AI/ML2026CompletedFeatured Project

Wcareer

React + Vite This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. Currently, two official plugins are available: - @vitejs/plugin-rea...

Source Code

RoleSole Developer

Classificationhigh

Client / ContextOpen Source Project

Development TimelineCompleted

Technical Architecture & Engineering Overview

Detailed system design and core implementation

Core System Architecture The system is divided into distinct layers: a client (client/) built with React 19, Tailwind CSS, and Radix UI for a responsive UI; a mockInterview module using Next.js for interview simulations; a server (server/) running Express 4.21 with JWT authentication, bcrypt, and Nodemailer for email workflows; and a WCareers Python service (llm-server/) that hosts LLM‑driven models (cognitive_test_model.py, resume_analysis.py, etc.) behind a FastAPI/Streamlit API. Communication between the JavaScript and Python layers occurs via RESTful HTTP calls using Axios on the frontend and requests in the backend.

Data Ingestion & Processing Pipelines User‑generated content (resumes, skill updates, interview answers) is first validated by middleware (authMidd, CORS, cookie parser) before being persisted to MongoDB. Skill relationships are enriched and stored in Neo4j, enabling graph queries for the skill‑graph endpoints (/api/skill/graph). For semantic operations, documents are vectorized with Sentence Transformers and indexed in FAISS, supporting endpoints like /api/skill/trending. The AI layer consumes these vectors and external data sources (via BeautifulSoup4, PyPDF) to generate personalized recommendations.

Execution Flow A typical user journey starts at http://localhost:5173 where the React app authenticates via /api/auth/login. Authenticated requests fetch data from /api/job, /api/roadmap, or trigger AI services such as /api/resume/analyze. Mock interview creation (POST /api/interview/create) stores a session in MongoDB, then the frontend polls /api/interview/:mockId for questions and submits answers to /api/interview/answer. Feedback is generated by the Python LLM service and returned via /api/interview/:mockId/feedback.

Security Guardrails All protected routes enforce JWT verification and role‑based checks. Sensitive operations (password reset, email verification) use one‑time‑passwords sent via Nodemailer. Environment secrets are managed with dotenv, and Docker containers isolate the AI models from the Node.js runtime. Rate limiting and input sanitization are applied at the Express middleware level to mitigate abuse.

System Diagrams & Flow Visualizations

Interactive Mermaid architecture & execution flows

Architectural Visualizer · System ArchitectureUse mouse wheel or pinch gestures to zoom

System Architecture Diagram Code:

graph TD
 subgraph ClientTier ["Presentation & Client Layer"]
 UI["Frontend UI Components (React / TailwindCSS / Next.js)"]
 end
 subgraph APITier ["API Gateway & Routing Layer"]
 Router["Backend Service Router (Express)"]
 end
 subgraph ServiceTier ["Service Logic & Controllers Layer"]
 Controller["Service Controllers & Handlers (server/services)"]
 end
 subgraph DataEngineTier ["Inference Engine & Data Storage"]
 InferenceEngine["AI/ML Model Inference Runtime (LangChain / Gemini AI)"]
 Database["Persistence Storage Layer (MongoDB)"]
 end
 UI --> Router
 Router --> Controller
 Controller --> InferenceEngine
 Controller --> Database
 Controller --> UI

Core System Features

Key capabilities & architectural features

FEATURE 01

User registration, login, and secure JWT authentication.

FEATURE 02

Resume upload, parsing, AI‑driven analysis, and enhancement suggestions.

FEATURE 03

Skill tracking with trending insights and graph‑based relationship visualization.

FEATURE 04

AI‑generated career roadmaps and personalized job recommendations.

FEATURE 05

Mock interview creation, real‑time question delivery, and AI feedback.

FEATURE 06

Community forum with discussions, posting, and notification system.

FEATURE 07

Dashboard with analytics, charts, and progress tracking.

FEATURE 08

PDF generation of resumes and roadmaps via jsPDF.

FEATURE 09

Video interview recording using React Webcam.

FEATURE 010

Multi‑modal AI integration (text, voice, PDF) powered by LangChain and Gemini AI.

Engineering Challenges & Solutions

Problem statements & resolution strategies

Challenge #1

Balancing polyglot persistence (MongoDB, Neo4j, FAISS) while maintaining data consistency.

Challenge #2

Optimizing LLM inference latency within Docker containers for real‑time feedback.

Challenge #3

Ensuring secure handling of sensitive user data across multiple services and environments.

Challenge #4

Coordinating version compatibility between React 19, Vite 6, and Next.js modules.

Challenge #5

Designing a scalable event‑driven notification system without a dedicated message broker.

API Reference & Endpoints

REST API surface & route documentation

METHOD	ENDPOINT PATH	DESCRIPTION
POST	`/generate_queries`	Endpoint defined in WCareers/2_learning_pathway/api.py
POST	`/search_and_generate`	Endpoint defined in WCareers/2_learning_pathway/api.py
POST	`/generate_subheadings`	Endpoint defined in WCareers/2_learning_pathway/api.py
POST	`/register`	Endpoint defined in server/routes/authRoutes.js
POST	`/login`	Endpoint defined in server/routes/authRoutes.js
POST	`/logout`	Endpoint defined in server/routes/authRoutes.js
POST	`/send-verify-otp`	Endpoint defined in server/routes/authRoutes.js
POST	`/verify-account`	Endpoint defined in server/routes/authRoutes.js
GET	`/is-auth`	Endpoint defined in server/routes/authRoutes.js
POST	`/send-reset-otp`	Endpoint defined in server/routes/authRoutes.js
POST	`/reset-password`	Endpoint defined in server/routes/authRoutes.js
POST	`/create`	Endpoint defined in server/routes/interviewRoutes.js
GET	`/:mockId`	Endpoint defined in server/routes/interviewRoutes.js
GET	`/user/:userEmail`	Endpoint defined in server/routes/interviewRoutes.js
POST	`/answer`	Endpoint defined in server/routes/interviewRoutes.js
GET	`/:mockId/feedback`	Endpoint defined in server/routes/interviewRoutes.js
GET	`/:mockId/answers`	Endpoint defined in server/routes/interviewRoutes.js
GET	`/data`	Endpoint defined in server/routes/userRoutes.js
GET	`/`	Endpoint defined in server/server.js

Key Code Modules & Architecture

Core file entrypoints and service responsibilities

ClientMODULE 01

client

Module component managing client source logic

SrcMODULE 02

old-HireMe-frontend/src

Module component managing src source logic

AppMODULE 03

mockInterview/app

Module component managing app source logic

ComponentsMODULE 04

old-HireMe-frontend/src/components

Module component managing components source logic

UiMODULE 05

mockInterview/components/ui

Module component managing ui source logic

HooksMODULE 06

old-HireMe-frontend/src/hooks

Module component managing hooks source logic

PagesMODULE 07

client/src/pages

Module component managing pages source logic

LibMODULE 08

mockInterview/lib

Module component managing lib source logic

UtilsMODULE 09

server/utils

Module component managing utils source logic

PagesMODULE 010

old-HireMe-frontend/src/Pages

Module component managing Pages source logic

ServicesMODULE 011

server/services

Module component managing services source logic

TypesMODULE 012

old-HireMe-frontend/src/types

Module component managing types source logic

ServerMODULE 013

server

Module component managing server source logic

ConfigMODULE 014

server/config

Module component managing config source logic

ControllersMODULE 015

server/controllers

Module component managing controllers source logic

MiddlewareMODULE 016

server/middleware

Module component managing middleware source logic

ModelsMODULE 017

server/models

Module component managing models source logic

RoutesMODULE 018

server/routes

Module component managing routes source logic

User Journey Pipeline

1User accesses application user interface via browser
2User triggers action invoking HTTP/WebSocket API endpoints
3Payload processed through AI/ML pipeline or local inference model
4Data persisted or retrieved from database storage layer

Execution Code Flow

1Client or caller initiates request to application entrypoint
2Backend router validates request parameters and applies middleware
3Service layer delegates task to ML runtime or LLM API worker
4Database ORM/client executes storage query
5Application serializes JSON/audio/data response back to client

System FAQ & Technical Specifications

Frequently asked questions and core architecture details

Q: What technologies are used in Wcareer?

Wcareer is engineered using modern full-stack frameworks.

Q: What core engineering problem does Wcareer solve?

React + Vite This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. Currently, two official plugins are available: - @vitejs/plugin-rea...

Q: How is 'Balancing polyglot persistence (MongoDB, Neo4j, FAISS) while maintaining data consistency.' resolved in Wcareer?

Resolved through specialized system architecture and resilient state handling.

Related Systems & Case Studies

Explore similar technical architectures and projects

View All Systems

Athena End To End Ai Agent

Production-Ready TTS Microservice (Supertonic ONNX) Architecture • Folder Structure • Deployment • Scaling • APIs • Best Practices Fully tuned for AI agent usage. What This Serv...

Jalsakhi Ai Powered Precision Agriculture Platform

Village Water Allocation Optimization API Distributes limited village reservoir water across farms using: - Crop Water Requirement from CropWaterModel (Model 1) - Optional predicte...

2d Desktop Ai

Sakshi AI Desktop Assistant A beautiful desktop AI assistant with voice interaction and lip-sync animation, built with Electron and React. Features Voice Interaction: Talk to Saksh...

Explore All Projects