res.json();
|
||||
const transformed = json.data.map((p: AirlineProduct) => transformProduct(p));
|
||||
setFlights(transformed);
|
||||
} catch (e) {
|
||||
setError(e instanceof Error ? e.message : 'Failed to load products');
|
||||
console.error('[FETCH_ERROR]', e);
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
return Array.from({ length: 12 }, (_, i) => {
|
||||
const depHour = Math.floor(Math.random() * 24);
|
||||
const arrHour = (depHour + Math.floor(Math.random() * 6) + 2) % 24;
|
||||
const stops = Math.random() > 0.6 ? 0 : Math.floor(Math.random() * 2) + 1;
|
||||
const cabin = cabins[Math.floor(Math.random() * cabins.length)];
|
||||
const fareRule = fareRules[Math.floor(Math.random() * fareRules.length)];
|
||||
|
||||
const basePrice = Math.floor(
|
||||
(cabin === 'economy' ? 200 : cabin === 'premium' ? 400 : cabin === 'business' ? 800 : 1500) +
|
||||
Math.random() * 300
|
||||
);
|
||||
|
||||
return {
|
||||
id: `flt-${i}`,
|
||||
departure: {
|
||||
time: `${depHour.toString().padStart(2, '0')}:${Math.floor(Math.random() * 60).toString().padStart(2, '0')}`,
|
||||
airport: airports[Math.floor(Math.random() * airports.length)],
|
||||
},
|
||||
arrival: {
|
||||
time: `${arrHour.toString().padStart(2, '0')}:${Math.floor(Math.random() * 60).toString().padStart(2, '0')}`,
|
||||
airport: airports[Math.floor(Math.random() * airports.length)],
|
||||
},
|
||||
duration: `${Math.floor(Math.random() * 5) + 2}h ${Math.floor(Math.random() * 60)}m`,
|
||||
stops,
|
||||
cabinClass: cabin,
|
||||
fareRule,
|
||||
refundable: Math.random() > 0.7,
|
||||
basePrice,
|
||||
};
|
||||
fetchFlights();
|
||||
}, [searchParams]);
|
||||
});
|
||||
};
|
||||
|
||||
export default function AirlineProducts() {
|
||||
const flights = genRandomFlights();
|
||||
|
||||
return (
|
||||
<>
|
||||
<h1 className="text-3xl font-bold mb-6">Available Flights</h1>
|
||||
{loading && <div className="text-center py-8">Loading...</div>}
|
||||
{error && <div className="text-red-500 text-center py-8">{error}</div>}
|
||||
{!loading && !error && (
|
||||
<Navigation />
|
||||
<main className="max-w-7xl mx-auto px-4 py-8">
|
||||
<h1 className="text-3xl font-bold mb-6">Available Flights</h1>
|
||||
<div className="space-y-4">
|
||||
{flights.map((f) => (
|
||||
<AirlineCard key={f.id} flight={f} />
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
||||
export default function AirlineProducts() {
|
||||
return (
|
||||
<>
|
||||
<Navigation />
|
||||
<main className="max-w-7xl mx-auto px-4 py-8">
|
||||
<Suspense fallback={<div className="text-center py-8">Loading...</div>}>
|
||||
<FlightsList />
|
||||
</Suspense>
|
||||
</main>
|
||||
</>
|
||||
);
|
||||
|
||||
@@ -13,6 +13,17 @@ export async function GET(req: NextRequest) {
|
||||
const experimentId = searchParams.get('experimentId');
|
||||
const storeMode = process.env.NEXT_PUBLIC_STORE_MODE || 'shop';
|
||||
|
||||
// log in dev
|
||||
if (process.env.NODE_ENV === 'development') {
|
||||
console.log('[pricing-api]', {
|
||||
productId,
|
||||
sessionId,
|
||||
experimentId,
|
||||
storeMode,
|
||||
timestamp: new Date().toISOString(),
|
||||
});
|
||||
}
|
||||
|
||||
if (!productId) {
|
||||
return NextResponse.json(
|
||||
{ error: 'productId is required' },
|
||||
@@ -23,46 +34,11 @@ export async function GET(req: NextRequest) {
|
||||
// stub: call external pricing provider (random for now)
|
||||
const basePrice = 100 + Math.random() * 900; // 100-1000 range
|
||||
const price = Math.round(basePrice * 100) / 100;
|
||||
const timestamp = new Date().toISOString();
|
||||
|
||||
// log price to kafka for elasticity computation
|
||||
if (sessionId) {
|
||||
const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
|
||||
try {
|
||||
await fetch(`${backendUrl}/api/kafka/price-log`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
productId,
|
||||
price,
|
||||
sessionId,
|
||||
experimentId: experimentId || undefined,
|
||||
storeMode,
|
||||
ts: timestamp,
|
||||
}),
|
||||
});
|
||||
} catch (err) {
|
||||
console.error('[price-log-error]', err);
|
||||
// don't fail the pricing request if logging fails
|
||||
}
|
||||
}
|
||||
|
||||
// log in dev
|
||||
if (process.env.NODE_ENV === 'development') {
|
||||
console.log('[pricing-api]', {
|
||||
productId,
|
||||
sessionId,
|
||||
experimentId,
|
||||
storeMode,
|
||||
price,
|
||||
timestamp,
|
||||
});
|
||||
}
|
||||
|
||||
const response: PricingResponse = {
|
||||
price,
|
||||
currency: 'EUR',
|
||||
cachedAt: timestamp,
|
||||
cachedAt: new Date().toISOString(),
|
||||
};
|
||||
|
||||
return NextResponse.json(response);
|
||||
|
||||
@@ -1,35 +0,0 @@
|
||||
import { NextRequest, NextResponse } from 'next/server';
|
||||
|
||||
export async function GET(
|
||||
req: NextRequest,
|
||||
{ params }: { params: Promise<{ id: string }> }
|
||||
) {
|
||||
const { id } = await params;
|
||||
|
||||
if (!id) {
|
||||
return NextResponse.json(
|
||||
{ error: 'product id is required' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
try {
|
||||
const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
|
||||
const url = new URL(`${backendUrl}/api/products/${id}`);
|
||||
|
||||
const res = await fetch(url.toString());
|
||||
|
||||
if (!res.ok) {
|
||||
throw new Error(`Backend returned ${res.status}`);
|
||||
}
|
||||
|
||||
const data = await res.json();
|
||||
return NextResponse.json(data);
|
||||
} catch (error) {
|
||||
console.error('[PRODUCT_DETAIL_ERROR]', error);
|
||||
return NextResponse.json(
|
||||
{ error: 'Failed to fetch product details' },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -1,40 +0,0 @@
|
||||
import { NextRequest, NextResponse } from 'next/server';
|
||||
|
||||
export async function GET(req: NextRequest) {
|
||||
const { searchParams } = new URL(req.url);
|
||||
const type = searchParams.get('type');
|
||||
|
||||
if (!type || !['hotel', 'airline'].includes(type)) {
|
||||
return NextResponse.json(
|
||||
{ error: 'type parameter must be "hotel" or "airline"' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
try {
|
||||
const backendUrl = process.env.BACKEND_URL || 'http://localhost:5000';
|
||||
const url = new URL(`${backendUrl}/api/products/type/${type}`);
|
||||
|
||||
// forward all query params to backend (excluding 'type')
|
||||
searchParams.forEach((value, key) => {
|
||||
if (key !== 'type') {
|
||||
url.searchParams.set(key, value);
|
||||
}
|
||||
});
|
||||
|
||||
const res = await fetch(url.toString());
|
||||
|
||||
if (!res.ok) {
|
||||
throw new Error(`Backend returned ${res.status}`);
|
||||
}
|
||||
|
||||
const data = await res.json();
|
||||
return NextResponse.json(data);
|
||||
} catch (error) {
|
||||
console.error('[PRODUCTS_PROXY_ERROR]', error);
|
||||
return NextResponse.json(
|
||||
{ error: 'Failed to fetch products' },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -1,110 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import { Navigation } from '@/components/ui';
|
||||
import { useCart } from '@/contexts/CartContext';
|
||||
import type { EventName } from '@/lib/events';
|
||||
|
||||
const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
|
||||
const e = new CustomEvent('definedInteraction', {
|
||||
detail: { eventName, productId, metadata },
|
||||
});
|
||||
document.dispatchEvent(e);
|
||||
};
|
||||
|
||||
export default function CartPage() {
|
||||
const { items, removeItem, clearCart, itemCount } = useCart();
|
||||
|
||||
const handleRemove = (id: string, type: string) => {
|
||||
removeItem(id);
|
||||
dispatchInteraction('remove_item', id, { type });
|
||||
};
|
||||
let itemTypes = Array.from(new Set(items.map(item => item.type)))[0] || 'items';
|
||||
|
||||
|
||||
const total = items.reduce((sum, item) => sum + item.price, 0);
|
||||
|
||||
return (
|
||||
<>
|
||||
<Navigation />
|
||||
<main className="max-w-4xl mx-auto px-4 py-8">
|
||||
<div className="flex justify-between items-center mb-6">
|
||||
<h1 className="text-3xl font-bold">Shopping Cart</h1>
|
||||
{itemCount > 0 && (
|
||||
<button
|
||||
onClick={clearCart}
|
||||
className="text-sm text-red-600 hover:underline"
|
||||
>
|
||||
Clear cart
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{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>
|
||||
</div>
|
||||
) : (
|
||||
<>
|
||||
<div className="space-y-4 mb-8">
|
||||
{items.map(item => (
|
||||
<div
|
||||
key={item.id}
|
||||
className="flex justify-between items-start p-4 border rounded-lg hover:bg-gray-50"
|
||||
>
|
||||
<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>
|
||||
)}
|
||||
|
||||
{item.type === 'airline' && (
|
||||
<div className="text-sm text-gray-600">
|
||||
<p>{String(item.metadata.cabinClass)} Class</p>
|
||||
<p>{String((item.metadata.departure as any)?.airport)} → {String((item.metadata.arrival as any)?.airport)}</p>
|
||||
<p>Duration: {String(item.metadata.duration)}</p>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<div className="text-right ml-4">
|
||||
<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"
|
||||
>
|
||||
Remove
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
|
||||
<div className="border-t pt-4">
|
||||
<div className="flex justify-between items-center mb-4">
|
||||
<span className="text-xl font-semibold">Total</span>
|
||||
<span className="text-3xl font-bold">${total.toFixed(2)}</span>
|
||||
</div>
|
||||
<button
|
||||
onClick={() => dispatchInteraction('checkout_start', undefined, { total, itemCount })}
|
||||
className="w-full py-3 bg-blue-600 hover:bg-blue-700 text-white rounded-lg font-medium transition-colors"
|
||||
>
|
||||
Proceed to Checkout
|
||||
</button>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</main>
|
||||
</>
|
||||
);
|
||||
}
|
||||
@@ -1,106 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import { useState, useEffect } from 'react';
|
||||
import { useParams, useRouter } from 'next/navigation';
|
||||
import { Navigation } from '@/components/ui';
|
||||
import { useCart } from '@/contexts/CartContext';
|
||||
import HotelDetails from '@/components/feats/hotel/HotelDetails';
|
||||
import { transformProduct, type Hotel, type HotelProduct } from '@/lib/hotel-utils';
|
||||
import type { EventName } from '@/lib/events';
|
||||
|
||||
const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
|
||||
const e = new CustomEvent('definedInteraction', {
|
||||
detail: { eventName, productId, metadata },
|
||||
});
|
||||
document.dispatchEvent(e);
|
||||
};
|
||||
|
||||
export default function HotelProductPage() {
|
||||
const params = useParams();
|
||||
const router = useRouter();
|
||||
const { addItem } = useCart();
|
||||
const [product, setProduct] = useState<Hotel | null>(null);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
const [added, setAdded] = useState(false);
|
||||
|
||||
const productId = params.id as string;
|
||||
|
||||
useEffect(() => {
|
||||
const fetchProduct = async () => {
|
||||
try {
|
||||
const res = await fetch(`/api/products/${productId}`);
|
||||
if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
|
||||
const json = await res.json();
|
||||
const transformed = transformProduct(json.data as HotelProduct);
|
||||
setProduct(transformed);
|
||||
|
||||
// fire learn_more_about_item event when product loads
|
||||
dispatchInteraction('learn_more_about_item', productId, {
|
||||
type: 'hotel',
|
||||
dateIndex: transformed.dateIndex,
|
||||
roomType: transformed.roomType,
|
||||
});
|
||||
} catch (e) {
|
||||
setError(e instanceof Error ? e.message : 'Failed to load product');
|
||||
console.error('[FETCH_HOTEL_ERROR]', e);
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
};
|
||||
fetchProduct();
|
||||
}, [productId]);
|
||||
|
||||
const handleAddToCart = () => {
|
||||
if (!product) return;
|
||||
|
||||
addItem({
|
||||
id: productId,
|
||||
type: 'hotel',
|
||||
name: product.name,
|
||||
price: product.pricePerNight,
|
||||
metadata: {
|
||||
roomType: product.roomType,
|
||||
nights: product.nights,
|
||||
checkIn: product.checkIn,
|
||||
checkOut: product.checkOut,
|
||||
},
|
||||
dateIndex: product.dateIndex,
|
||||
});
|
||||
|
||||
dispatchInteraction('add_item_to_cart', productId, {
|
||||
type: 'hotel',
|
||||
price: product.pricePerNight,
|
||||
});
|
||||
|
||||
setAdded(true);
|
||||
setTimeout(() => setAdded(false), 2000);
|
||||
};
|
||||
|
||||
return (
|
||||
<>
|
||||
<Navigation />
|
||||
<main className="max-w-4xl mx-auto px-4 py-8">
|
||||
{loading && <div className="text-center py-8">Loading hotel details...</div>}
|
||||
{error && <div className="text-red-500 text-center py-8">{error}</div>}
|
||||
|
||||
{!loading && !error && product && (
|
||||
<>
|
||||
<button
|
||||
onClick={() => router.back()}
|
||||
className="mt-6 text-blue-600 hover:underline"
|
||||
>
|
||||
← Back to rooms
|
||||
</button>
|
||||
<HotelDetails
|
||||
product={product}
|
||||
onAddToCart={handleAddToCart}
|
||||
addedToCart={added}
|
||||
/>
|
||||
|
||||
</>
|
||||
)}
|
||||
</main>
|
||||
</>
|
||||
);
|
||||
}
|
||||
@@ -1,69 +1,74 @@
|
||||
'use client';
|
||||
|
||||
import { useState, useEffect, Suspense } from 'react';
|
||||
import { useSearchParams } from 'next/navigation';
|
||||
import { Navigation } from '@/components/ui';
|
||||
import HotelCard from '@/components/feats/hotel/HotelCard';
|
||||
import { transformProduct, type Hotel, type HotelProduct } from '@/lib/hotel-utils';
|
||||
|
||||
function RoomsList() {
|
||||
const searchParams = useSearchParams();
|
||||
const [rooms, setRooms] = useState<Hotel[]>([]);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
|
||||
useEffect(() => {
|
||||
const fetchRooms = async () => {
|
||||
try {
|
||||
const url = new URL('/api/products', window.location.origin);
|
||||
url.searchParams.set('type', 'hotel');
|
||||
|
||||
// forward all relevant search params to the API
|
||||
const params = ['dateIndex', 'destination', 'adults', 'rooms'];
|
||||
params.forEach(param => {
|
||||
const val = searchParams.get(param);
|
||||
if (val) url.searchParams.set(param, val);
|
||||
});
|
||||
|
||||
const res = await fetch(url.toString());
|
||||
if (!res.ok) throw new Error(`Failed to fetch: ${res.status}`);
|
||||
const json = await res.json();
|
||||
const transformed = json.data.map((p: HotelProduct) => transformProduct(p));
|
||||
setRooms(transformed);
|
||||
} catch (e) {
|
||||
setError(e instanceof Error ? e.message : 'Failed to load products');
|
||||
console.error('[FETCH_ERROR]', e);
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
};
|
||||
fetchRooms();
|
||||
}, [searchParams]);
|
||||
|
||||
return (
|
||||
<>
|
||||
<h1 className="text-3xl font-bold mb-6">Available Rooms</h1>
|
||||
{loading && <div className="text-center py-8">Loading...</div>}
|
||||
{error && <div className="text-red-500 text-center py-8">{error}</div>}
|
||||
{!loading && !error && (
|
||||
<div className="space-y-4">
|
||||
{rooms.map((r) => (
|
||||
<HotelCard key={r.id} hotel={r} />
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
interface Hotel {
|
||||
id: string;
|
||||
name: string;
|
||||
roomType: string;
|
||||
checkIn: string;
|
||||
checkOut: string;
|
||||
amenities: string[];
|
||||
refundable: boolean;
|
||||
pricePerNight: number;
|
||||
nights: number;
|
||||
}
|
||||
|
||||
const genRandomHotels = (): Hotel[] => {
|
||||
const names = [
|
||||
'Grand Plaza Hotel',
|
||||
'Seaside Resort',
|
||||
'Downtown Suites',
|
||||
'Mountain View Lodge',
|
||||
'City Center Inn',
|
||||
'Luxury Beach Resort',
|
||||
'Urban Boutique Hotel',
|
||||
'Garden View Hotel',
|
||||
];
|
||||
const roomTypes = ['Standard Room', 'Deluxe Room', 'Suite', 'Executive Suite', 'Premium Room'];
|
||||
const amenities = ['wifi', 'pool', 'gym', 'parking', 'breakfast', 'spa'];
|
||||
|
||||
return Array.from({ length: 10 }, (_, i) => {
|
||||
const nights = Math.floor(Math.random() * 5) + 1;
|
||||
const basePrice = Math.floor(80 + Math.random() * 220);
|
||||
const selectedAmenities = amenities
|
||||
.sort(() => Math.random() - 0.5)
|
||||
.slice(0, Math.floor(Math.random() * 3) + 2);
|
||||
|
||||
const today = new Date();
|
||||
const checkInDate = new Date(today);
|
||||
checkInDate.setDate(today.getDate() + Math.floor(Math.random() * 10));
|
||||
const checkOutDate = new Date(checkInDate);
|
||||
checkOutDate.setDate(checkInDate.getDate() + nights);
|
||||
|
||||
return {
|
||||
id: `htl-${i}`,
|
||||
name: names[i % names.length],
|
||||
roomType: roomTypes[Math.floor(Math.random() * roomTypes.length)],
|
||||
checkIn: checkInDate.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
|
||||
checkOut: checkOutDate.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
|
||||
amenities: selectedAmenities,
|
||||
refundable: Math.random() > 0.5,
|
||||
pricePerNight: basePrice,
|
||||
nights,
|
||||
};
|
||||
});
|
||||
};
|
||||
|
||||
export default function HotelProducts() {
|
||||
const hotels = genRandomHotels();
|
||||
|
||||
return (
|
||||
<>
|
||||
<Navigation />
|
||||
<main className="max-w-7xl mx-auto px-4 py-8">
|
||||
<Suspense fallback={<div className="text-center py-8">Loading...</div>}>
|
||||
<RoomsList />
|
||||
</Suspense>
|
||||
<h1 className="text-3xl font-bold mb-6">Available Hotels</h1>
|
||||
<div className="space-y-4">
|
||||
{hotels.map((h) => (
|
||||
<HotelCard key={h.id} hotel={h} />
|
||||
))}
|
||||
</div>
|
||||
</main>
|
||||
</>
|
||||
);
|
||||
|
||||
@@ -2,7 +2,6 @@ import type { Metadata } from "next";
|
||||
import { Geist, Geist_Mono } from "next/font/google";
|
||||
import "./globals.css";
|
||||
import { TrackingProvider } from "@/components/TrackingProvider";
|
||||
import { CartProvider } from "@/contexts/CartContext";
|
||||
|
||||
const geistSans = Geist({
|
||||
variable: "--font-geist-sans",
|
||||
@@ -29,9 +28,7 @@ export default function RootLayout({
|
||||
<body
|
||||
className={`${geistSans.variable} ${geistMono.variable} antialiased`}
|
||||
>
|
||||
<CartProvider>
|
||||
<TrackingProvider>{children}</TrackingProvider>
|
||||
</CartProvider>
|
||||
<TrackingProvider>{children}</TrackingProvider>
|
||||
</body>
|
||||
</html>
|
||||
);
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
'use client';
|
||||
|
||||
import type { EventName } from '@/lib/events';
|
||||
import type { Flight } from '@/lib/airline-utils';
|
||||
import { useHoverTracking } from '@/hooks/useHoverTracking';
|
||||
import PriceDisplay from '@/components/ui/PriceDisplay';
|
||||
|
||||
@@ -12,17 +11,32 @@ const dispatchInteraction = (eventName: EventName, productId?: string, metadata?
|
||||
document.dispatchEvent(e);
|
||||
};
|
||||
|
||||
type CabinClass = 'economy' | 'premium' | 'business' | 'first';
|
||||
type FareRule = 'flexible' | 'standard' | 'basic';
|
||||
|
||||
interface Flight {
|
||||
id: string;
|
||||
departure: { time: string; airport: string };
|
||||
arrival: { time: string; airport: string };
|
||||
duration: string;
|
||||
stops: number;
|
||||
cabinClass: CabinClass;
|
||||
fareRule: FareRule;
|
||||
refundable: boolean;
|
||||
basePrice: number;
|
||||
}
|
||||
|
||||
export default function AirlineCard({ flight }: { flight: Flight }) {
|
||||
const durationRef = useHoverTracking({
|
||||
eventName: 'hover_over_title',
|
||||
productId: flight.id,
|
||||
metadata: { elementText: flight.duration, dateIndex: flight.dateIndex },
|
||||
metadata: { elementText: flight.duration },
|
||||
});
|
||||
|
||||
const priceRef = useHoverTracking({
|
||||
eventName: 'hover_over_paragraph',
|
||||
productId: flight.id,
|
||||
metadata: { elementText: 'price', dateIndex: flight.dateIndex },
|
||||
metadata: { elementText: 'price' },
|
||||
});
|
||||
|
||||
const handleCardClick = () => {
|
||||
@@ -30,9 +44,7 @@ export default function AirlineCard({ flight }: { flight: Flight }) {
|
||||
cabinClass: flight.cabinClass,
|
||||
fareRule: flight.fareRule,
|
||||
price: flight.basePrice,
|
||||
dateIndex: flight.dateIndex,
|
||||
});
|
||||
window.location.href = `/airline/products/${flight.id}`;
|
||||
};
|
||||
|
||||
return (
|
||||
|
||||
@@ -1,75 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import type { Flight } from '@/lib/airline-utils';
|
||||
|
||||
interface AirlineDetailsProps {
|
||||
product: Flight;
|
||||
onAddToCart: () => void;
|
||||
addedToCart: boolean;
|
||||
}
|
||||
|
||||
export default function AirlineDetails({ product, onAddToCart, addedToCart }: AirlineDetailsProps) {
|
||||
return (
|
||||
<div className="w-full flex flex-col lg:flex-row gap-12 py-8">
|
||||
{/* Image Section */}
|
||||
<div className="w-full lg:w-1/3 bg-gray-100 rounded-lg aspect-square flex items-center justify-center shrink-0">
|
||||
<span className="text-gray-400 text-lg font-medium">Flight Image</span>
|
||||
</div>
|
||||
|
||||
{/* Details Section */}
|
||||
<div className="flex-1 flex flex-col">
|
||||
<div className="flex justify-between items-start border-b pb-6 mb-6">
|
||||
<div>
|
||||
<h1 className="text-3xl font-bold text-gray-900 mb-1">{product.flightType}</h1>
|
||||
<p className="text-lg text-gray-500">{product.cabinClass} Class</p>
|
||||
</div>
|
||||
<div className="text-right">
|
||||
<p className="text-4xl font-bold text-gray-900">${product.basePrice}</p>
|
||||
{product.refundable && (
|
||||
<span className="inline-block mt-2 px-3 py-1 bg-green-50 text-green-700 rounded-full text-xs font-medium">
|
||||
Refundable
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center justify-between mb-10">
|
||||
<div className="text-center min-w-[100px]">
|
||||
<p className="text-3xl font-bold text-gray-900">{product.departure.time}</p>
|
||||
<p className="text-sm text-gray-500 font-medium mt-1">{product.departure.airport}</p>
|
||||
</div>
|
||||
|
||||
<div className="flex-1 px-8 flex flex-col items-center">
|
||||
<p className="text-sm text-gray-500 mb-2">{product.duration}</p>
|
||||
<div className="w-full h-0.5 bg-gray-200 relative flex items-center justify-center">
|
||||
<div className="absolute w-3 h-3 bg-gray-400 rounded-full"></div>
|
||||
</div>
|
||||
<p className="text-sm text-gray-500 mt-2">
|
||||
{product.stops === 0 ? 'Nonstop' : `${product.stops} stop${product.stops > 1 ? 's' : ''}`}
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="text-center min-w-[100px]">
|
||||
<p className="text-3xl font-bold text-gray-900">{product.arrival.time}</p>
|
||||
<p className="text-sm text-gray-500 font-medium mt-1">{product.arrival.airport}</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="mt-auto flex items-center justify-between pt-6 border-t">
|
||||
<div className="text-gray-600">
|
||||
<span className="font-bold text-gray-900">{product.availability}</span> seats remaining
|
||||
<span className="mx-2">•</span>
|
||||
{product.fareRule}
|
||||
</div>
|
||||
<button
|
||||
onClick={onAddToCart}
|
||||
disabled={addedToCart}
|
||||
className="px-8 py-4 bg-black hover:bg-gray-800 disabled:bg-green-600 text-white rounded-lg text-lg font-medium transition-all min-w-[200px]"
|
||||
>
|
||||
{addedToCart ? 'In Cart' : 'Add to Cart'}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,9 +1,7 @@
|
||||
'use client';
|
||||
|
||||
import { useState, FormEvent } from 'react';
|
||||
import { useRouter } from 'next/navigation';
|
||||
import { Button, Label, Input, DateInput, RadioGroup, Dropdown, DropdownCounter } from '@/components/ui';
|
||||
import { dateToDaysFromToday } from '@/lib/airline-utils';
|
||||
|
||||
type TripType = 'roundtrip' | 'oneway' | 'multicity';
|
||||
|
||||
@@ -21,7 +19,6 @@ const LocationIcon = () => (
|
||||
);
|
||||
|
||||
export default function AirlineHero() {
|
||||
const router = useRouter();
|
||||
const [tripType, setTripType] = useState<TripType>('roundtrip');
|
||||
const [origin, setOrigin] = useState('');
|
||||
const [destination, setDestination] = useState('');
|
||||
@@ -31,23 +28,7 @@ export default function AirlineHero() {
|
||||
|
||||
const handleSearch = (e: FormEvent) => {
|
||||
e.preventDefault();
|
||||
const params = new URLSearchParams();
|
||||
|
||||
if (departDate) {
|
||||
const daysOffset = dateToDaysFromToday(departDate);
|
||||
params.set('dateIndex', daysOffset.toString());
|
||||
}
|
||||
|
||||
if (origin) params.set('origin', origin);
|
||||
if (destination) params.set('destination', destination);
|
||||
if (tripType !== 'roundtrip') params.set('tripType', tripType);
|
||||
if (returnDate && tripType === 'roundtrip') params.set('returnDate', returnDate);
|
||||
|
||||
params.set('adults', passengers.adults.toString());
|
||||
params.set('children', passengers.children.toString());
|
||||
params.set('infants', passengers.infants.toString());
|
||||
|
||||
router.push(`/airline/products?${params.toString()}`);
|
||||
console.log({ tripType, origin, destination, departDate, returnDate, passengers });
|
||||
};
|
||||
|
||||
const totalPax = passengers.adults + passengers.children + passengers.infants;
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
'use client';
|
||||
|
||||
import type { EventName } from '@/lib/events';
|
||||
import type { Hotel } from '@/lib/hotel-utils';
|
||||
import { useHoverTracking } from '@/hooks/useHoverTracking';
|
||||
import PriceDisplay from '@/components/ui/PriceDisplay';
|
||||
|
||||
@@ -12,6 +11,18 @@ const dispatchInteraction = (eventName: EventName, productId?: string, metadata?
|
||||
document.dispatchEvent(e);
|
||||
};
|
||||
|
||||
interface Hotel {
|
||||
id: string;
|
||||
name: string;
|
||||
roomType: string;
|
||||
checkIn: string;
|
||||
checkOut: string;
|
||||
amenities: string[];
|
||||
refundable: boolean;
|
||||
pricePerNight: number;
|
||||
nights: number;
|
||||
}
|
||||
|
||||
const AmenityIcon = ({ name }: { name: string }) => {
|
||||
const iconMap: Record<string, string> = {
|
||||
wifi: 'Wi-Fi',
|
||||
@@ -28,13 +39,13 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
|
||||
const titleRef = useHoverTracking({
|
||||
eventName: 'hover_over_title',
|
||||
productId: hotel.id,
|
||||
metadata: { elementText: hotel.name, dateIndex: hotel.dateIndex },
|
||||
metadata: { elementText: hotel.name },
|
||||
});
|
||||
|
||||
const priceRef = useHoverTracking({
|
||||
eventName: 'hover_over_paragraph',
|
||||
productId: hotel.id,
|
||||
metadata: { elementText: 'price', dateIndex: hotel.dateIndex },
|
||||
metadata: { elementText: 'price' },
|
||||
});
|
||||
|
||||
const handleCardClick = () => {
|
||||
@@ -42,9 +53,7 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
|
||||
roomType: hotel.roomType,
|
||||
price: hotel.pricePerNight,
|
||||
nights: hotel.nights,
|
||||
dateIndex: hotel.dateIndex,
|
||||
});
|
||||
window.location.href = `/hotel/products/${hotel.id}`;
|
||||
};
|
||||
|
||||
return (
|
||||
|
||||
@@ -1,74 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import type { Hotel } from '@/lib/hotel-utils';
|
||||
|
||||
interface HotelDetailsProps {
|
||||
product: Hotel;
|
||||
onAddToCart: () => void;
|
||||
addedToCart: boolean;
|
||||
}
|
||||
|
||||
export default function HotelDetails({ product, onAddToCart, addedToCart }: HotelDetailsProps) {
|
||||
return (
|
||||
<div className="w-full flex flex-col lg:flex-row gap-12 py-8">
|
||||
{/* Image Section - Larger and cleaner */}
|
||||
<div className="w-full lg:w-1/2 bg-gray-100 rounded-lg aspect-[4/3] flex items-center justify-center shrink-0">
|
||||
<span className="text-gray-400 text-lg font-medium">Hotel Image</span>
|
||||
</div>
|
||||
|
||||
{/* Details Section - Full height/width usage */}
|
||||
<div className="flex-1 flex flex-col">
|
||||
<div className="border-b pb-6 mb-6">
|
||||
<h1 className="text-4xl font-bold text-gray-900 mb-2">{product.name}</h1>
|
||||
<p className="text-xl text-gray-500">{product.roomType}</p>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-2 gap-8 mb-8">
|
||||
<div>
|
||||
<h3 className="text-sm font-semibold text-gray-900 uppercase tracking-wider mb-2">Check-in</h3>
|
||||
<p className="text-lg text-gray-700">{product.checkIn}</p>
|
||||
</div>
|
||||
<div>
|
||||
<h3 className="text-sm font-semibold text-gray-900 uppercase tracking-wider mb-2">Check-out</h3>
|
||||
<p className="text-lg text-gray-700">{product.checkOut}</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="mb-8">
|
||||
<h3 className="text-sm font-semibold text-gray-900 uppercase tracking-wider mb-3">Amenities</h3>
|
||||
<div className="flex flex-wrap gap-3">
|
||||
{product.amenities.map(a => (
|
||||
<span key={a} className="px-3 py-1.5 bg-gray-100 text-gray-700 rounded-md text-sm font-medium">
|
||||
{a}
|
||||
</span>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{product.refundable && (
|
||||
<div className="mb-8 p-4 bg-green-50 text-green-800 rounded-md inline-block">
|
||||
<span className="font-medium">Free cancellation available</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="mt-auto pt-6 border-t flex items-center justify-between">
|
||||
<div>
|
||||
<p className="text-sm text-gray-500 mb-1">Total for {product.nights} night{product.nights > 1 ? 's' : ''}</p>
|
||||
<div className="flex items-baseline gap-2">
|
||||
<span className="text-4xl font-bold text-gray-900">${product.pricePerNight * product.nights}</span>
|
||||
<span className="text-gray-500">/ {product.nights} nights</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<button
|
||||
onClick={onAddToCart}
|
||||
disabled={addedToCart}
|
||||
className="px-8 py-4 bg-black hover:bg-gray-800 disabled:bg-green-600 text-white rounded-lg text-lg font-medium transition-all min-w-[200px]"
|
||||
>
|
||||
{addedToCart ? 'In Cart' : 'Add to Cart'}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,9 +1,7 @@
|
||||
'use client';
|
||||
|
||||
import { useState, FormEvent } from 'react';
|
||||
import { useRouter } from 'next/navigation';
|
||||
import { Button, Label, Input, DateInput, Dropdown, DropdownCounter } from '@/components/ui';
|
||||
import { dateToDaysFromToday } from '@/lib/hotel-utils';
|
||||
|
||||
const LocationIcon = () => (
|
||||
<svg className="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
|
||||
@@ -13,25 +11,14 @@ const LocationIcon = () => (
|
||||
);
|
||||
|
||||
export default function HotelHero() {
|
||||
const router = useRouter();
|
||||
const [destination, setDestination] = useState('');
|
||||
const [checkIn, setCheckIn] = useState('');
|
||||
const [checkOut, setCheckOut] = useState('');
|
||||
const [guests, setGuests] = useState({ adults: 2, rooms: 1 });
|
||||
|
||||
const handleSearch = (e: FormEvent) => {
|
||||
e.preventDefault();
|
||||
const params = new URLSearchParams();
|
||||
|
||||
if (checkIn) {
|
||||
const daysOffset = dateToDaysFromToday(checkIn);
|
||||
params.set('dateIndex', daysOffset.toString());
|
||||
}
|
||||
|
||||
if (destination) params.set('destination', destination);
|
||||
params.set('adults', guests.adults.toString());
|
||||
params.set('rooms', guests.rooms.toString());
|
||||
|
||||
router.push(`/hotel/products?${params.toString()}`);
|
||||
console.log({ destination, checkIn, checkOut, guests });
|
||||
};
|
||||
|
||||
return (
|
||||
@@ -39,16 +26,16 @@ export default function HotelHero() {
|
||||
<div className="w-full max-w-4xl px-4">
|
||||
<div className="text-center mb-8">
|
||||
<h1 className="text-4xl md:text-5xl font-bold mb-4">
|
||||
Find your perfect room
|
||||
Find your perfect stay
|
||||
</h1>
|
||||
<p className="text-lg">
|
||||
Search rooms, compare prices, and book with confidence
|
||||
Search hotels, compare prices, and book with confidence
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<form onSubmit={handleSearch} className="search-form">
|
||||
<div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-4">
|
||||
<div>
|
||||
<div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-4 gap-4">
|
||||
<div className="sm:col-span-2">
|
||||
<Label htmlFor="destination">Where to?</Label>
|
||||
<Input
|
||||
type="text"
|
||||
@@ -62,7 +49,7 @@ export default function HotelHero() {
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<Label htmlFor="checkIn">Date (1 night stay)</Label>
|
||||
<Label htmlFor="checkIn">Check-in</Label>
|
||||
<DateInput
|
||||
id="checkIn"
|
||||
value={checkIn}
|
||||
@@ -72,27 +59,43 @@ export default function HotelHero() {
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<Label htmlFor="guests">Guests</Label>
|
||||
<Dropdown label={`${guests.adults} ${guests.adults === 1 ? 'adult' : 'adults'}`}>
|
||||
<Label htmlFor="checkOut">Check-out</Label>
|
||||
<DateInput
|
||||
id="checkOut"
|
||||
value={checkOut}
|
||||
onChange={(e) => setCheckOut(e.target.value)}
|
||||
required
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="sm:col-span-2 lg:col-span-4">
|
||||
<Label htmlFor="guests">Guests & Rooms</Label>
|
||||
<Dropdown label={`${guests.adults} ${guests.adults === 1 ? 'adult' : 'adults'}, ${guests.rooms} ${guests.rooms === 1 ? 'room' : 'rooms'}`}>
|
||||
<DropdownCounter
|
||||
label="Adults"
|
||||
value={guests.adults}
|
||||
min={1}
|
||||
onChange={(v) => setGuests({ ...guests, adults: v })}
|
||||
/>
|
||||
<DropdownCounter
|
||||
label="Rooms"
|
||||
value={guests.rooms}
|
||||
min={1}
|
||||
onChange={(v) => setGuests({ ...guests, rooms: v })}
|
||||
/>
|
||||
</Dropdown>
|
||||
</div>
|
||||
|
||||
<div className="sm:col-span-2 lg:col-span-3">
|
||||
<div className="sm:col-span-2 lg:col-span-4">
|
||||
<Button type="submit" fullWidth>
|
||||
Search Rooms
|
||||
Search Hotels
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
</form>
|
||||
|
||||
<div className="mt-6 text-center text-sm">
|
||||
<p>Over 2 million rooms worldwide · Best price guarantee · Free cancellation on most bookings</p>
|
||||
<p>Over 2 million hotels worldwide · Best price guarantee · Free cancellation on most bookings</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -1,76 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import { createContext, useContext, useState, useEffect, ReactNode } from 'react';
|
||||
|
||||
export interface CartItem {
|
||||
id: string;
|
||||
type: 'hotel' | 'airline';
|
||||
name: string;
|
||||
price: number;
|
||||
metadata: Record<string, unknown>;
|
||||
dateIndex: number;
|
||||
}
|
||||
|
||||
interface CartContextType {
|
||||
items: CartItem[];
|
||||
addItem: (item: CartItem) => void;
|
||||
removeItem: (id: string) => void;
|
||||
clearCart: () => void;
|
||||
itemCount: number;
|
||||
}
|
||||
|
||||
const CartContext = createContext<CartContextType | undefined>(undefined);
|
||||
|
||||
const CART_KEY = 'phantom_cart';
|
||||
|
||||
export const CartProvider = ({ children }: { children: ReactNode }) => {
|
||||
const [items, setItems] = useState<CartItem[]>([]);
|
||||
const [loaded, setLoaded] = useState(false);
|
||||
|
||||
// load cart from sessionStorage on mount
|
||||
useEffect(() => {
|
||||
const stored = sessionStorage.getItem(CART_KEY);
|
||||
if (stored) {
|
||||
try {
|
||||
setItems(JSON.parse(stored));
|
||||
} catch (e) {
|
||||
console.error('[CART_LOAD]', e);
|
||||
}
|
||||
}
|
||||
setLoaded(true);
|
||||
}, []);
|
||||
|
||||
// persist to sessionStorage whenever cart changes
|
||||
useEffect(() => {
|
||||
if (!loaded) return;
|
||||
sessionStorage.setItem(CART_KEY, JSON.stringify(items));
|
||||
}, [items, loaded]);
|
||||
|
||||
const addItem = (item: CartItem) => {
|
||||
setItems(prev => {
|
||||
// prevent duplicates
|
||||
if (prev.find(i => i.id === item.id)) return prev;
|
||||
return [...prev, item];
|
||||
});
|
||||
};
|
||||
|
||||
const removeItem = (id: string) => {
|
||||
setItems(prev => prev.filter(i => i.id !== id));
|
||||
};
|
||||
|
||||
const clearCart = () => {
|
||||
setItems([]);
|
||||
};
|
||||
|
||||
return (
|
||||
<CartContext.Provider value={{ items, addItem, removeItem, clearCart, itemCount: items.length }}>
|
||||
{children}
|
||||
</CartContext.Provider>
|
||||
);
|
||||
};
|
||||
|
||||
export const useCart = () => {
|
||||
const ctx = useContext(CartContext);
|
||||
if (!ctx) throw new Error('useCart must be used within CartProvider');
|
||||
return ctx;
|
||||
};
|
||||
@@ -1,75 +0,0 @@
|
||||
export interface AirlineProduct {
|
||||
id: string;
|
||||
flight_type: string;
|
||||
date_index: number;
|
||||
metadata: {
|
||||
departure: { time: string; airport: string };
|
||||
arrival: { time: string; airport: string };
|
||||
duration: string;
|
||||
stops: number;
|
||||
cabin_class: string;
|
||||
fare_rule: string;
|
||||
refundable: boolean;
|
||||
total?: number;
|
||||
base_price: number;
|
||||
};
|
||||
availability: number;
|
||||
}
|
||||
|
||||
export interface Flight {
|
||||
id: string;
|
||||
flightType: string;
|
||||
departure: { time: string; airport: string };
|
||||
arrival: { time: string; airport: string };
|
||||
duration: string;
|
||||
stops: number;
|
||||
cabinClass: string;
|
||||
fareRule: string;
|
||||
refundable: boolean;
|
||||
basePrice: number;
|
||||
dateIndex: number;
|
||||
availability: number;
|
||||
}
|
||||
|
||||
const EPOCH = new Date(0);
|
||||
|
||||
export const transformProduct = (p: AirlineProduct): Flight => {
|
||||
const { id, flight_type, date_index, metadata, availability } = p;
|
||||
|
||||
return {
|
||||
id,
|
||||
flightType: flight_type,
|
||||
departure: metadata.departure,
|
||||
arrival: metadata.arrival,
|
||||
duration: metadata.duration,
|
||||
stops: metadata.stops,
|
||||
cabinClass: metadata.cabin_class,
|
||||
fareRule: metadata.fare_rule,
|
||||
refundable: metadata.refundable,
|
||||
basePrice: metadata.base_price,
|
||||
dateIndex: date_index,
|
||||
availability,
|
||||
};
|
||||
};
|
||||
|
||||
// convert date string to days from today
|
||||
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);
|
||||
};
|
||||
@@ -1,71 +0,0 @@
|
||||
export interface HotelProduct {
|
||||
id: string;
|
||||
room_type: string;
|
||||
date_index: number;
|
||||
metadata: {
|
||||
amenities?: string[];
|
||||
total?: number;
|
||||
image_url?: string;
|
||||
base_price?: number;
|
||||
name?: string;
|
||||
refundable?: boolean;
|
||||
};
|
||||
availability: number;
|
||||
}
|
||||
|
||||
export interface Hotel {
|
||||
id: string;
|
||||
name: string;
|
||||
roomType: string;
|
||||
checkIn: string;
|
||||
checkOut: string;
|
||||
dateIndex: number;
|
||||
amenities: string[];
|
||||
refundable: boolean;
|
||||
pricePerNight: number;
|
||||
nights: number;
|
||||
}
|
||||
|
||||
const EPOCH = new Date(0);
|
||||
|
||||
export const transformProduct = (p: HotelProduct): Hotel => {
|
||||
const { id, room_type, date_index, metadata } = p;
|
||||
const checkIn = new Date(EPOCH.getTime() + date_index * 86400000);
|
||||
const nights = 1;
|
||||
const checkOut = new Date(checkIn.getTime() + nights * 86400000);
|
||||
|
||||
return {
|
||||
id,
|
||||
name: metadata?.name || room_type,
|
||||
roomType: room_type,
|
||||
checkIn: checkIn.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
|
||||
checkOut: checkOut.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }),
|
||||
dateIndex: date_index,
|
||||
amenities: metadata?.amenities || [],
|
||||
refundable: metadata?.refundable || false,
|
||||
pricePerNight: metadata?.base_price || 100,
|
||||
nights,
|
||||
};
|
||||
};
|
||||
|
||||
// convert date string to days from today
|
||||
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);
|
||||
};
|
||||
@@ -1,25 +0,0 @@
|
||||
import { HotelProduct, Hotel, transformProduct as transformHotel } from './hotel-utils';
|
||||
import { AirlineProduct, Flight, transformProduct as transformFlight } from './airline-utils';
|
||||
|
||||
export type Product = Hotel | Flight;
|
||||
export type ProductRaw = HotelProduct | AirlineProduct;
|
||||
|
||||
export const isHotelProduct = (p: ProductRaw): p is HotelProduct => {
|
||||
return 'room_type' in p;
|
||||
};
|
||||
|
||||
export const isAirlineProduct = (p: ProductRaw): p is AirlineProduct => {
|
||||
return 'flight_type' in p;
|
||||
};
|
||||
|
||||
export const transformProduct = (p: ProductRaw): Product => {
|
||||
if (isHotelProduct(p)) {
|
||||
return transformHotel(p);
|
||||
}
|
||||
return transformFlight(p);
|
||||
};
|
||||
|
||||
export const getProductType = (p: Product): 'hotel' | 'airline' => {
|
||||
if ('roomType' in p) return 'hotel';
|
||||
return 'airline';
|
||||
};
|
||||
@@ -11,7 +11,6 @@ export function proxy(req: NextRequest) {
|
||||
pathname.startsWith('/_next') ||
|
||||
pathname.startsWith('/static') ||
|
||||
pathname.startsWith('/start-task') ||
|
||||
pathname.startsWith('/cart') ||
|
||||
pathname.includes('.')
|
||||
// TODO: add robots.txt and sitemap.xml if needed here
|
||||
) {
|
||||
|
||||
Reference in New Issue
Block a user