An enterprise-grade AI career platform and ATS intelligence studio. Features algorithmic ATS simulation for top-tier tech companies, automated job description extraction, AI-powered resume rewriting via NVIDIA NIM, interview coaching, and developer portfolio analysis. Built with FastAPI and React 19.
Apex Resume Studio integrates 12 specialized career intelligence modules into a responsive dashboard with a cyber-dark aesthetic:
- Deep Resume Analysis: PDF document extraction (
PyPDF2), keyword identification, ATS compatibility scoring, and contextual proficiency metrics. - Enterprise ATS Simulator: Algorithmic scoring mimicking screening filters of major tech companies (Google, Amazon, Meta, Apple, Microsoft) with keyword density breakdown and length validation.
- AI Resume Optimizer: Rewrites professional summaries and bullet points against specific job requirements using NVIDIA NIM (
meta/llama-3.1-70b-instruct). - Job Description Auto-Fetcher: Scrapes and parses job requirements directly from job posting URLs using BeautifulSoup4 and requests.
- STAR Interview Coach: Generates role-specific technical and behavioral questions categorized by difficulty, with structured AI model answers following the STAR format.
- Personalized Learning Path: Auto-generates structured skill roadmaps with curated courses and resources to bridge resume skill gaps.
- Smart Project Recommendations: Recommends hands-on portfolio projects designed to demonstrate missing technical proficiencies.
- Resume Intelligence Suite: Audits claim credibility, analyzes soft skill evidence (Leadership, Teamwork, Problem Solving, Adaptability), evaluates sentiment/subjectivity via TextBlob, and visualizes section weight.
- GitHub Portfolio Analyzer: Connects to the GitHub API via PyGithub to evaluate public repositories, top programming languages, and contribution patterns.
- LinkedIn Profile Sync: Compares resume text with exported LinkedIn profile data to surface gaps, date discrepancies, or missing achievements.
- Student Career Coach: Tailored mode for students, interns, and early-career candidates, weighting potential, coursework, and personal projects over commercial tenure.
- Resume Version Vault: Manage and switch between tailored versions of resumes across applications.
Apex-Resume-Studio/
├── app_utils/ # LLM interface & NVIDIA NIM client wrapper
├── backend/ # FastAPI REST API application & endpoints
├── config.py # Global application configuration & ATS keyword dictionaries
├── frontend/ # React 19 SPA (Vite, Tailwind CSS, React Router v7)
├── modules/ # Core business logic & AI processing engines
├── tests/ # Automated backend unit test suite
├── Procfile # Production process declaration
└── render.yaml # Infrastructure-as-code deployment specification
- Framework: FastAPI (Uvicorn ASGI)
- Language: Python 3.10+ (tested on Python 3.12)
- AI / LLM: NVIDIA NIM API (
meta/llama-3.1-70b-instruct) via OpenAI client - PDF Processing: PyPDF2
- NLP & Analysis: TextBlob, Pandas
- APIs & Scraping: PyGithub, BeautifulSoup4, Requests
- Framework: React 19 + Vite 8
- Styling: Tailwind CSS 3.4 (Midnight Slate dark theme, cyan accents, glassmorphism)
- Routing: React Router v7
- Linter: Oxlint
- Icons: Google Material Symbols Outlined
- Python: Version 3.10 or higher
- Node.js: Version 18.0 or higher (Node 20+ recommended)
- NVIDIA NIM API Key: Required for AI-powered rewriting, interview coaching, and project suggestions (Get a free key from NVIDIA Build).
- GitHub Token (Optional): Recommended for higher rate limits on GitHub portfolio analysis.
git clone https://github.com/nfsprogramming/Apex-Resume-Studio.git
cd Apex-Resume-Studio- Create and activate a virtual environment (recommended):
python -m venv .venv # Windows: .venv\Scripts\activate # macOS/Linux: source .venv/bin/activate
- Install dependencies:
pip install -r requirements.txt
- Configure environment variables:
Edit
cp .env.example .env
.envto supply your API credentials:NVIDIA_API_KEY=your_nvidia_nim_api_key_here GITHUB_TOKEN=your_optional_github_token_here
- Start the FastAPI server:
The interactive Swagger documentation will be available at
python -m uvicorn backend.main:app --reload --port 8000
http://localhost:8000/docs.
- In a separate terminal, navigate to the frontend directory:
cd frontend - Install dependencies:
npm install
- Configure environment variables (optional for default
http://localhost:8000):cp .env.example .env
- Start the Vite development server:
Open
npm run dev
http://localhost:5173in your browser.
Run the unit test suite covering ATS simulation, skill analysis, soft skill detection, and API health endpoints:
python -m unittest discover -s testsValidate frontend code standards and compile production assets:
cd frontend
npm run lint
npm run buildThe repository includes a ready-to-use render.yaml specification and Procfile:
buildCommand: pip install -r requirements.txt
startCommand: cd backend && uvicorn main:app --host 0.0.0.0 --port $PORTSet NVIDIA_API_KEY and GITHUB_TOKEN in your platform's environment variables.
frontend/vercel.json is pre-configured with client-side SPA routing rewrites:
# Build command:
npm run build
# Output directory:
distSet VITE_API_BASE_URL to your production backend URL (e.g. https://your-api.onrender.com).
- PDF Parsing: PyPDF2 extracts text from standard digital PDFs; scanned/image-only PDFs require an external OCR pre-processor.
- LLM Rate Limits: NVIDIA NIM developer tier includes rate quotas. Ensure an active key is present in
.env. - Live JD Scraping: Some job platforms (e.g., LinkedIn gated postings) require login sessions; in such cases, paste the raw job description text directly.
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a feature branch:
git checkout -b feature/amazing-feature. - Commit your changes:
git commit -m "Add amazing feature". - Ensure tests and lint pass:
python -m unittest discover -s tests && cd frontend && npm run lint && npm run build. - Push to your branch and submit a Pull Request.
This project is licensed under the MIT License — see the LICENSE file for details.