ScholarSync – Resume & Google Scholar Integration App

Objective

Develop a full‑stack web application using Next.js that integrates a user’s resume and Google Scholar profile to suggest suitable projects based on their skills, education, and academic work. The app will:

1. Parse the resume to extract key details (name, contact, skills, experience, education).
2. Fetch the Google Scholar profile to gather research interests, publications, citations, and h‑index.
3. Run a recommendation engine that suggests projects categorized by the user’s skills and academic expertise.

---

Table of Contents

1. Frontend Specifications
2. Backend Specifications
3. State Management & Async Patterns
4. Security Considerations
5. Design Patterns
6. Testing
7. Linting & Tooling

---

Frontend Specifications

1. Core Technologies

• Framework: Next.js (App Router, SSR/SSG)
• Language: TypeScript
• Styling: Tailwind CSS + shadcn/ui components
• State Management: Redux Toolkit (with Thunks)
• Animations: Framer Motion

2. Key UI Components

• ResumeUploader:

  • Drag & drop or click to upload PDF/DOCX
  • Shows upload progress, file metadata
  • “Analyze Resume” button

• ScholarProfileInput:

  • URL input field for Google Scholar
  • Validates URL format, shows loader on fetch
  • Displays researcher name, affiliation, citations, h‑index, interests, recent publications

• ProjectSuggestions:

  • Lists recommended projects with title, description, match score badge
  • Tags for matched skills & research areas
  • “Refine Analysis” & “Save Results” buttons

• ResultsPage:

  • Summary cards for resume & scholar data
  • Grid of suggested projects with dynamic gradients and hover effects

---

Backend Specifications

1. Resume Parsing API

• Route: POST /api/parse-resume
• Implementation: Next.js API Route
• Parser: pdf-parse (PDF) and mammoth.js (DOCX)
• Output:

  {
    "name": "John Doe",
    "emails": ["john@example.com"],
    "phone": "+1-555-123-4567",
    "skills": ["JavaScript","React","AWS",…],
    "education": ["B.Sc. Computer Science 2020",…],
    "experience": ["Frontend Intern – XYZ Corp (2021–22)",…]
  }
  

2. Google Scholar API

• Route: POST /api/scrape-scholar
• Implementation: Next.js API Route using Puppeteer
• Data Extracted:
  • name (selector #gsc_prf_in)
  • affiliation (selector .gsc_prf_il)
  • interests (selector .gsc_prf_ila)
  • citations, hIndex (from td.gsc_rsb_std)
  • publications (first 20 rows, selector .gsc_a_tr)

---

3. Project Suggestion Logic

• Route: POST /api/suggest-projects
• Input: Parsed resume JSON + scholar JSON
• Engine:
  1. Embeddings: Sentence‑Transformers all‑mpnet‑base‑v2 served via a FastAPI microservice (embed-engine/)
  2. Matcher: Cosine similarity between the concatenated profile embedding and each project’s embedding
  3. Fallback: If no high‑score matches are found, return the top 3 generic projects with a forced 50% match score
• Storage: Supabase table results with columns:
  • id UUID (primary key)
  • created_at TIMESTAMP
  • resume_data JSONB
  • scholar_data JSONB
  • project_suggestions JSONB

---

State Management & Async Patterns

Redux Slices

• resumeSlice — stores parsed resume data
• scholarSlice — stores Google Scholar profile data
• suggestionSlice — stores project suggestions

Async Thunks

• fetchResume — calls /api/parse-resume
• fetchScholar — calls /api/scrape-scholar
• fetchSuggestions — invokes the embedding engine, then saves to Supabase

Promise Patterns

• Promise.all() — run resume parse and scholar scrape in parallel
• Promise.race() — optionally fallback to the fastest parser if multiple are configured
• Sequential chaining — resume ⟶ scholar ⟶ embed ⟶ database save

---

Security Considerations

Input Validation

• File type & size checks on resume uploads
• URL pattern validation for Scholar profile endpoint

API Security

• Rate limiting on /api/scrape-scholar to prevent abuse
• Secrets (Supabase keys, service roles) stored exclusively in environment variables
• Supabase Row‑Level Security (RLS) with a policy allowing only the service role to insert

Sanitization & Headers

• Strip any HTML from parsed text to prevent XSS
• Use dangerouslySetInnerHTML only on sanitized strings
• Configure a strict CSP via Next.js’s headers() API

---

Design Patterns

Pattern	Use Case
Factory	Instantiate PDFParser vs DocxParser based on file type
Strategy	Swapable text‑extraction strategies for different resume formats
Observer	UI components subscribe to Redux store updates for live progress feedback
Singleton	Single Supabase client and single embedding‐model instance across the app
Adapter	Normalize Puppeteer vs Cheerio scrapers under a common ScholarProfile interface

---

Testing

Unit Tests (Jest & RTL)

• ResumeUploader renders and fires API calls correctly
• scholarSlice reducers and async thunks
• matcher.ts scoring logic with mocked embeddings

Integration Tests

• Next.js API routes tested via supertest or Next’s built‑in test utils
• Mock Puppeteer & pdf-parse to simulate edge cases

E2E Tests (Cypress)

• Full upload → analyze → results workflow
• Test with a variety of resume files (different formats, missing sections)
• Validate error handling on invalid Google Scholar URLs

Linting & Tooling

• ESLint + Prettier

  • Configured for TypeScript, React, and Next.js
  • Rules enforced via .eslintrc.json and .prettierrc
  • Automatically fixes formatting and lint errors on save or via CLI

• Husky Pre‑commit Hooks

  • Runs eslint --fix and prettier --write on staged files
  • Ensures code style consistency before commits
  • Defined in package.json under "husky" scripts

• EditorConfig

  • .editorconfig enforces consistent indentation, line endings, and charset
  • Shared across IDEs to standardize developer environment

• TypeScript

  • Strict mode enabled ("strict": true) in tsconfig.json
  • No implicit any, strict null checks, and path aliases configured

• Gitignore

  • Ignores build artifacts (.next/, node_modules/, venv/, etc.)
  • Keeps repository clean and reduces merge conflicts

Installation & Setup

Prerequisite: Node.js v18+, Python 3.10+, and Git

1. Clone the Repository

git clone https://github.com/your-username/scholarsync.git
cd scholarsync

2. Install Frontend Dependencies

cd scholar-sync
npm install

3. Install Backend Dependencies

cd embed-engine
python -m venv venv
source venv/bin/activate  
pip install -r requirements.txt

If missing, install manually:

pip install fastapi uvicorn sentence-transformers torch

4. Environment Variables

Create .env.local in scholar-sync/:

NEXT_PUBLIC_SUPABASE_URL=https://<your-project>.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
SUPABASE_SERVICE_ROLE_KEY=your-service-role-key

5. Start the Servers

Run embedding engine:

cd embed-engine
uvicorn app:app --reload --port 8001

Run Next.js frontend:

cd scholar-sync
npm run dev