ResumeAnalyzer AI - AI-Powered Resume Analysis & Job Matching Platform

Live Application: https://resumeanalyzerai.com

An enterprise-grade AI resume optimization platform that helps job seekers land their dream jobs through intelligent resume analysis, ATS optimization, and personalized job matching. Built with production-ready Flask backend and React frontend, featuring advanced machine learning algorithms, NLP-powered skill extraction, and real-time market intelligence.

ResumeAnalyzer AI streamlines the job search process by providing data-driven insights, identifying resume gaps, and connecting candidates with the most relevant opportunities.

Modern, responsive landing page with clear call-to-action and feature highlights

✨ Key Features

Resume Analysis & Optimization

• AI-Powered Analysis: Gemini 1.5 integration for intelligent resume feedback
• Match Scoring: TF-IDF and cosine similarity algorithms for job-resume matching
• Keyword Extraction: spaCy NLP for skills and keyword identification
• Gap Analysis: Identifies missing skills and provides improvement suggestions
• Cover Letter Generation: AI-generated personalized cover letters

AI-powered resume analysis with instant ATS compatibility scoring and actionable recommendations

Job Matching & Market Intelligence

• Semantic Job Matching: ML-based job recommendations using TF-IDF vectorization
• Market Trends Analysis: Real-time job market insights and salary data
• Skills Demand Tracking: Identify in-demand skills across industries
• Salary Intelligence: Competitive salary ranges for target roles
• Top Companies: Discover leading employers in your field

Track and manage all your job applications in one organized dashboard

Authentication & User Management

• JWT Authentication: Secure token-based authentication
• Google OAuth Integration: One-click social login
• Guest Access: Try features without registration (with limits)
• Admin Dashboard: User management and system diagnostics
• Email Verification: Resend integration for secure account activation

User Experience

• Interactive Dashboard: Historical analysis tracking with visualizations
• PDF Resume Upload: Secure file processing and storage
• Email Delivery: Results sent via Resend (on-demand)
• Responsive Design: Mobile-friendly React UI
• Real-time Feedback: Interactive skill verification system

Comprehensive dashboard with analysis history, insights, and performance metrics

🛠️ Technology Stack

Backend

• Framework: Flask 3.0 (Python 3.11+)
• Database: PostgreSQL with SQLAlchemy 2.0 ORM
• AI/ML: Google Gemini 1.5, spaCy NLP, scikit-learn (TF-IDF, cosine similarity)
• Authentication: Flask-JWT-Extended, Google OAuth 2.0
• File Processing: PyPDF2, python-docx
• Testing: pytest, pytest-cov
• Deployment: Gunicorn WSGI server, Docker

Frontend

• Framework: React 18
• Routing: React Router v6
• HTTP Client: Axios
• Styling: CSS3, Responsive Design
• Deployment: Nginx, Docker

Infrastructure

• Hosting: Render.com (auto-deploy from GitHub)
• Database: Render PostgreSQL
• Domain: resumeanalyzerai.com
• CI/CD: GitHub Actions

🚀 Quickstart (Docker)

Requirements: Docker & Docker Compose

1. Build and start services:

docker-compose up --build

2. Access the application:
   • Backend API: http://localhost:5000
   • Frontend: http://localhost:3000

For manual local setup without Docker, see SETUP_GUIDE.md or the backend/ and frontend/ README files.

Repo layout

.
├── backend/            # Flask API, models, AI processing, tests, migrations
├── frontend/           # React app (Create React App)
├── migrations/         # Alembic DB migrations
├── tests/              # Backend pytest tests
├── docker-compose.yml
├── README_V2.md        # More detailed production README
└── SETUP_GUIDE.md      # Configuration and setup notes

Quick links:

• Backend README: README_BACKEND.md
• Frontend README: README_FRONTEND.md
• Developer quickstart: DEV_QUICKSTART.md

Backend — local dev

1. Create and activate a virtualenv

cd backend
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
python -m spacy download en_core_web_sm

2. Configure environment variables (copy .env.example if present)

3. Initialize the database and run migrations

# create DB (Postgres)
# adjust DATABASE_URL as needed
alembic upgrade head

4. Run the app

python app.py

Frontend — local dev

cd frontend
npm install
cp env.example .env.local
# edit .env.local to point REACT_APP_API_URL to the backend
npm start

Testing

• Backend tests: run from backend/ using pytest

cd backend
pytest -q

• Frontend tests: run from frontend/

cd frontend
npm test

🚀 Deployment

Detailed analytics and performance tracking for your job search journey

Production Environment

Current Deployment: Render.com (auto-deploy from GitHub main branch)

• Frontend: https://resumeanalyzerai.com
• Backend API: https://resumatch-backend-7qdb.onrender.com
• Database: PostgreSQL (Render managed)

Local Deployment

# Using Docker Compose (recommended)
docker-compose up --build

# Backend will be at http://localhost:5000
# Frontend will be at http://localhost:3000

Production Setup Checklist

1. Environment Variables (set in Render dashboard):

   • DATABASE_URL - PostgreSQL connection string
   • JWT_SECRET_KEY - Secure random key for JWT
   • GEMINI_API_KEY - Google Gemini API key
   • RESEND_API_KEY - Resend API key for email delivery
   • GOOGLE_CLIENT_ID - Google OAuth
   • GOOGLE_CLIENT_SECRET - Google OAuth
   • ADZUNA_APP_ID - Adzuna job API (optional)
   • ADZUNA_APP_KEY - Adzuna job API (optional)

2. Database Initialization:

   # In Render shell
   cd /app && python final_db_init.py

3. Admin Account Setup:

   # Configure admin accounts via environment variables or database
   cd /app && python update_admin_passwords.py

4. Sample Data (for demo/presentation):

   # Insert 5 sample job postings
   cd /app && python insert_sample_jobs.py

Admin Access

Admin dashboard provides comprehensive system diagnostics, user management, and analytics.

Access admin diagnostics at: /api/v1/admin/diagnostics/full-diagnostic

Configuration

Edit environment variables in docker-compose.yml or in a local .env file. Important variables include DATABASE_URL, JWT_SECRET_KEY, GEMINI_API_KEY, and RESEND_API_KEY for email. See SETUP_GUIDE.md for more details.

🏗️ Architecture & Technical Implementation

ResumeAnalyzer AI demonstrates enterprise-level software engineering practices:

• AI/ML Integration: Google Gemini 1.5 for intelligent resume feedback, spaCy for NLP, scikit-learn for semantic job matching
• Full-Stack Architecture: RESTful API with Flask + modern React SPA with responsive design
• Database Design: PostgreSQL with 28+ tables, optimized indexing, and proper relationships
• Cloud Infrastructure: Production deployment on Render with automated CI/CD pipelines
• Security & Authentication: JWT tokens, OAuth 2.0 (Google), bcrypt password hashing, CSRF protection, input validation
• Software Engineering: Comprehensive test coverage (pytest), Docker containerization, Git version control
• Data Visualization: Interactive dashboards with historical analysis tracking
• Scalability: Rate limiting, caching, async processing, connection pooling
• Monitoring & Logging: Structured logging, performance metrics, error tracking

🙏 Acknowledgments

Built with powerful open-source technologies:

• Google Gemini for advanced AI capabilities
• spaCy for natural language processing
• scikit-learn for machine learning algorithms
• Flask and React communities for robust frameworks
• PostgreSQL for reliable data storage

📧 Support

For technical support or questions:

1. Check the comprehensive documentation in SETUP_GUIDE.md
2. Review API documentation for integration details
3. Check application logs: docker-compose logs backend
4. Visit the help center at resumeanalyzerai.com/help

📄 License

This project is proprietary software. All rights reserved.

For licensing inquiries, please contact the development team.