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Author SHA1 Message Date
f7ec2e0f9d extra 2025-11-14 14:18:33 +01:00
2dc3dad0a5 my changes and draft 2025-11-14 11:44:18 +01:00
9bb6f842f4 topic auto create 2025-11-13 18:41:37 +01:00
53a39b07dd prod kafka server logging 2025-11-13 18:27:36 +01:00
4acfb019f8 fixing prod 2025-11-13 18:21:07 +01:00
Daniel Alves Rösel
37b2099ee0 2 nextjs scaffold with store mode shop and admin session experiment wiring event emission v1 (#17)
* chore: cleaning gitignore

* formating and env documentation

* feat: context switching of hotel/airline depndent on env var via middleware

* fixed alignment and building

* wrong file

* prods

* fixed applying style

* better session cookie management

* tentative session storage with maybe using airtable

* migrated api of ingestion

* events and products apge

* fixing build

* 13 create outline for research paper draft (#18)

* updated outline for paper from issue

* extra paper sections and some formalization of series data

* algorithms and acknowledgements

* updated outline for paper from issue

* upadted text formating

* event unification

* refactor tracking to ues callbacks instead of refs

* implement a pricing display api with session passing

* moved middleware to proxy according to new changes in Nextjs

* refactoed kafka ingestion to go via backend not web-db

* Refactor docker-compose services to use individual Dockerfiles (#20)

* Initial plan

* Refactor services into individual Dockerfiles

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* Add EXPOSE directives to all Dockerfiles with port documentation

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* fixing small bugs and adding exepriments to tracking

* added some doc
2025-11-13 18:07:27 +01:00
Copilot
7ece6e82cb Refactor docker-compose services to use individual Dockerfiles (#20)
* Initial plan

* Refactor services into individual Dockerfiles

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* Add EXPOSE directives to all Dockerfiles with port documentation

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>
2025-11-12 16:11:50 +01:00
6b7060450c updated outline for paper from issue 2025-11-07 14:46:02 +01:00
Daniel Alves Rösel
f6e780fdf1 13 create outline for research paper draft (#18)
* updated outline for paper from issue

* extra paper sections and some formalization of series data

* algorithms and acknowledgements
2025-11-07 14:39:59 +01:00
Daniel Alves Rösel
f427943ebc Merge pull request #16 from velocitatem/15-define-the-color-and-style-scheme-for-the-hotel-and-airline-platforms
15 define the color and style scheme for the hotel and airline platforms
2025-11-06 14:18:17 +01:00
64 changed files with 3229 additions and 462 deletions

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@@ -1,5 +1,18 @@
HOSTNAME=localhost
# Network configuration
HOSTNAME=localhost # hostname for service discovery across docker network
# PORTS
KAFKA_PORT=9092
REDIS_PORT=6377
# Application configuration
STORE_MODE=hotel # platform mode: 'hotel' or 'airline' - determines product catalog and UI theme
NEXT_PUBLIC_API_BASE=http://localhost:3000 # base URL for API endpoints, must be valid URL format
NEXT_PUBLIC_APP_ENV=dev # application environment: 'dev' or 'prod' - controls logging, error handling
NEXT_PUBLIC_HOVER_THRESHOLD=1200 # hover threshold in milliseconds for UI interactions
# Backend service
BACKEND_URL=http://localhost:5000 # backend API URL for kafka ingestion (set to railway service URL in prod)
# Service ports - used by docker-compose and service communication
BACKEND_PORT=5000 # backend server port for kafka ingestion API
KAFKA_HOST=localhost # kafka broker hostname - set to remote host in prod (e.g., kafka.example.com)
KAFKA_PORT=9092 # kafka broker port for event streaming
REDIS_PORT=6377 # redis port for worker queue and caching
REDPANDA_CONSOLE_PORT=8084 # redpanda console UI port for kafka monitoring

6
.gitignore vendored
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@@ -1,2 +1,6 @@
**/.env
**/.venv
**/.venv
PHANTOM.wiki/
**/.virtual_documents/
**/__pycache__/
**/.ipynb_checkpoints/

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@@ -1 +1,5 @@
[![Build PDF](https://github.com/velocitatem/PHANTOM/actions/workflows/latex.yml/badge.svg)](https://github.com/velocitatem/PHANTOM/actions/workflows/latex.yml)
- https://phantom-hotel.vercel.app/
- https://phantom-airline.vercel.app/

137
backend/server/app.py Normal file
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@@ -0,0 +1,137 @@
# boilerplate code
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional, Any
import uvicorn
import os
import json
from datetime import datetime
from kafka import KafkaProducer, KafkaAdminClient
from kafka.admin import NewTopic
from kafka.errors import TopicAlreadyExistsError
from dotenv import load_dotenv
load_dotenv()
app = FastAPI()
# kafka producer - lazy init
_producer: Optional[KafkaProducer] = None
def get_producer() -> KafkaProducer:
global _producer
if _producer is None:
host = os.getenv('KAFKA_HOST', 'localhost')
port = os.getenv('KAFKA_PORT', '29092') # use internal broker port
broker = f'{host}:{port}' if port else host
print(f"[KAFKA_INIT] Connecting to broker: {broker}")
_producer = KafkaProducer(
bootstrap_servers=[broker],
value_serializer=lambda v: json.dumps(v).encode('utf-8'),
key_serializer=lambda k: k.encode('utf-8') if k else None,
acks=1,
retries=3,
max_in_flight_requests_per_connection=5,
request_timeout_ms=30000,
api_version_auto_timeout_ms=10000,
max_block_ms=5000, # don't block send() for more than 5s
)
print(f"[KAFKA_INIT] Producer created successfully")
return _producer
class EventPayload(BaseModel):
sessionId: str
eventName: str
page: str
productId: Optional[str] = None
metadata: Optional[dict[str, Any]] = None
storeMode: str
userAgent: Optional[str] = None
ts: Optional[str] = None
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.on_event("startup")
async def startup_event():
"""create kafka topics on startup"""
host = os.getenv('KAFKA_HOST', 'localhost')
port = os.getenv('KAFKA_PORT', '29092')
broker = f'{host}:{port}'
try:
print(f"[STARTUP] Creating Kafka topics on {broker}")
admin = KafkaAdminClient(
bootstrap_servers=[broker],
request_timeout_ms=10000,
)
topics = [
NewTopic(name='user-interactions', num_partitions=3, replication_factor=1)
]
admin.create_topics(new_topics=topics, validate_only=False)
print(f"[STARTUP] Topics created successfully")
admin.close()
except TopicAlreadyExistsError:
print(f"[STARTUP] Topics already exist, skipping creation")
except Exception as e:
print(f"[STARTUP] Failed to create topics: {e}")
print(f"[STARTUP] Will rely on auto-creation on first message")
@app.get("/health")
async def health():
kafka_status = "unknown"
try:
producer = get_producer()
# attempt to get cluster metadata to verify connection
producer.bootstrap_connected()
kafka_status = "connected"
except Exception as e:
kafka_status = f"error: {str(e)}"
return {
"status": "healthy",
"kafka": kafka_status,
"kafka_broker": f"{os.getenv('KAFKA_HOST', 'localhost')}:{os.getenv('KAFKA_PORT', '9092')}"
}
@app.post("/api/kafka/ingest")
async def ingest_logs(event: EventPayload):
try:
if not event.ts:
event.ts = datetime.utcnow().isoformat() + 'Z'
producer = get_producer()
future = producer.send(
'user-interactions',
key=event.sessionId,
value=event.model_dump()
)
# add callback for error logging but don't block
future.add_errback(lambda e: print(f"[KAFKA_SEND_ERROR] {e}"))
return {"success": True}
except Exception as e:
import traceback
print(f"[ERROR] {e}")
print(traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/kafka/dump")
def dump_logs():
# TODO: implement a dump of logs of time period t_start to t_end (params of get)
# OR: allow for params of last_n logs as a param - creating two modes of the dumping
pass
if __name__ == "__main__":
PORT=int(os.getenv("BACKEND_PORT", 5000))
uvicorn.run("server:app", host="0.0.0.0", port=PORT, reload=True)

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@@ -0,0 +1,5 @@
fastapi==0.104.1
uvicorn[standard]==0.24.0
kafka-python==2.0.2
pydantic==2.5.0
python-dotenv==1.0.0

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@@ -1,15 +1,34 @@
services:
backend:
container_name: "PHANTOM-backend"
build:
context: .
dockerfile: docker/backend.Dockerfile
ports:
- "${BACKEND_PORT:-5000}:5000"
environment:
- KAFKA_HOST=kafka
- KAFKA_PORT=29092
depends_on:
- kafka
restart: unless-stopped
redis:
container_name: "PHANTOM-redis"
image: redis:7-alpine
build:
context: ./docker
dockerfile: Redis.dockerfile
ports:
- "${REDIS_PORT:-6378}:6379"
volumes:
- phantom_redis_data:/data
restart: unless-stopped
zookeeper:
container_name: "PHANTOM-zookeeper"
image: confluentinc/cp-zookeeper:latest
build:
context: ./docker
dockerfile: Zookeeper.dockerfile
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ports:
@@ -17,7 +36,9 @@ services:
kafka:
container_name: "PHANTOM-kafka"
image: confluentinc/cp-kafka:7.5.0
build:
context: ./docker
dockerfile: Kafka.dockerfile
depends_on:
- zookeeper
environment:
@@ -36,7 +57,9 @@ services:
redpanda-console:
container_name: "PHANTOM-redpanda-console"
image: docker.redpanda.com/redpandadata/console:latest
build:
context: ./docker
dockerfile: RedpandaConsole.dockerfile
depends_on:
- kafka
environment:

7
docker/Kafka.dockerfile Normal file
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@@ -0,0 +1,7 @@
FROM confluentinc/cp-kafka:7.5.0
# Expose Kafka ports
# 9092: External client connections
# 29092: Internal broker communication
# 9999: JMX monitoring port
EXPOSE 9092 29092 9999

4
docker/Redis.dockerfile Normal file
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@@ -0,0 +1,4 @@
FROM redis:7-alpine
# Expose Redis port
EXPOSE 6379

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@@ -0,0 +1,4 @@
FROM docker.redpanda.com/redpandadata/console:latest
# Expose Redpanda Console web UI port
EXPOSE 8080

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@@ -0,0 +1,4 @@
FROM confluentinc/cp-zookeeper:latest
# Expose Zookeeper client port
EXPOSE 2181

12
docker/backend.Dockerfile Normal file
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@@ -0,0 +1,12 @@
FROM python:3.11-slim
WORKDIR /app
COPY backend/server/requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY backend/server/app.py .
EXPOSE 5000
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "5000"]

File diff suppressed because it is too large Load Diff

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@@ -6,14 +6,15 @@
(setq TeX-command-extra-options
"-file-line-error -interaction=nonstopmode")
(TeX-add-to-alist 'LaTeX-provided-class-options
'(("report" "12pt") ("article" "12pt") ("acmart" "sigconf" "nonacm")))
'(("report" "12pt") ("article" "12pt") ("acmart" "sigconf" "nonacm" "natbib=false")))
(TeX-run-style-hooks
"latex2e"
"preamble"
"chapters/01-intro"
"acmart"
"acmart10")
(TeX-add-symbols
'("footnotetextcopyrightpermission" 1)))
'("footnotetextcopyrightpermission" 1))
(LaTeX-add-labels
"research"))
:latex)

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@@ -0,0 +1,106 @@
@techReport{,
abstract = {We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known. We consider two instances of this problem: i.) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and ii.) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function "on the fly," and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is "close" to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function; manifested as the revenue loss due to model uncertainty.},
author = {Omar Besbes and Assaf Zeevi},
journal = {Operations Research},
keywords = {Revenue management,asymptotic analysis,estimation,exploration-exploitation,learning,pricing,value of information},
title = {Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms *}
}
@misc{Ghaffary,
author = {Shirin Ghaffary and Matt Day},
note = {Updated 2025-11-05},
title = {Amazon Sues to Stop Perplexity From Using AI Tool to Buy Stuff},
url = {https://www.bloomberg.com/news/articles/2025-11-04/amazon-demands-perplexity-stop-ai-agent-from-making-purchases}
}
@phdthesis{,
abstract = {Algorithmic pricing is an emerging business practice that uses computational algorithms to determine
the prices of products and services based on a number of dynamic factors. The aim of this thesis is to
draw attention to the existence of these business practices, and the ethical and social implications that
derive from them, and then focus on what could be effective solutions to increase the well-being of
the community.
In Chapter 2 of the thesis, a general introduction to the topic will be made, starting from its history
and its evolution over the years; Chapter 3 will examine the different types of pricing algorithms.
Subsequently, in Chapter 4 we will analyze the sectors in which they are most applicable, and the
relative advantages and disadvantages they bring with them, with a critical analysis of the trade-offs
generated. The effect of algorithmic pricing on competition will be studied, considering how the
ability of algorithms to adapt quickly to market conditions can foster anti-competitive practices, such
as price discrimination. Later, in Chapter 5, we will look at the issue of price transparency and how
the opacity of algorithms can make it difficult for consumers to understand the pricing process and
assess whether they are receiving fair treatment.
To address these ethical issues, several possible solutions will be brought to light, described in
Chapter 6, which will focus on the role of the government, as a regulatory, of the end consumer, who
must be encouraged to educate and inform himself about the use of these practices, and of the
company, as responsible for making its customers aware and acting in compliance with government
laws, for fair and non-discriminatory use.},
author = {Fabio Salassa and Paolo Pautassi},
school = {Politecnico di Torino},
title = {Politecnico di Torino Algorithmic Pricing in the digital age "Ethical considerations on its economic and social implications, and an analysis of possible solutions to overcome its critical issues" Tutor: Candidate},
url = {https://webthesis.biblio.polito.it/secure/31375/1/tesi.pdf}
}
@inproceedings{Mueller2019,
author = {Jonas W Mueller and Vasilis Syrgkanis and Matt Taddy},
booktitle = {Advances in Neural Information Processing Systems 32 (NeurIPS 2019)},
pages = {15442-15452},
title = {Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing},
url = {https://proceedings.neurips.cc/paper/2019/file/0a3df70393993583a13c0dd6686f3f32-Paper.pdf},
year = {2019}
}
@article{Amjad2017,
abstract = { In this paper, the question of interest is estimating true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. To address this question, we introduce a %non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of "matrix completion" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [7], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to 0 as the number of store locations and time intervals increase to $\infty$. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach. },
author = {Muhammad J. Amjad and Devavrat Shah},
doi = {10.1145/3154489},
issue = {2},
journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
month = {12},
pages = {1-28},
publisher = {Association for Computing Machinery (ACM)},
title = {Censored Demand Estimation in Retail},
volume = {1},
url = {https://par.nsf.gov/servlets/purl/10066022},
year = {2017}
}
@article{Prez-Ricardo2025,
abstract = {The study aims to explore tourists' booking intentions by analyzing the price elasticity of demand in tourist accommodations. This analysis should reveal how changes in price affect booking behavior across different customer segments, using online booking records. A dataset was compiled from 106 hotels in Malaga, Spain, comprising 27,910 online bookings sourced exclusively from hotel websites. To understand the price elasticity of demand, a simple log-log regression was applied, segmenting the data based on key revenue-related variables. Subsequently, a cluster segmentation was performed using the Elbow method and K-means algorithm to identify distinct market segments. The findings highlighted that Family Travelers and Short Stay Travelers segments exhibited elastic demand, indicating higher sensitivity to price fluctuations. In contrast, Early Bookers and Mid-Season Long Stayers demonstrated inelastic demand, with lower responsiveness to changes in tourist accommodation prices. The number of variables analyzed in this study, along with the cluster analysis, represent a novelty and contribute to the existing literature on market segmentation and price elasticity of demand. This integration enriches both fields of research, offering mutual benefits and deeper insights that enhance the understanding of booking intention and pricing strategies.},
author = {Elizabeth del Carmen Pérez-Ricardo and Josefa García-Mestanza},
doi = {10.1016/j.iedeen.2025.100271},
issn = {24448834},
issue = {1},
journal = {European Research on Management and Business Economics},
keywords = {Booking intention,Price elasticity,Tourist segmentation},
month = {1},
publisher = {European Academy of Management and Business Economics},
title = {Exploring booking intentions through price elasticity of demand in tourism accommodations using large-scale data analytics},
volume = {31},
year = {2025}
}
@article{Iliou2021,
author = {Christos Iliou and Theodoros Kostoulas and Theodora Tsikrika and Vasilis Katos and Stefanos Vrochidis and Ioannis Kompatsiaris},
doi = {10.1145/3447815},
issue = {3},
journal = {Digital Threats: Research and Practice},
pages = {1-26},
title = {Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics},
volume = {2},
url = {https://dl.acm.org/doi/10.1145/3447815},
year = {2021}
}
@article{ArnoudVdenBoer2015,
author = {Arnoud V. den Boer},
doi = {10.1016/j.sorms.2015.03.001},
issue = {1},
journal = {Surveys in Operations Research and Management Science},
month = {6},
pages = {1-18},
title = {Dynamic pricing and learning: Historical origins, current research, and new directions},
volume = {20},
url = {https://www.sciencedirect.com/science/article/pii/S1876735415000021},
year = {2015}
}
@article{Calvano2018,
author = {Emilio Calvano and Giacomo Calzolari and Vincenzo Denicolo and Sergio Pastorello},
doi = {10.2139/ssrn.3304991},
journal = {SSRN Electronic Journal},
title = {Artificial Intelligence, Algorithmic Pricing and Collusion},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304991},
year = {2018}
}

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@@ -6,5 +6,11 @@
%% \label{fig:example}
%% \end{figure}
\section{Know They Enemy}
To know how to overcome we need to
\section{Introduction}
Research Objectives and Contribution: What are we making, why and who should care?
\subsection{Motivation and Market Context}
Current market dynamics and trends of dynamic pricing and AI agents. Future projections of AI agents. Key stakeholders that are discussing this and reporting on it (Thales). Who is most affected
\subsection{Solution Space Overview}
Different approaches and perspectives, here also add a preview of what will be developed and explored in the lit review.

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@@ -0,0 +1,17 @@
\section{Literature Review}
\subsection{Foundational Concepts}
What is the taxonomy and definition of an agent and an actor in this case, a bit more about interaction models in sessions and about dynamic pricing algorithms.
\subsection{Problem Evidence and Market Impact}
Documented instances of agent-driven market disruptions - Quantitative evidence of pricing manipulation - Case studies from affected industries
\subsection{Theoretical Foundations: Economic Prallels}
Economic foundations: relating the problem to options pricing theory. Cost of Information (COI) concept and its relevance
\subsection{Landscape of Existing Work}
Previous efforts in adversarial computer use LLM agents, show how multi-faceted the whole problem is
Here we can show a market visualization (venn-like-diagram)

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@@ -0,0 +1,68 @@
\section{Methodology}
\subsection{Problem Formalization}
Mathematical formalization of agent-induced pricing distortions. Formal definition of potential loss mechanisms $\alpha D$
We consider a business across time during which we have an evolving vector $p_t \in \Re^N$ where $N$ is the number of products in our catalogue. our price vector is directly dependent on a demand function $q_t$ which we define as a linear method of a price elasticity matrix $B_t$. This is the same setup that Microsoft created in their research.
We gether interaction data from users interacting with a sample platform simulating a hotel/airline which generates interaction distributions $I_t = \{(p_t, q_t^\text{obs}, \pi_t)\}_{t=1}^T$
\subsection{Cost of Information Framework}
Mathematical demonstration and validation of the COI and citation backed evidence, and framework overview + show harm to user via other cost distortions. Maybe split into 3.2.1 (COI Theory) and 3.2.2 (Framework Design)
\subsection{System Architecture}
\begin{figure}[ht]
\centering
\begin{tikzpicture}[
node distance=1.5cm and 2.5cm,
box/.style={rectangle, draw, thick, minimum height=1cm, minimum width=3cm, align=center, fill=blue!10},
kafka/.style={rectangle, draw=orange, thick, minimum height=1cm, minimum width=3cm, align=center, fill=orange!15},
arrow/.style={thick,->,>=Stealth}
]
% Nodes
\node[box] (webapp) {Web Application \\ (Producer \& Consumer)};
\node[kafka, below=of webapp] (kafka) {Apache Kafka \\ Cluster};
\node[box, below=of kafka] (backend) {Backend Services / Microservices \\ (Producers and Consumers)};
% Connections
\draw[arrow] (webapp) to[out=210,in=150] node[above]{Publish} (kafka);
\draw[arrow] (kafka) to[out=50,in=330] node[below]{Consume} (webapp);
\draw[arrow] (backend) -- node[above]{Publish/Consume} (kafka);
% Optional: Kafka internal components
%\node[below=0.7cm of kafka, align=center] (topics) {Topics \\ Partitions};
% Optional background
\begin{scope}[on background layer]
\node[draw, rounded corners, fill=orange!5, fit=(kafka), inner sep=0.3cm] {};
\end{scope}
\end{tikzpicture}
\caption{Technical Diagram}
\end{figure}
High level overview of how it works
\subsection{Experimental Design}
Study methodology and approach. Data acquisition strategy. Defined objectives and success criteria. Observable metrics and KPIs
\subsection{Dynamic Pricing Algorithm Analysis}
Deep dive into how the algorithm works, different kinds and justification for chosen appraoches + agent impact modeling and quantification.
\subsection{Reinforcement Learning Formulation}
How do we define the state space, action space and reward function breakdown and algorithm benchmarking.
POSSIBLY: Expand into full subsections: 3.6.1 (State-Action Space), 3.6.2 (Reward Design), 3.6.3 (Benchmarking)
\begin{algorithm}[t]
\DontPrintSemicolon
\KwIn{stepsize $\eta$, smoothing $\delta$, rank $d$}
\For{$t=1$ \KwTo $T$}{
Sample $u_t$ on unit sphere; set $x_t^\prime=x_t+\delta u_t$\;
Set $p_t \gets U x_t^\prime$ and observe $q_t, R_t(p_t)$\;
$x_{t+1} \gets \Pi\_{\mathcal{X}}(x_t-\eta R_t(p_t) u_t)$\;
}
\caption{Online Pricing Optimization (template)}
\end{algorithm}

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@@ -0,0 +1,16 @@
\section{Results}
\subsection{Behavioral Analysis}
Include markov chains of transition matrices, compare distributions (look at Divergence metrics)
\subsection{Experimental Outcomes}
Align with defined objectives, show results and statistical significance (or not).
\subsection{Interpretation and Insights}
Inference from given patterns and show key findings.
\subsection{Anomalies}

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@@ -0,0 +1,9 @@
\section{Discussion}
\subsection{Risk Assessment and Limitations}
Acknowledge risks and constraints and data sizes.
\subsection{Implications of Findings}
Interpretation of results and altenrative scenarios with broader market implications.

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@@ -0,0 +1,8 @@
\section{Conclusion}
\subsection{Summary of contributions }
Restate the thesis and key findings with validation of research objectives.
\subsection{Future Works and Next Steps}
Identify the research gaps here and potential business implications and setup of business + Proposed extensions and a long term agenda.

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@@ -0,0 +1,3 @@
\section{Acknowledgements}
Eugene Bykovets, PhD - ETH

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@@ -10,7 +10,7 @@
\begin{document}
\title{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
\title{First Proposal: Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
\author{Daniel Rösel}
\email{daniel@alves.world}
@@ -34,13 +34,60 @@ The primary objective of this thesis is to develop and validate pricing heuristi
\maketitle
\input{chapters/01-intro}
\section{Preliminary literature review}
From very relevant news, the legal conflicts of agentic access to platforms have clearly indicated a need for prevention of secondary negative effects on ``legacy'' systems which power modern pricing systems \cite{Ghaffary}. Dynamic pricing algorithms rely on directly translating demand features $q$ to $\hat{p}$ new price assignments across a catalogue of products. This demand estimation does often take into account a small degree of error and noise from the data. However, adversarially introduced interactions, which are non-conducive to pricing optimization nor are a fully accurate representation of the driving human demand, have not been considered as part of the systems. Research such as \cite{Mueller2019} introduces very clear methodology for pricing algorithms backed by demand estimation for online pricing optimization which can be followed for proposing adjustments and improvements as highlighted in \ref{research}. Another often encountered demand distortion occurs through censored demand environments \cite{Amjad2017}.
Other efforts such as \cite{Calvano2018} explore ways of modeling the interactions between multiple pricing algorithms or agents which in an effort to maximize their reward drive the market to supra-competitive pricing which leaves the boundaries of the market equilibrium, creating a harmful effect on the customers by this process of algorithmic collusion. This harm can be directly translated to our setting where through interactions between two learners there is a potential of market destabilization.
\section{Research question or objective} \label{research}
\begin{quote}
How do agent-generated interactions contaminate demand functions in dynamic pricing algorithms, and how significantly does this contamination affect key performance indicators ($\Delta$)?
\end{quote}
The objectives are to gather data on how humans ($H$) and agents ($A$) interact with commerce platforms, and to identify the most reliable methodology for true demand estimation to fuel the dynamic pricing algorithm. This discrimination task can be accomplished through three distinct approaches:
\begin{enumerate}
\item \textbf{Explicit filtering approach:} Decompose pipeline components and employ an estimator $P(A|s)$ (where $s$ represents session interaction data) to explicitly filter agent-generated interactions from the processing stream.
\item \textbf{Learned transformation approach:} Utilize a learned transformation on the product demand feature $B$, where $B = B_H + B_A$, with the goal of deriving a more representative demand feature $B_\text{clean} = B_H + W_\epsilon B_A$ that appropriately weights agent contributions.
\item \textbf{Reinforcement learning approach:} Frame the problem as a reinforcement learning task where interactions are modeled as environmental components, guiding the algorithm to learn an appropriate pricing policy that implicitly accounts for genuine human demand ($B_H$).
\end{enumerate}
\section{Execution plan with approximate calendar}
This is a tentative execution plan for this research, keeping in mind a more agile approach rather than a waterfall-like set of goals and targets:
\begin{description}
\item[November 2024:] Complete platform deployment for data collection and observations (70\% complete). Implement user authentication system with magic link invites to enable participant enrollment.
\item[December 2024:] Gather initial interaction data and explore the separability of distributions between human and agentic interaction patterns. Begin testing online algorithms for session-based pricing optimizations.
\item[January 2025:] Conduct controlled experiments comparing human versus agent execution of identical tasks. Establish behavioral signature models and quantify contamination impact ($\Delta$). Develop and validate the explicit filtering approach using $P(A|s)$ estimator.
\item[February 2025:] Design and train the learned transformation model for demand feature adjustment ($B_\text{clean}$). Implement reinforcement learning framework and train pricing policy that implicitly accounts for genuine human demand.
\item[March 2025:] Conduct comparative evaluation across all three proposed approaches. Finalize experimental results and perform statistical analysis of revenue recovery and KPI improvements.
\item[April 2025:] Internal review, revisions, and thesis documentation finalization. Prepare for final submission.
\end{description}
\section{Desired measurable outcome or answer}
The first step is measuring how well we can separate human from agent session data. We can start with standard accuracy metrics as a baseline.
What really matters for the larger picture is the economic impact of accurate demand estimation. We measure this through revenue leakage and revenue recovery. For benchmarking, we need to compare scenarios under default pricing policies versus adjusted ones - this gives us lower and upper bounds for our performance.
Since we're also concerned with human-centric outcomes, we need to collect user friction ratings that compare more radical solutions (like CAPTCHAs) against minimal or no defenses.
\printbibliography
\clearpage
\onecolumn
\appendix
\input{../build/concatenated_code}
% \clearpage
% \onecolumn
% \appendix
\end{document}

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@@ -4,10 +4,12 @@
\usepackage{csquotes}
\usepackage{subcaption}
\usepackage{siunitx}
\usepackage{tikz}
\usepackage{listings}
\usepackage{xcolor}
\usepackage[ruled,vlined]{algorithm2e}
\usetikzlibrary{positioning, shapes, arrows.meta, fit, backgrounds}
\lstset{
basicstyle=\ttfamily\footnotesize,
breaklines=true,
@@ -18,7 +20,10 @@
commentstyle=\color{green!60!black},
stringstyle=\color{red},
showstringspaces=false,
captionpos=b
captionpos=b,
inputencoding=utf8,
extendedchars=true,
literate={·}{{\textperiodcentered}}1 {}{{\textminus}}1 {}{{---}}1 {}{{--}}1
}
% Use biblatex instead of natbib (acmart default)

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@@ -12,3 +12,86 @@ The webapp should serve under the / route the landing page which for both platfo
- /app will have (airline) and (hotel) children which each have a layout.tsx and page.tsx where /app also has a parent layout defining layout.tsx and globals.css for any shared styling to avoid repretition.
- /components/ is gonna have ui/ which defines things like Button, Card, DatePicker with generic definitions and any tracking or observation code. We then define feats/airline/ and feats/hotel/ as children of components with specific components like AirlineHero and HotelCard.
- in /styles/ we define airline.css and hotel.css to tailor accents and styling for each.
## How to Run
```sh
# install deps
npm install
# set store mode (hotel or airline)
export STORE_MODE=hotel
# run dev server
npm run dev
```
Server runs on `http://localhost:3000`
## Environment Variables
| Variable | Description | Default | Example |
|----------|-------------|---------|---------|
| `HOSTNAME` | Server hostname | `localhost` | `localhost` |
| `STORE_MODE` | Mode switch for platform | `hotel` | `hotel` or `airline` |
| `NEXT_PUBLIC_API_BASE` | Public API base URL | `http://localhost:3000` | `http://localhost:3000` |
| `NEXT_PUBLIC_APP_ENV` | Application environment | `dev` | `dev`, `prod` |
| `NEXT_PUBLIC_HOVER_THRESHOLD` | Hover dwell threshold (ms) | `1200` | `1200` |
| `BACKEND_URL` | Backend service URL | `http://localhost:5000` | `http://localhost:5000` |
## Routes
### Public Pages
- `/` — Landing page (mode-aware root)
- `/hotel` — Hotel mode landing
- `/hotel/products` — Hotel catalog
- `/airline` — Airline mode landing
- `/airline/products` — Flight catalog
- `/admin/experiments` — Experiment management UI
### API Routes
- `GET /api/session` — Fetch or create session, sets httpOnly cookie
- `GET /api/pricing?productId=X&sessionId=Y&experimentId=Z` — Get product price from provider
- `POST /api/ingest` — Ingest event to Kafka via backend
- `GET /api/admin/experiments` — List all experiments
- `POST /api/admin/experiments/start` — Start new experiment for session
- `POST /api/admin/experiments/stop` — Stop experiment by ID
## Event Catalog
All events are ingested via `POST /api/ingest` and follow the `EventBase` schema. Below are the 17 canonical events:
| Event Name | Category | Payload Example |
|------------|----------|-----------------|
| `session_start` | Session | `{ sessionId, experimentId?, storeMode, ts, page, eventName, userAgent? }` |
| `page_view` | Navigation | `{ sessionId, experimentId?, storeMode, ts, page: "/hotel", eventName: "page_view" }` |
| `view_item_page` | Discovery | `{ sessionId, storeMode, ts, page: "/hotel/products", productId: "H001", eventName: "view_item_page" }` |
| `learn_more_about_item` | Discovery | `{ sessionId, storeMode, ts, page, productId, eventName: "learn_more_about_item" }` |
| `add_item_to_cart` | Cart | `{ sessionId, storeMode, ts, page, productId, eventName: "add_item_to_cart" }` |
| `remove_item` | Cart | `{ sessionId, storeMode, ts, page, productId, eventName: "remove_item" }` |
| `checkout_start` | Cart | `{ sessionId, storeMode, ts, page, eventName: "checkout_start" }` |
| `purchase_complete` | Cart | `{ sessionId, storeMode, ts, page, eventName: "purchase_complete", metadata?: { total: 500 } }` |
| `search` | Filter/Search | `{ sessionId, storeMode, ts, page, eventName: "search", metadata: { query: "paris" } }` |
| `filter_for_date` | Filter/Search | `{ sessionId, storeMode, ts, page, eventName: "filter_for_date", metadata: { from: "2025-01-15", to: "2025-01-20" } }` |
| `filter_for_amenities` | Filter/Search | `{ sessionId, storeMode, ts, page, eventName: "filter_for_amenities", metadata: { amenities: ["wifi", "pool"] } }` |
| `filter_for_price` | Filter/Search | `{ sessionId, storeMode, ts, page, eventName: "filter_for_price", metadata: { min: 100, max: 500 } }` |
| `sort_change` | Filter/Search | `{ sessionId, storeMode, ts, page, eventName: "sort_change", metadata: { sort: "price_asc" } }` |
| `hover_over_title` | Dwell signal | `{ sessionId, storeMode, ts, page, productId?, eventName: "hover_over_title", metadata: { duration: 1500 } }` |
| `hover_over_paragraph` | Dwell signal | `{ sessionId, storeMode, ts, page, productId?, eventName: "hover_over_paragraph", metadata: { duration: 2000 } }` |
| `hover_over_link` | Dwell signal | `{ sessionId, storeMode, ts, page, productId?, eventName: "hover_over_link", metadata: { href: "/hotel/products" } }` |
| `hover_over_button` | Dwell signal | `{ sessionId, storeMode, ts, page, productId?, eventName: "hover_over_button", metadata: { label: "Book Now" } }` |
## Architecture
### Route Groups
- `(hotel)` — Hotel mode pages
- `(airline)` — Airline mode pages
- `api/*` — API routes (session, pricing, ingest, admin)
### Middleware Flow
1. Request arrives at Next.js
2. Session middleware checks for `phantom_session_id` cookie
3. If missing, `/api/session` mints new session + sets cookie
4. Store mode (`STORE_MODE` env) determines rendered page variant
5. Client-side components fetch pricing via `/api/pricing`
6. User interactions emit events to `/api/ingest` → Kafka

22
web/package-lock.json generated
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@@ -8,10 +8,10 @@
"name": "web",
"version": "0.1.0",
"dependencies": {
"kafkajs": "^2.2.4",
"next": "16.0.0",
"react": "19.2.0",
"react-dom": "19.2.0"
"react-dom": "19.2.0",
"zod": "^4.1.12"
},
"devDependencies": {
"@tailwindcss/postcss": "^4",
@@ -1041,15 +1041,6 @@
"jiti": "lib/jiti-cli.mjs"
}
},
"node_modules/kafkajs": {
"version": "2.2.4",
"resolved": "https://registry.npmjs.org/kafkajs/-/kafkajs-2.2.4.tgz",
"integrity": "sha512-j/YeapB1vfPT2iOIUn/vxdyKEuhuY2PxMBvf5JWux6iSaukAccrMtXEY/Lb7OvavDhOWME589bpLrEdnVHjfjA==",
"license": "MIT",
"engines": {
"node": ">=14.0.0"
}
},
"node_modules/lightningcss": {
"version": "1.30.2",
"resolved": "https://registry.npmjs.org/lightningcss/-/lightningcss-1.30.2.tgz",
@@ -1616,6 +1607,15 @@
"integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
"dev": true,
"license": "MIT"
},
"node_modules/zod": {
"version": "4.1.12",
"resolved": "https://registry.npmjs.org/zod/-/zod-4.1.12.tgz",
"integrity": "sha512-JInaHOamG8pt5+Ey8kGmdcAcg3OL9reK8ltczgHTAwNhMys/6ThXHityHxVV2p3fkw/c+MAvBHFVYHFZDmjMCQ==",
"license": "MIT",
"funding": {
"url": "https://github.com/sponsors/colinhacks"
}
}
}
}

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@@ -8,10 +8,10 @@
"start": "next start"
},
"dependencies": {
"kafkajs": "^2.2.4",
"next": "16.0.0",
"react": "19.2.0",
"react-dom": "19.2.0"
"react-dom": "19.2.0",
"zod": "^4.1.12"
},
"devDependencies": {
"@tailwindcss/postcss": "^4",

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@@ -0,0 +1,199 @@
'use client';
import { useEffect, useState } from 'react';
import { useSession } from '@/hooks/useSession';
type Experiment = {
id: string;
status: 'active' | 'stopped';
sessionIds: string[];
createdAt: number;
};
export default function ExperimentsAdmin() {
const { sessionId, isLoading: sessionLoading } = useSession();
const [exps, setExps] = useState<Experiment[]>([]);
const [loading, setLoading] = useState(false);
const [error, setError] = useState<string | null>(null);
const fetchExps = async () => {
try {
const res = await fetch('/api/admin/experiments');
if (!res.ok) throw new Error(`fetch failed: ${res.status}`);
const data = await res.json();
setExps(data.experiments || []);
} catch (err: any) {
setError(err.message);
}
};
useEffect(() => {
fetchExps();
}, []);
const handleStart = async () => {
if (!sessionId) {
setError('no session available');
return;
}
setLoading(true);
setError(null);
try {
const res = await fetch('/api/admin/experiments/start', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ sessionId }),
});
if (!res.ok) {
const data = await res.json();
throw new Error(data.error || 'start failed');
}
await fetchExps(); // refresh list
} catch (err: any) {
setError(err.message);
} finally {
setLoading(false);
}
};
const handleStop = async (expId: string) => {
setLoading(true);
setError(null);
try {
const res = await fetch('/api/admin/experiments/stop', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ experimentId: expId }),
});
if (!res.ok) {
const data = await res.json();
throw new Error(data.error || 'stop failed');
}
await fetchExps(); // refresh list
} catch (err: any) {
setError(err.message);
} finally {
setLoading(false);
}
};
if (sessionLoading) {
return (
<div className="flex min-h-screen items-center justify-center bg-zinc-50 dark:bg-black">
<p className="text-zinc-600 dark:text-zinc-400">loading session...</p>
</div>
);
}
return (
<div className="min-h-screen bg-zinc-50 px-6 py-12 dark:bg-black">
<div className="mx-auto max-w-5xl">
<div className="mb-8 flex items-center justify-between">
<div>
<h1 className="text-3xl font-semibold tracking-tight text-black dark:text-zinc-50">
Experiments
</h1>
<p className="mt-2 text-sm text-zinc-600 dark:text-zinc-400">
current session: {sessionId || 'none'}
</p>
</div>
<button
onClick={handleStart}
disabled={loading || !sessionId}
className="rounded-lg bg-black px-4 py-2 text-sm font-medium text-white transition-colors hover:bg-zinc-800 disabled:opacity-50 dark:bg-zinc-50 dark:text-black dark:hover:bg-zinc-200"
>
{loading ? 'starting...' : 'start experiment'}
</button>
</div>
{error && (
<div className="mb-4 rounded-lg bg-red-50 p-4 text-sm text-red-800 dark:bg-red-950 dark:text-red-200">
{error}
</div>
)}
<div className="overflow-hidden rounded-lg border border-zinc-200 bg-white dark:border-zinc-800 dark:bg-zinc-950">
<table className="w-full text-left text-sm">
<thead className="border-b border-zinc-200 bg-zinc-50 dark:border-zinc-800 dark:bg-zinc-900">
<tr>
<th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
experiment id
</th>
<th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
status
</th>
<th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
session count
</th>
<th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
created
</th>
<th className="px-6 py-3 font-medium text-zinc-900 dark:text-zinc-100">
action
</th>
</tr>
</thead>
<tbody className="divide-y divide-zinc-200 dark:divide-zinc-800">
{exps.length === 0 ? (
<tr>
<td
colSpan={5}
className="px-6 py-8 text-center text-zinc-500 dark:text-zinc-400"
>
no experiments yet
</td>
</tr>
) : (
exps.map((exp) => (
<tr
key={exp.id}
className="hover:bg-zinc-50 dark:hover:bg-zinc-900"
>
<td className="px-6 py-4 font-mono text-xs text-zinc-700 dark:text-zinc-300">
{exp.id.slice(0, 8)}...
</td>
<td className="px-6 py-4">
<span
className={`inline-block rounded-full px-2 py-1 text-xs font-medium ${
exp.status === 'active'
? 'bg-green-100 text-green-800 dark:bg-green-950 dark:text-green-200'
: 'bg-zinc-100 text-zinc-800 dark:bg-zinc-800 dark:text-zinc-200'
}`}
>
{exp.status}
</span>
</td>
<td className="px-6 py-4 text-zinc-700 dark:text-zinc-300">
{exp.sessionIds.length}
</td>
<td className="px-6 py-4 text-zinc-700 dark:text-zinc-300">
{new Date(exp.createdAt).toLocaleString()}
</td>
<td className="px-6 py-4">
{exp.status === 'active' && (
<button
onClick={() => handleStop(exp.id)}
disabled={loading}
className="text-sm font-medium text-red-600 hover:text-red-700 disabled:opacity-50 dark:text-red-400 dark:hover:text-red-300"
>
stop
</button>
)}
</td>
</tr>
))
)}
</tbody>
</table>
</div>
</div>
</div>
);
}

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@@ -0,0 +1,6 @@
import { ReactNode } from 'react';
import '@/styles/airline.css';
export default function AirlineLayout({ children }: { children: ReactNode }) {
return <div data-mode="airline">{children}</div>;
}

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@@ -0,0 +1,9 @@
import AirlineHero from '@/components/feats/airline/AirlineHero';
export default function AirlineHome() {
return (
<main>
<AirlineHero />
</main>
);
}

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@@ -0,0 +1,74 @@
'use client';
import { Navigation } from '@/components/ui';
import AirlineCard from '@/components/feats/airline/AirlineCard';
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;
}
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'];
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,
};
});
};
export default function AirlineProducts() {
const flights = genRandomFlights();
return (
<>
<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>
</main>
</>
);
}

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@@ -0,0 +1,15 @@
import { NextResponse } from 'next/server';
import { getAllExperiments } from '@/lib/sessionStore';
export async function GET() {
try {
const exps = getAllExperiments();
return NextResponse.json({ experiments: exps });
} catch (err: any) {
console.error('experiments list error:', err);
return NextResponse.json(
{ error: err.message || 'unknown error' },
{ status: 500 }
);
}
}

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@@ -0,0 +1,43 @@
import { NextRequest, NextResponse } from 'next/server';
import { randomUUID } from 'crypto';
import { createExperiment, getSession } from '@/lib/sessionStore';
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { sessionId } = body;
if (!sessionId) {
return NextResponse.json(
{ error: 'sessionId required' },
{ status: 400 }
);
}
// verify session exists
const session = getSession(sessionId);
if (!session) {
return NextResponse.json(
{ error: 'session not found' },
{ status: 404 }
);
}
// generate and create experiment
const experimentId = randomUUID();
const exp = createExperiment(sessionId, experimentId);
return NextResponse.json({
experimentId: exp.id,
sessionId,
status: exp.status,
createdAt: exp.createdAt,
});
} catch (err: any) {
console.error('experiment start error:', err);
return NextResponse.json(
{ error: err.message || 'unknown error' },
{ status: 500 }
);
}
}

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@@ -0,0 +1,39 @@
import { NextRequest, NextResponse } from 'next/server';
import { stopExperimentById, getExperiment } from '@/lib/sessionStore';
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { experimentId } = body;
if (!experimentId) {
return NextResponse.json(
{ error: 'experimentId required' },
{ status: 400 }
);
}
// verify experiment exists
const existing = getExperiment(experimentId);
if (!existing) {
return NextResponse.json(
{ error: 'experiment not found' },
{ status: 404 }
);
}
// stop the experiment
const exp = stopExperimentById(experimentId);
return NextResponse.json({
experimentId: exp!.id,
status: exp!.status,
});
} catch (err: any) {
console.error('experiment stop error:', err);
return NextResponse.json(
{ error: err.message || 'unknown error' },
{ status: 500 }
);
}
}

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@@ -0,0 +1,42 @@
import { NextRequest, NextResponse } from 'next/server';
import type { EventBase } from '@/lib/events';
const BACKEND_URL = process.env.BACKEND_URL || 'http://localhost:5000';
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const storeMode = process.env.STORE_MODE || 'hotel';
const userAgent = req.headers.get('user-agent') || undefined;
const event: EventBase = {
...body,
storeMode,
userAgent,
ts: body.ts || new Date().toISOString(),
};
const res = await fetch(`${BACKEND_URL}/api/kafka/ingest`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(event),
});
if (!res.ok) {
throw new Error(`Backend returned ${res.status}`);
}
if (process.env.NEXT_PUBLIC_APP_ENV === 'dev') {
console.log('[ingest]', event);
}
return NextResponse.json({ success: true });
} catch (err: any) {
console.error('[ingest error]', err);
return NextResponse.json(
{ error: err.message || 'unknown error' },
{ status: 500 }
);
}
}

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@@ -0,0 +1,45 @@
import { NextRequest, NextResponse } from 'next/server';
interface PricingResponse {
price: number;
currency: string;
cachedAt: string;
}
export async function GET(req: NextRequest) {
const { searchParams } = new URL(req.url);
const productId = searchParams.get('productId');
const sessionId = searchParams.get('sessionId');
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' },
{ status: 400 }
);
}
// 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 response: PricingResponse = {
price,
currency: 'EUR',
cachedAt: new Date().toISOString(),
};
return NextResponse.json(response);
}

View File

@@ -0,0 +1,46 @@
import { NextRequest, NextResponse } from 'next/server';
import { randomUUID } from 'crypto';
import { getSession, createSession } from '@/lib/sessionStore';
const COOKIE_NAME = 'phantom_session_id';
const isProd = process.env.NODE_ENV === 'production';
export async function GET(req: NextRequest) {
try {
// check for existing session cookie
const existingSession = req.cookies.get(COOKIE_NAME)?.value;
if (existingSession) {
const sessionData = getSession(existingSession);
return NextResponse.json({
sessionId: existingSession,
experimentId: sessionData?.experimentId,
});
}
// mint new session id
const sessionId = randomUUID();
createSession(sessionId);
const res = NextResponse.json({ sessionId, experimentId: undefined });
// set httpOnly cookie with security flags
res.cookies.set({
name: COOKIE_NAME,
value: sessionId,
httpOnly: true,
sameSite: 'lax',
secure: isProd,
path: '/',
maxAge: 60 * 60 * 24 * 30, // 30 days
});
return res;
} catch (err: any) {
console.error('session error:', err);
return NextResponse.json(
{ error: err.message || 'unknown error' },
{ status: 500 }
);
}
}

View File

@@ -1,33 +0,0 @@
import { NextRequest, NextResponse } from 'next/server';
import { sendInteractionEvent } from '@/lib/kafka';
export async function POST(req: NextRequest) {
try {
const body = await req.json();
const { sessionId, eventType, targetEl, targetUrl, metadata } = body;
if (!sessionId || !eventType) {
return NextResponse.json(
{ error: 'sessionId and eventType required' },
{ status: 400 }
);
}
await sendInteractionEvent({
sessionId,
eventType,
targetEl,
targetUrl,
metadata,
ts: Date.now(),
});
return NextResponse.json({ success: true });
} catch (err: any) {
console.error('track error:', err);
return NextResponse.json(
{ error: err.message || 'unknown error' },
{ status: 500 }
);
}
}

View File

@@ -1,5 +1,6 @@
@import "tailwindcss";
@layer base {
:root {
--background: #ffffff;
--foreground: #171717;
@@ -13,6 +14,7 @@
--border-radius: 8px;
--shadow-card: 0 2px 8px rgba(0, 0, 0, 0.1);
}
}
@theme inline {
--color-background: var(--background);
@@ -21,6 +23,7 @@
--font-mono: var(--font-geist-mono);
}
@layer base {
@media (prefers-color-scheme: dark) {
:root {
--background: #0a0a0a;
@@ -66,7 +69,9 @@ input, select, textarea {
font-size: 1rem;
outline: none;
}
}
@layer components {
.container {
max-width: 1200px;
margin: 0 auto;
@@ -86,13 +91,19 @@ input, select, textarea {
font-size: 1rem;
border-radius: var(--border-radius);
transition: all 0.2s ease;
background-color: #007aff;
color: #ffffff;
border: none;
cursor: pointer;
}
.btn-primary:hover {
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
background-color: #0051d5;
}
.section-spacing {
margin-bottom: var(--spacing-lg);
}
}

View File

@@ -0,0 +1,6 @@
import { ReactNode } from 'react';
import '@/styles/hotel.css';
export default function HotelLayout({ children }: { children: ReactNode }) {
return <div data-mode="hotel">{children}</div>;
}

View File

@@ -0,0 +1,9 @@
import HotelHero from '@/components/feats/hotel/HotelHero';
export default function HotelHome() {
return (
<main>
<HotelHero />
</main>
);
}

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@@ -0,0 +1,75 @@
'use client';
import { Navigation } from '@/components/ui';
import HotelCard from '@/components/feats/hotel/HotelCard';
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">
<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>
</>
);
}

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@@ -0,0 +1,87 @@
'use client';
import type { EventName } from '@/lib/events';
import { useHoverTracking } from '@/hooks/useHoverTracking';
import PriceDisplay from '@/components/ui/PriceDisplay';
const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
const e = new CustomEvent('definedInteraction', {
detail: { eventName, productId, 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 },
});
const priceRef = useHoverTracking({
eventName: 'hover_over_paragraph',
productId: flight.id,
metadata: { elementText: 'price' },
});
const handleCardClick = () => {
dispatchInteraction('view_item_page', flight.id, {
cabinClass: flight.cabinClass,
fareRule: flight.fareRule,
price: flight.basePrice,
});
};
return (
<div
className="flight-card cursor-pointer"
onClick={handleCardClick}
>
<div className="flight-timing">
<div className="flight-time">{flight.departure.time}</div>
<div className="flight-airport">{flight.departure.airport}</div>
</div>
<div className="flight-route">
<div ref={durationRef} className="flight-duration">{flight.duration}</div>
<div className="flight-stops">
{flight.stops === 0 ? 'Direct' : `${flight.stops} stop${flight.stops > 1 ? 's' : ''}`}
</div>
</div>
<div className="flight-timing">
<div className="flight-time">{flight.arrival.time}</div>
<div className="flight-airport">{flight.arrival.airport}</div>
</div>
<div className="flight-pricing">
<div className="fare-class capitalize mb-2">{flight.cabinClass}</div>
<div className="text-sm text-[var(--text-secondary)] mb-2 capitalize">{flight.fareRule}</div>
{flight.refundable && (
<div className="badge-value text-xs mb-2">Refundable</div>
)}
<div ref={priceRef}>
<PriceDisplay
productId={flight.id}
className="fare-price"
/>
</div>
</div>
</div>
);
}

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@@ -0,0 +1,156 @@
'use client';
import { useState, FormEvent } from 'react';
import { Button, Label, Input, DateInput, RadioGroup, Dropdown, DropdownCounter } from '@/components/ui';
type TripType = 'roundtrip' | 'oneway' | 'multicity';
const PlaneIcon = () => (
<svg className="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M12 19l9 2-9-18-9 18 9-2zm0 0v-8" />
</svg>
);
const LocationIcon = () => (
<svg className="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17.657 16.657L13.414 20.9a1.998 1.998 0 01-2.827 0l-4.244-4.243a8 8 0 1111.314 0z" />
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M15 11a3 3 0 11-6 0 3 3 0 016 0z" />
</svg>
);
export default function AirlineHero() {
const [tripType, setTripType] = useState<TripType>('roundtrip');
const [origin, setOrigin] = useState('');
const [destination, setDestination] = useState('');
const [departDate, setDepartDate] = useState('');
const [returnDate, setReturnDate] = useState('');
const [passengers, setPassengers] = useState({ adults: 1, children: 0, infants: 0 });
const handleSearch = (e: FormEvent) => {
e.preventDefault();
console.log({ tripType, origin, destination, departDate, returnDate, passengers });
};
const totalPax = passengers.adults + passengers.children + passengers.infants;
return (
<div className="hero-section min-h-[70vh] flex items-center justify-center">
<div className="w-full max-w-5xl px-4">
<div className="text-center mb-8">
<h1 className="text-4xl md:text-5xl font-bold mb-4">
Book flights at the best prices
</h1>
<p className="text-lg">
Compare hundreds of airlines and find the perfect flight for your journey
</p>
</div>
<div className="search-form">
<form onSubmit={handleSearch}>
<div className="mb-6">
<RadioGroup
name="tripType"
value={tripType}
onChange={setTripType}
options={[
{ value: 'roundtrip', label: 'Round-trip' },
{ value: 'oneway', label: 'One-way' },
{ value: 'multicity', label: 'Multi-city' },
]}
/>
</div>
<div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-4 gap-4">
<div>
<Label htmlFor="origin">From</Label>
<Input
type="text"
id="origin"
value={origin}
onChange={(e) => setOrigin(e.target.value)}
placeholder="Airport or city"
icon={<PlaneIcon />}
required
/>
</div>
<div>
<Label htmlFor="destination">To</Label>
<Input
type="text"
id="destination"
value={destination}
onChange={(e) => setDestination(e.target.value)}
placeholder="Airport or city"
icon={<LocationIcon />}
required
/>
</div>
<div>
<Label htmlFor="departDate">Departure</Label>
<DateInput
id="departDate"
value={departDate}
onChange={(e) => setDepartDate(e.target.value)}
required
/>
</div>
<div>
<Label htmlFor="returnDate">Return</Label>
{tripType === 'roundtrip' ? (
<DateInput
id="returnDate"
value={returnDate}
onChange={(e) => setReturnDate(e.target.value)}
required
/>
) : (
<DateInput id="returnDate" disabled />
)}
</div>
</div>
<div className="grid grid-cols-4 sm:grid-cols-3 lg:grid-cols-4 gap-4 mt-4">
<div className="sm:col-span-1 lg:col-span-1">
<Label htmlFor="passengers">Passengers</Label>
<Dropdown label={`${totalPax} ${totalPax === 1 ? 'passenger' : 'passengers'}`}>
<DropdownCounter
label="Adults"
sublabel="12+ years"
value={passengers.adults}
min={1}
onChange={(v) => setPassengers({ ...passengers, adults: v })}
/>
<DropdownCounter
label="Children"
sublabel="2-11 years"
value={passengers.children}
onChange={(v) => setPassengers({ ...passengers, children: v })}
/>
<DropdownCounter
label="Infants"
sublabel="Under 2"
value={passengers.infants}
onChange={(v) => setPassengers({ ...passengers, infants: v })}
/>
</Dropdown>
</div>
</div>
<div className="mt-6">
<Button type="submit" fullWidth>
Search Flights
</Button>
</div>
</form>
</div>
<div className="mt-6 text-center text-sm">
<p>Direct flights available · Flexible booking · Compare 500+ airlines worldwide</p>
</div>
</div>
</div>
);
}

View File

@@ -0,0 +1,98 @@
'use client';
import type { EventName } from '@/lib/events';
import { useHoverTracking } from '@/hooks/useHoverTracking';
import PriceDisplay from '@/components/ui/PriceDisplay';
const dispatchInteraction = (eventName: EventName, productId?: string, metadata?: Record<string, unknown>) => {
const e = new CustomEvent('definedInteraction', {
detail: { eventName, productId, 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',
pool: 'Pool',
gym: 'Gym',
parking: 'Parking',
breakfast: 'Breakfast',
spa: 'Spa',
};
return <span className="feature-tag">{iconMap[name.toLowerCase()] || name}</span>;
};
export default function HotelCard({ hotel }: { hotel: Hotel }) {
const titleRef = useHoverTracking({
eventName: 'hover_over_title',
productId: hotel.id,
metadata: { elementText: hotel.name },
});
const priceRef = useHoverTracking({
eventName: 'hover_over_paragraph',
productId: hotel.id,
metadata: { elementText: 'price' },
});
const handleCardClick = () => {
dispatchInteraction('view_item_page', hotel.id, {
roomType: hotel.roomType,
price: hotel.pricePerNight,
nights: hotel.nights,
});
};
return (
<div
className="hotel-card cursor-pointer"
onClick={handleCardClick}
>
<div className="hotel-image bg-gray-200 flex items-center justify-center">
<span className="text-gray-400 text-sm">Image</span>
</div>
<div className="hotel-info">
<h3 ref={titleRef} className="hotel-name">{hotel.name}</h3>
<div className="hotel-location text-sm mb-2">{hotel.roomType}</div>
<div className="text-sm text-[var(--text-secondary)] mb-2">
{hotel.checkIn} - {hotel.checkOut}
</div>
<div className="hotel-features">
{hotel.amenities.map((a) => (
<AmenityIcon key={a} name={a} />
))}
</div>
{hotel.refundable && (
<div className="free-cancellation mt-2">Free cancellation</div>
)}
</div>
<div className="hotel-pricing">
<div ref={priceRef}>
<PriceDisplay
productId={hotel.id}
className="price-wrapper"
perNight
/>
</div>
<div className="text-xs text-[var(--text-secondary)] mt-1">
Total for {hotel.nights} night{hotel.nights > 1 ? 's' : ''}
</div>
</div>
</div>
);
}

View File

@@ -0,0 +1,103 @@
'use client';
import { useState, FormEvent } from 'react';
import { Button, Label, Input, DateInput, Dropdown, DropdownCounter } from '@/components/ui';
const LocationIcon = () => (
<svg className="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17.657 16.657L13.414 20.9a1.998 1.998 0 01-2.827 0l-4.244-4.243a8 8 0 1111.314 0z" />
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M15 11a3 3 0 11-6 0 3 3 0 016 0z" />
</svg>
);
export default function HotelHero() {
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();
console.log({ destination, checkIn, checkOut, guests });
};
return (
<div className="hero-section min-h-[70vh] flex items-center justify-center">
<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 stay
</h1>
<p className="text-lg">
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-4 gap-4">
<div className="sm:col-span-2">
<Label htmlFor="destination">Where to?</Label>
<Input
type="text"
id="destination"
value={destination}
onChange={(e) => setDestination(e.target.value)}
placeholder="City, hotel, or landmark"
icon={<LocationIcon />}
required
/>
</div>
<div>
<Label htmlFor="checkIn">Check-in</Label>
<DateInput
id="checkIn"
value={checkIn}
onChange={(e) => setCheckIn(e.target.value)}
required
/>
</div>
<div>
<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-4">
<Button type="submit" fullWidth>
Search Hotels
</Button>
</div>
</div>
</form>
<div className="mt-6 text-center text-sm">
<p>Over 2 million hotels worldwide · Best price guarantee · Free cancellation on most bookings</p>
</div>
</div>
</div>
);
}

View File

@@ -0,0 +1,20 @@
import { ReactNode, ButtonHTMLAttributes } from 'react';
type BtnVariant = 'primary' | 'secondary';
interface BtnProps extends ButtonHTMLAttributes<HTMLButtonElement> {
variant?: BtnVariant;
children: ReactNode;
fullWidth?: boolean;
}
export default function Button({ variant = 'primary', children, fullWidth, className = '', ...props }: BtnProps) {
const baseClass = variant === 'primary' ? 'btn-primary' : 'btn-secondary';
const widthClass = fullWidth ? 'w-full' : '';
return (
<button className={`${baseClass} ${widthClass} ${className}`.trim()} {...props}>
{children}
</button>
);
}

View File

@@ -0,0 +1,7 @@
import { InputHTMLAttributes } from 'react';
interface DateInpProps extends Omit<InputHTMLAttributes<HTMLInputElement>, 'type'> {}
export default function DateInput({ className = '', ...props }: DateInpProps) {
return <input type="date" className={`input-field ${className}`.trim()} {...props} />;
}

View File

@@ -0,0 +1,83 @@
'use client';
import { ReactNode, useState, useRef, useEffect } from 'react';
interface DropdownProps {
label: string;
children: ReactNode;
}
export default function Dropdown({ label, children }: DropdownProps) {
const [open, setOpen] = useState(false);
const ref = useRef<HTMLDivElement>(null);
useEffect(() => {
const handleClick = (e: MouseEvent) => {
if (ref.current && !ref.current.contains(e.target as Node)) {
setOpen(false);
}
};
document.addEventListener('mousedown', handleClick);
return () => document.removeEventListener('mousedown', handleClick);
}, []);
return (
<div className="relative" ref={ref}>
<button
type="button"
onClick={() => setOpen(!open)}
className="input-field flex justify-between items-center w-full"
>
<span>{label}</span>
<svg className="w-5 h-5 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 9l-7 7-7-7" />
</svg>
</button>
{open && (
<div className="absolute z-10 mt-2 w-full bg-white border border-gray-200 rounded-lg shadow-lg p-4">
{children}
</div>
)}
</div>
);
}
interface CounterProps {
label: string;
sublabel?: string;
value: number;
min?: number;
max?: number;
onChange: (val: number) => void;
}
export function DropdownCounter({ label, sublabel, value, min = 0, max = 99, onChange }: CounterProps) {
return (
<div className="flex justify-between items-center py-3 border-b border-gray-100 last:border-b-0">
<div className="flex flex-col">
<span className="text-sm font-medium text-gray-900">{label}</span>
{sublabel && <span className="text-xs text-gray-500 mt-0.5">{sublabel}</span>}
</div>
<div className="flex items-center gap-3">
<button
type="button"
onClick={() => onChange(Math.max(min, value - 1))}
disabled={value <= min}
className="w-9 h-9 rounded-full border-2 border-gray-300 flex items-center justify-center hover:border-blue-500 hover:bg-blue-50 disabled:opacity-40 disabled:cursor-not-allowed disabled:hover:border-gray-300 disabled:hover:bg-transparent transition-colors text-lg font-medium text-gray-700"
>
</button>
<span className="w-10 text-center font-semibold text-gray-900">{value}</span>
<button
type="button"
onClick={() => onChange(Math.min(max, value + 1))}
disabled={value >= max}
className="w-9 h-9 rounded-full border-2 border-gray-300 flex items-center justify-center hover:border-blue-500 hover:bg-blue-50 disabled:opacity-40 disabled:cursor-not-allowed disabled:hover:border-gray-300 disabled:hover:bg-transparent transition-colors text-lg font-medium text-gray-700"
>
+
</button>
</div>
</div>
);
}

View File

@@ -0,0 +1,29 @@
import { InputHTMLAttributes, ReactNode } from 'react';
interface InpProps extends InputHTMLAttributes<HTMLInputElement> {
icon?: ReactNode;
}
export default function Input({ icon, className = '', style, ...props }: InpProps) {
const padClass = icon ? 'pl-10' : '';
// Fallback if a custom CSS rule still overrides Tailwind
const mergedStyle = icon ? { paddingInlineStart: '2.5rem', ...style } : style;
return (
<div className="relative">
{icon && (
<div
aria-hidden
className="pointer-events-none absolute inset-y-0 left-0 flex items-center pl-3 text-gray-400 z-10"
>
{icon}
</div>
)}
<input
className={`input-field ${className} ${padClass}`}
style={mergedStyle}
{...props}
/>
</div>
);
}

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@@ -0,0 +1,13 @@
import { ReactNode, LabelHTMLAttributes } from 'react';
interface LblProps extends LabelHTMLAttributes<HTMLLabelElement> {
children: ReactNode;
}
export default function Label({ children, className = '', ...props }: LblProps) {
return (
<label className={`block text-sm font-medium mb-2 ${className}`.trim()} {...props}>
{children}
</label>
);
}

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@@ -0,0 +1,48 @@
'use client';
import Link from 'next/link';
import { usePathname } from 'next/navigation';
import type { EventName } from '@/lib/events';
const dispatchInteraction = (eventName: EventName, metadata?: Record<string, unknown>) => {
const e = new CustomEvent('definedInteraction', {
detail: { eventName, metadata },
});
document.dispatchEvent(e);
};
const NavLink = ({ href, children }: { href: string; children: React.ReactNode }) => {
const path = usePathname();
const isActive = path === href;
return (
<Link
href={href}
className={`px-4 py-2 rounded-md transition-colors ${
isActive
? 'bg-[var(--accent-primary)] text-white font-semibold'
: 'hover:bg-[var(--accent-primary-light)] text-[var(--text-primary)]'
}`}
>
{children}
</Link>
);
};
export default function Navigation() {
return (
<nav className="bg-[var(--bg-primary)] border-b border-gray-200 shadow-sm">
<div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8">
<div className="flex justify-between h-16">
<div className="flex items-center space-x-1">
<NavLink href="/">Home</NavLink>
<NavLink href="/products">Products</NavLink>
<NavLink href="/search">Search</NavLink>
<NavLink href="/cart">Cart</NavLink>
<NavLink href="/checkout">Checkout</NavLink>
</div>
</div>
</div>
</nav>
);
}

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@@ -0,0 +1,136 @@
'use client';
import { useEffect, useState, useRef } from 'react';
interface PriceDisplayProps {
productId: string;
className?: string;
perNight?: boolean;
}
interface PricingData {
price: number;
currency: string;
cachedAt: string;
}
interface SessionData {
sessionId: string;
experimentId?: string;
}
const fetchSession = async (): Promise<SessionData> => {
try {
const res = await fetch('/api/session');
const data = await res.json();
return {
sessionId: data.sessionId || '',
experimentId: data.experimentId || '',
};
} catch (err) {
console.error('failed to fetch session:', err);
return { sessionId: '', experimentId: '' };
}
};
const formatPrice = (price: number, currency: string) => {
return new Intl.NumberFormat('en-US', { // like an std localization
style: 'currency',
currency,
}).format(price);
};
const isCacheStale = (cachedAt: string, thresholdMs = 60000) => {
const cacheTime = new Date(cachedAt).getTime();
const now = Date.now();
return now - cacheTime > thresholdMs;
};
export default function PriceDisplay({
productId,
className = '',
perNight = false,
}: PriceDisplayProps) {
const sessionRef = useRef<SessionData | null>(null);
const [data, setData] = useState<PricingData | null>(null);
const [loading, setLoading] = useState(true);
const [error, setError] = useState<string | null>(null);
useEffect(() => {
const initAndFetch = async () => {
setLoading(true);
setError(null);
try {
// fetch session if not already loaded
if (!sessionRef.current) {
sessionRef.current = await fetchSession();
}
const { sessionId, experimentId } = sessionRef.current;
if (!sessionId) {
setError('Invalid session');
setLoading(false);
return;
}
const params = new URLSearchParams({
productId,
sessionId,
experimentId: experimentId || '',
});
const res = await fetch(`/api/pricing?${params.toString()}`);
if (!res.ok) {
throw new Error(`Failed to fetch price: ${res.status}`);
}
const pricingData: PricingData = await res.json();
setData(pricingData);
} catch (err) {
setError(err instanceof Error ? err.message : 'Unknown error');
} finally {
setLoading(false);
}
};
initAndFetch();
}, [productId]);
if (loading) {
return (
<div className={`price-loading ${className}`}>
<div className="spinner-border animate-spin inline-block w-4 h-4 border-2 rounded-full" role="status">
<span className="sr-only">Loading...</span>
</div>
</div>
);
}
if (error || !data) {
return (
<div className={`price-error ${className}`}>
<span className="text-red-500 text-sm">Price unavailable</span>
</div>
);
}
const isStale = isCacheStale(data.cachedAt);
const formattedPrice = formatPrice(data.price, data.currency);
return (
<div className={`price-display ${className}`}>
<div className="price-amount">
{formattedPrice}
{perNight && <span className="text-xs ml-1">/night</span>}
</div>
{isStale && (
<span className="price-stale text-xs text-yellow-600" title={`Cached at ${data.cachedAt}`}>
prices may be outdated
</span>
)}
</div>
);
}

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@@ -0,0 +1,33 @@
'use client';
interface RadioOpt<T extends string> {
value: T;
label: string;
}
interface RadioGrpProps<T extends string> {
name: string;
options: RadioOpt<T>[];
value: T;
onChange: (val: T) => void;
}
export default function RadioGroup<T extends string>({ name, options, value, onChange }: RadioGrpProps<T>) {
return (
<div className="flex gap-4">
{options.map((opt) => (
<label key={opt.value} className="flex items-center cursor-pointer">
<input
type="radio"
name={name}
value={opt.value}
checked={value === opt.value}
onChange={(e) => onChange(e.target.value as T)}
className="mr-2"
/>
<span className="text-sm">{opt.label}</span>
</label>
))}
</div>
);
}

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@@ -0,0 +1,7 @@
export { default as Button } from './Button';
export { default as Label } from './Label';
export { default as Input } from './Input';
export { default as DateInput } from './DateInput';
export { default as RadioGroup } from './RadioGroup';
export { default as Dropdown, DropdownCounter } from './Dropdown';
export { default as Navigation } from './Navigation';

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@@ -0,0 +1,63 @@
import { useCallback, useRef } from 'react';
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);
};
interface UseHoverTrackingOptions {
eventName: EventName;
productId?: string;
metadata?: Record<string, unknown>;
threshold?: number; // ms, default 1500 or NEXT_PUBLIC_HOVER_THRESHOLD
}
export const useHoverTracking = (options: UseHoverTrackingOptions) => {
const defaultThreshold = process.env.NEXT_PUBLIC_HOVER_THRESHOLD
? parseInt(process.env.NEXT_PUBLIC_HOVER_THRESHOLD, 10)
: 1500;
const { eventName, productId, metadata, threshold = defaultThreshold } = options;
const timerRef = useRef<NodeJS.Timeout | undefined>(undefined);
const startRef = useRef<number | undefined>(undefined);
return useCallback((node: HTMLElement | null) => {
if (!node) {
if (timerRef.current) clearTimeout(timerRef.current);
return;
}
const onEnter = () => {
startRef.current = Date.now();
timerRef.current = setTimeout(() => {
const dwellTime = Date.now() - startRef.current!;
dispatchInteraction(eventName, productId, {
...metadata,
dwellTime,
});
}, threshold);
};
const onLeave = () => {
if (timerRef.current) {
clearTimeout(timerRef.current);
timerRef.current = undefined;
}
};
node.addEventListener('mouseenter', onEnter);
node.addEventListener('mouseleave', onLeave);
return () => {
node.removeEventListener('mouseenter', onEnter);
node.removeEventListener('mouseleave', onLeave);
if (timerRef.current) clearTimeout(timerRef.current);
};
}, [eventName, productId, metadata, threshold]);
};

View File

@@ -1,117 +1,86 @@
import { useEffect, useRef } from 'react';
import { useEffect, useRef, useState } from 'react';
import '@/lib/experiments' // ensure experiments lib is loaded
import type { EventName } from '@/lib/events';
const genSessionId = () => {
if (typeof window === 'undefined') return '';
let sid = sessionStorage.getItem('phantom_session_id');
if (!sid) {
sid = `${Date.now()}-${Math.random().toString(36).slice(2)}`;
sessionStorage.setItem('phantom_session_id', sid);
// TODO: when creating new id send to exepriemtn tracking db
// match between sesion-id and experiment-id for this session
// so that we can identify all interactions aligning with a specific experiment goal.
}
return sid;
const fetchSessionId = async (): Promise<string> => {
try {
const res = await fetch('/api/session');
const data = await res.json();
return data.sessionId || '';
} catch (err) {
console.error('failed to fetch session:', err);
return '';
}
};
const track = async (ev: {
sessionId: string;
eventType: string;
targetEl?: string;
targetUrl?: string;
metadata?: Record<string, any>;
sessionId: string;
eventName: EventName;
page: string;
productId?: string;
metadata?: Record<string, unknown>;
}) => {
try {
await fetch('/api/track', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(ev),
});
} catch (err) {
console.error('track failed:', err);
}
try {
await fetch('/api/ingest', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(ev),
});
} catch (err) {
console.error('track failed:', err);
}
};
export const useInteractionTracking = () => {
const sidRef = useRef<string>('');
const [ready, setReady] = useState(false);
useEffect(() => {
sidRef.current = genSessionId();
const handleClick = (e: MouseEvent) => {
const tgt = e.target as HTMLElement;
track({
sessionId: sidRef.current,
eventType: 'click',
targetEl: tgt.tagName,
targetUrl: tgt instanceof HTMLAnchorElement ? tgt.href : undefined,
metadata: {
x: e.clientX,
y: e.clientY,
path: window.location.pathname,
},
});
};
const handleScroll = () => {
track({
sessionId: sidRef.current,
eventType: 'scroll',
metadata: {
scrollY: window.scrollY,
path: window.location.pathname,
},
});
};
// fetch session id from httpOnly cookie via API
fetchSessionId().then((sid) => {
sidRef.current = sid;
setReady(true);
});
const handlePageView = () => {
if (!sidRef.current) return;
const page = window.location.pathname;
track({
sessionId: sidRef.current,
eventType: 'pageview',
eventName: 'page_view',
page,
metadata: {
path: window.location.pathname,
referrer: document.referrer,
},
});
};
enum DefinedInteractions {
ADD_TO_CART = 'add_to_cart',
PURCHASE = 'purchase',
}
// called when clicking on "Add to Cart" button or "Purchase" button
const handleDefinedInteraction = (
interactionType: DefinedInteractions,
metadata?: Record<string, any>
) => {
// called for canonical events dispatched via custom events
const handleDefinedInteraction = (e: Event) => {
if (!sidRef.current) return;
const customEvent = e as CustomEvent<{
eventName: EventName;
productId?: string;
metadata?: Record<string, unknown>;
}>;
const page = window.location.pathname;
track({
sessionId: sidRef.current,
eventType: interactionType,
metadata: {
path: window.location.pathname,
...metadata,
},
eventName: customEvent.detail.eventName,
page,
productId: customEvent.detail.productId,
metadata: customEvent.detail.metadata,
});
};
// wait for session to be ready before tracking
if (!ready) return;
handlePageView();
document.addEventListener('click', handleClick);
document.addEventListener('definedInteraction', (e: Event) => {
const customEvent = e as CustomEvent;
handleDefinedInteraction(customEvent.detail.interactionType, customEvent.detail.metadata);
});
// TOO NOISY: enable if needed but tbh not worth it
//window.addEventListener('scroll', handleScroll, { passive: true });
document.addEventListener('definedInteraction', handleDefinedInteraction);
return () => {
document.removeEventListener('click', handleClick);
document.removeEventListener('definedInteraction', (e: Event) => {
const customEvent = e as CustomEvent;
handleDefinedInteraction(customEvent.detail.interactionType, customEvent.detail.metadata);
});
//window.removeEventListener('scroll', handleScroll);
document.removeEventListener('definedInteraction', handleDefinedInteraction);
};
}, []);
}, [ready]);
};

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@@ -0,0 +1,38 @@
import { useEffect, useState } from 'react';
type SessionState = {
sessionId: string | null;
experimentId: string | null;
isLoading: boolean;
};
export const useSession = () => {
const [state, setState] = useState<SessionState>({
sessionId: null,
experimentId: null,
isLoading: true,
});
useEffect(() => {
const fetchSession = async () => {
try {
const res = await fetch('/api/session');
if (!res.ok) throw new Error(`fetch failed: ${res.status}`);
const data = await res.json();
setState({
sessionId: data.sessionId || null,
experimentId: data.experimentId || null,
isLoading: false,
});
} catch (err) {
console.error('session fetch error:', err);
setState({ sessionId: null, experimentId: null, isLoading: false });
}
};
fetchSession();
}, []);
return state;
};

30
web/src/lib/config.ts Normal file
View File

@@ -0,0 +1,30 @@
import { z } from 'zod';
type Env = z.infer<typeof envSchema>;
const envSchema = z.object({
STORE_MODE: z.enum(['hotel', 'airline'], {
message: 'STORE_MODE must be either "hotel" or "airline"'
}),
NEXT_PUBLIC_API_BASE: z.string().url({
message: 'NEXT_PUBLIC_API_BASE must be a valid URL (e.g., http://localhost:3000)'
}),
NEXT_PUBLIC_APP_ENV: z.enum(['dev', 'prod'], {
message: 'NEXT_PUBLIC_APP_ENV must be either "dev" or "prod"'
}),
});
// parse and validate env at module load, fail fast with descriptive errors
const parseEnv = (): Env => {
const result = envSchema.safeParse({
STORE_MODE: process.env.STORE_MODE,
NEXT_PUBLIC_API_BASE: process.env.NEXT_PUBLIC_API_BASE,
NEXT_PUBLIC_APP_ENV: process.env.NEXT_PUBLIC_APP_ENV,
});
if (!result.success) {
const errors = result.error.issues.map((err) => `${err.path.join('.')}: ${err.message}`).join('\n');
throw new Error(`Environment validation failed:\n${errors}`);
}
return result.data;
};
export const config: Env = parseEnv();

91
web/src/lib/events.ts Normal file
View File

@@ -0,0 +1,91 @@
import { z } from 'zod';
// canonical events for tracking user interactions
export type EventName =
// navigation & discovery
| 'page_view'
| 'view_item_page'
| 'learn_more_about_item'
// cart operations
| 'add_item_to_cart'
| 'remove_item'
| 'checkout_start'
| 'purchase_complete'
// filtering & search
| 'search'
| 'filter_for_date'
| 'filter_for_amenities'
| 'filter_for_price'
| 'sort_change'
// dwell signals (Ns threshold)
| 'hover_over_title'
| 'hover_over_paragraph'
| 'hover_over_link'
| 'hover_over_button'
// session
| 'session_start';
export const eventNames: readonly EventName[] = [
'page_view',
'view_item_page',
'learn_more_about_item',
'add_item_to_cart',
'remove_item',
'checkout_start',
'purchase_complete',
'search',
'filter_for_date',
'filter_for_amenities',
'filter_for_price',
'sort_change',
'hover_over_title',
'hover_over_paragraph',
'hover_over_link',
'hover_over_button',
'session_start',
] as const;
export interface EventBase {
sessionId: string;
experimentId?: string;
storeMode: 'hotel' | 'airline';
ts: string; // ISO8601
page: string;
eventName: EventName;
productId?: string;
metadata?: Record<string, unknown>;
userAgent?: string;
}
// zod schema for runtime validation
export const eventBaseSchema = z.object({
sessionId: z.string().min(1),
experimentId: z.string().optional(),
storeMode: z.enum(['hotel', 'airline']),
ts: z.string().datetime(), // validates ISO8601
page: z.string().min(1),
eventName: z.enum([
'page_view',
'view_item_page',
'learn_more_about_item',
'add_item_to_cart',
'remove_item',
'checkout_start',
'purchase_complete',
'search',
'filter_for_date',
'filter_for_amenities',
'filter_for_price',
'sort_change',
'hover_over_title',
'hover_over_paragraph',
'hover_over_link',
'hover_over_button',
'session_start',
]),
productId: z.string().optional(),
metadata: z.record(z.string(), z.unknown()).optional(),
userAgent: z.string().optional(),
});
export type EventBaseValidated = z.infer<typeof eventBaseSchema>;

View File

@@ -1,42 +0,0 @@
import { Kafka, Producer } from 'kafkajs';
let producer: Producer | null = null;
const kafka = new Kafka({
clientId: 'phantom-web',
brokers: [`${process.env.KAFKA_HOST || 'localhost'}:${process.env.KAFKA_PORT || '9092'}`],
});
export const getProducer = async (): Promise<Producer> => {
if (!producer) {
producer = kafka.producer();
await producer.connect();
}
return producer;
};
export const sendInteractionEvent = async (ev: {
sessionId: string;
eventType: string;
targetEl?: string;
targetUrl?: string;
metadata?: Record<string, any>;
ts: number;
}) => {
const p = await getProducer();
// add to the metadata
await p.send({
topic: 'user-interactions',
messages: [{
key: ev.sessionId,
value: JSON.stringify(ev),
}],
});
};
export const disconnect = async () => {
if (producer) {
await producer.disconnect();
producer = null;
}
};

102
web/src/lib/sessionStore.ts Normal file
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@@ -0,0 +1,102 @@
type SessionData = {
experimentId?: string;
startedAt: number;
status: 'active' | 'stopped';
};
type ExperimentData = {
id: string;
status: 'active' | 'stopped';
sessionIds: string[];
createdAt: number;
};
const store = new Map<string, SessionData>();
const experiments = new Map<string, ExperimentData>();
const cfg = {
key: process.env.AIRTABLE_API_KEY,
base: process.env.AIRTABLE_BASE_ID,
table: process.env.AIRTABLE_TABLE_NAME || 'Sessions',
};
// sync session to airtable if credentials present
const syncToAirtable = async (sid: string, data: SessionData) => {
if (!cfg.key || !cfg.base) return; // skip if not configured
try {
const url = `https://api.airtable.com/v0/${cfg.base}/${encodeURIComponent(cfg.table)}`;
await fetch(url, {
method: 'POST',
headers: {
Authorization: `Bearer ${cfg.key}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
fields: {
sessionId: sid,
experimentId: data.experimentId || '',
startedAt: new Date(data.startedAt).toISOString(),
status: data.status,
},
}),
});
} catch (err) {
console.error('airtable sync failed:', err);
}
};
export const getSession = (sid: string) => store.get(sid);
export const createSession = (sid: string) => {
const data: SessionData = { startedAt: Date.now(), status: 'active' };
store.set(sid, data);
syncToAirtable(sid, data); // async fire-and-forget
return data;
};
export const setExperiment = (sid: string, expId: string) => {
const data = store.get(sid) || createSession(sid);
data.experimentId = expId;
store.set(sid, data);
syncToAirtable(sid, data);
return data;
};
export const stopExperiment = (sid: string) => {
const data = store.get(sid);
if (data) {
data.status = 'stopped';
store.set(sid, data);
syncToAirtable(sid, data);
}
return data;
};
// experiment-level operations
export const createExperiment = (sid: string, expId: string) => {
const exp: ExperimentData = {
id: expId,
status: 'active',
sessionIds: [sid],
createdAt: Date.now(),
};
experiments.set(expId, exp);
setExperiment(sid, expId); // link session to experiment
console.log(`experiment ${expId} started with session ${sid}`);
return exp;
};
export const stopExperimentById = (expId: string) => {
const exp = experiments.get(expId);
if (exp) {
exp.status = 'stopped';
experiments.set(expId, exp);
console.log(`experiment ${expId} stopped`);
}
return exp;
};
export const getExperiment = (expId: string) => experiments.get(expId);
export const getAllExperiments = () => Array.from(experiments.values());

36
web/src/proxy.ts Normal file
View File

@@ -0,0 +1,36 @@
import { NextRequest, NextResponse } from 'next/server';
export function proxy(req: NextRequest) {
const mode = process.env.STORE_MODE;
const { pathname } = req.nextUrl;
// skip rewrites for api routes, admin routes, static files, and next internals
if (
pathname.startsWith('/api') ||
pathname.startsWith('/admin') ||
pathname.startsWith('/_next') ||
pathname.startsWith('/static') ||
pathname.includes('.')
// TODO: add robots.txt and sitemap.xml if needed here
) {
return NextResponse.next();
}
// already prefixed with mode
if (pathname.startsWith(`/${mode}`)) {
return NextResponse.next();
}
// rewrite root and unprefixed paths to mode-specific route group
const url = req.nextUrl.clone();
url.pathname = `/${mode}${pathname === '/' ? '' : pathname}`;
return NextResponse.rewrite(url);
}
export const config = {
matcher: [
// match all paths except those starting with _next/static, _next/image, favicon.ico
'/((?!_next/static|_next/image|favicon.ico).*)',
],
};

View File

@@ -1,25 +1,38 @@
/* Airline Platform - Sky Blue Theme */
:root[data-mode="airline"] {
@layer base {
[data-mode="airline"] {
--accent-primary: #007aff;
--accent-secondary: #4caf50;
--accent-warning: #ff3b30;
--accent-primary-hover: #0051d5;
--accent-primary-light: #e6f2ff;
--text-accent: #007aff;
--hero-bg: linear-gradient(to bottom, white, #e6f2ff);
}
}
@layer components {
[data-mode="airline"] {
--primary-color: var(--accent-primary);
}
[data-mode="airline"] .btn-primary {
background-color: var(--accent-primary);
color: #ffffff;
background-color: var(--accent-primary) !important;
color: #ffffff !important;
padding: 12px 24px;
font-weight: 600;
font-size: 1rem;
border-radius: var(--border-radius);
border: none;
cursor: pointer;
transition: all 0.2s ease;
}
[data-mode="airline"] .btn-primary:hover {
background-color: var(--accent-primary-hover);
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(0, 122, 255, 0.3);
}
[data-mode="airline"] .btn-secondary {
@@ -264,6 +277,7 @@
border-radius: 6px;
padding: 12px;
transition: border-color 0.2s ease;
width: 100%;
}
[data-mode="airline"] .input-field:focus {
@@ -300,3 +314,8 @@
[data-mode="airline"] .checkbox-label:hover {
color: var(--accent-primary);
}
[data-mode="airline"] .hero-section {
background: var(--hero-bg);
}
}

View File

@@ -1,6 +1,7 @@
/* Hotel Platform - Action Blue Theme */
:root[data-mode="hotel"] {
@layer base {
[data-mode="hotel"] {
--accent-primary: #007aff;
--accent-secondary: #4caf50;
--accent-warning: #d9534f;
@@ -8,8 +9,11 @@
--accent-primary-light: #e6f2ff;
--text-accent: #007aff;
--bg-tertiary: #f5f5f7;
--hero-bg: linear-gradient(to bottom, white, #f5f5f5);
}
}
@layer components {
[data-mode="hotel"] {
--primary-color: var(--accent-primary);
}
@@ -17,10 +21,19 @@
[data-mode="hotel"] .btn-primary {
background-color: var(--accent-primary);
color: #ffffff;
padding: 12px 24px;
font-weight: 600;
font-size: 1rem;
border-radius: var(--border-radius);
border: none;
cursor: pointer;
transition: all 0.2s ease;
}
[data-mode="hotel"] .btn-primary:hover {
background-color: var(--accent-primary-hover);
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(0, 122, 255, 0.3);
}
[data-mode="hotel"] .btn-secondary {
@@ -398,3 +411,8 @@
color: var(--accent-primary);
border-bottom-color: var(--accent-primary);
}
[data-mode="hotel"] .hero-section {
background: var(--hero-bg);
}
}