SUSTech Merch Store Microservices

Wed, 01 Oct 2025 00:00:00 +0000

Background

Time: Oct 2025
Context: Assignment 2 for SUSTech “Distributed and Cloud Computing” (module: Services & API Architectures).
Repository: Tsurumalu/SUSTech-Merch-Store

Goal

Build a microservice-based online merch store for SUSTech-branded products. The system exposes RESTful APIs to clients, isolates database access behind a gRPC service, and streams operational logs to Kafka through a dedicated logging service—all orchestrated with Docker Compose.

Architecture

Three application services run alongside shared infrastructure:

Service	Role	Protocol / Stack
API Service	REST gateway, JWT auth, business orchestration	Flask, OpenAPI 3.0
DB Service	User, product, and order persistence	gRPC + psycopg2
Logging Service	Centralized log ingestion	gRPC → Kafka

Infrastructure containers: PostgreSQL 17 (persistent storage), Zookeeper + Kafka (log channel log-channel).

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2
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Client ──HTTP──► API Service ──gRPC──► DB Service ──► PostgreSQL
 │
 └──gRPC──► Logging Service ──► Kafka

REST API

Defined in openapi.yaml and implemented in Flask:

Dialog: GET / greeting endpoint
UserManager: register, deactivate, get user, update profile, login
ProductManager: list products, get product by ID
OrderManager: place order, cancel order, get order details

Protected routes use JWT (Bearer token, HS256, 24-hour expiration). Authentication failures and key operations are forwarded to the logging service.

Data Model

PostgreSQL schema (db-init/init.sql):

products: SUSTech Hoodie, Water Bottle, Notebook (pre-seeded with stock)
users: sid, username, email, password hash
orders: user–product linkage with quantity cap (1–3 per order) and computed total_price

Implementation Highlights

Service decomposition: API layer never talks to PostgreSQL directly; all DB operations go through the gRPC AssistantService.
Connection pooling: DB service uses psycopg2.pool.ThreadedConnectionPool for concurrent gRPC requests.
Structured logging: API service streams LogMessage events (register, login, order, auth failures) to the logging service, which publishes JSON payloads to Kafka via confluent_kafka.
Containerized deployment: compose.yaml wires api-server, db-server, and logging-server with health-checked Kafka/Zookeeper and volume-backed Postgres init scripts.
Dev utilities: Makefile helpers for local psql, DB reset, and Kafka log streaming.

Tech Stack

Python (Flask, gRPC, PyJWT)
PostgreSQL 17
Apache Kafka (Confluent Platform 7.7)
Docker & Docker Compose
OpenAPI 3.0
Protocol Buffers (gRPC service definitions)

Weibo Personality Analyzer

Fri, 09 May 2025 00:00:00 +0000

Background

Time: May 2026
Context: CS114 course project.
Project title: 微博性格分析师：利用生成式AI洞察社交媒体人格
English title: Weibo Personality Analyzer: Using Generative AI to Gain Insight into Social Media Persona

Goal

The project aims to build a web-based tool called “Weibo Personality Analyzer”. By collecting and analyzing a Weibo user’s public posts, the system infers content themes, writing style, and personality-related tendencies from social media text.

The core role of AI is to use the Qwen large language model to process unstructured Weibo posts, extract linguistic signals, and evaluate whether generative AI can support social media persona analysis in a structured and explainable workflow.

System Workflow

User enters a target Weibo username through the web interface at callitwhatyouwant.cn(now unavailable).
The backend launches a Selenium-based scraper through scraper.py, which simulates browser behavior for more stable data collection.
The system supports collecting around 50 to 300 public posts.
Raw posts are cleaned and converted into a structured Markdown file.
Qwen analyzes the Markdown input and produces a Markdown report.
The Flask backend formats the result and returns it to the web frontend.

AI Analysis

The Qwen LLM is used as the core analysis engine. It receives structured Markdown content and produces analysis across three main dimensions:

Content theme modeling: clusters recurring topics and interest areas.
Writing style analysis: evaluates formality, casualness, emotional tendency, and expression habits.
Personality inference: maps language features to MBTI-inspired dimensions while avoiding unsupported psychological diagnosis.

Web Application

Frontend: HTML, CSS, and JavaScript with responsive design.
Backend: Python Flask routes for request handling, scraper triggering, Qwen API calls, and result formatting.
Deployment: Tencent Cloud ECS server with the domain callitwhatyouwant.cn.

Prompt Engineering

The prompt design went through several iterations:

Version	Prompt Strategy	Result
V1.0	“Analyze these Weibo posts: [text]”	Too general and lacked depth.
V2.0	Asked the model to act as a social media analyst and output theme, style, and personality sections.	Structure improved, but personality analysis was still weak.
V3.0	Added MBTI-based dimensions, negative constraints against unsupported mental-state claims, and required Markdown output.	Reliability and readability improved significantly.

Example prompt direction:

As a social media analyst, analyze the following public Weibo posts from three perspectives: content themes, writing style, and MBTI-inspired personality tendencies. Do not infer private mental health status or make claims unsupported by the text. Output the result in Markdown.

Role of Generative AI

Analysis engine: Qwen performs multi-dimensional text interpretation, including topic, sentiment, style, and persona-related signals.
Development accelerator: generative AI helped draft frontend structure, CSS styling, Flask route templates, and debugging suggestions.
Debugging support: AI-assisted log interpretation helped locate syntax and logic errors during development.
Security suggestions: AI provided reminders such as cookie encryption and safer handling of user data.

Tech Stack

Qwen LLM
Python
Selenium
Flask
HTML/CSS
JavaScript
Markdown parsing
Tencent Cloud ECS

Tools Used

Qwen large language model for post analysis.
Claude 3.7 Sonnet for backend development support.
Bolt.new for frontend design support.

Flask on Zitao Liao

SUSTech Merch Store Microservices

Background

Goal

Architecture

REST API

Data Model

Implementation Highlights

Tech Stack

Weibo Personality Analyzer

Background

Goal

System Workflow

AI Analysis

Web Application

Prompt Engineering

Role of Generative AI

Tech Stack

Tools Used