AI Resume Analyzer

Overview

AI Resume Analyzer is a full-stack web application that uses artificial intelligence to analyze resumes and match them with job descriptions. The system extracts skills from PDF resumes, scores them based on industry relevance, and provides job matching capabilities with detailed recommendations for improvement.

Features

Core Functionality

Resume Upload & Analysis: Upload PDF resumes for automatic text extraction and skill identification
AI-Powered Scoring: Calculate resume quality scores based on skills, length, and diversity
Job Matching: Compare resumes against job descriptions using TF-IDF similarity algorithms
Skill Gap Analysis: Identify missing skills and provide targeted recommendations
Resume Dashboard: Track upload history, scores, and analytics

User Management

Secure Authentication: JWT-based user authentication and authorization
User Profiles: Personalized dashboard for each user
Resume History: Complete history of uploaded and analyzed resumes

Technical Features

PDF Processing: Advanced text extraction from PDF documents
Machine Learning: Custom ML models for skill extraction and job matching
Responsive Design: Mobile-first web interface
Real-time Analysis: Instant feedback and scoring results

Technology Stack

Backend

Framework: FastAPI (Python)
Database: PostgreSQL with SQLAlchemy ORM
Authentication: JWT tokens with bcrypt hashing
ML/AI: scikit-learn, custom algorithms
PDF Processing: pdfplumber
Validation: Pydantic schemas

Frontend

Framework: Next.js 16 (App Router)
Language: TypeScript
UI Library: React 19
Styling: Tailwind CSS 4
Components: shadcn/ui (Radix UI primitives)
Icons: Lucide React

Infrastructure

Deployment: Backend on Render, Frontend on Vercel
Database: PostgreSQL (production)
Environment: Python virtual environment

Project Structure

ai-resume-analyzer/
├── backend/                      # FastAPI backend application
│   ├── main.py                   # Application entry point
│   ├── database.py               # Database configuration
│   ├── requirements.txt          # Python dependencies
│   ├── api/                      # API route handlers
│   ├── models/                   # SQLAlchemy database models
│   ├── services/                 # Business logic layer
│   ├── ml/                       # Machine learning components
│   ├── schemas/                  # Pydantic validation schemas
│   ├── security/                 # Authentication utilities
│   ├── utils/                    # Utility functions
│   ├── uploads/                  # File upload directory
│   └── README.md                 # Backend documentation
├── frontend/                     # Next.js frontend application
│   ├── app/                      # Next.js App Router pages
│   ├── components/               # React components
│   ├── lib/                      # Utility libraries
│   ├── public/                   # Static assets
│   ├── package.json              # Node.js dependencies
│   └── README.md                 # Frontend documentation
├── venv/                         # Python virtual environment
└── README.md                     # This file

Prerequisites

Python 3.8+ for backend
Node.js 18+ for frontend
PostgreSQL database
Git for version control

Installation & Setup

1. Clone the Repository

git clone <repository-url>
cd ai-resume-analyzer

2. Backend Setup

# Navigate to backend directory
cd backend

# Create Python virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Set up environment variables
# Create .env file in backend directory
DATABASE_URL=postgresql://username:password@localhost:5432/resume_analyzer
SECRET_KEY=your-secret-key-here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

3. Database Setup

# Create PostgreSQL database
createdb resume_analyzer

# The application will automatically create tables on startup

4. Frontend Setup

# Navigate to frontend directory
cd ../frontend

# Install Node.js dependencies
npm install

# Configure API endpoint (optional)
# Update lib/api.ts if needed
export const API_URL = "http://localhost:8000"

Running the Application

Development Mode

Start Backend:

cd backend
venv\Scripts\activate  # Windows
uvicorn main:app --reload

Backend will be available at: http://localhost:8000

Start Frontend:
```
cd frontend
npm run dev
```
Frontend will be available at: http://localhost:3000

Production Mode

Backend Production:

cd backend
uvicorn main:app --host 0.0.0.0 --port 8000

Frontend Production:
```
cd frontend
npm run build
npm run start
```

Usage

User Registration & Login

Visit the application at http://localhost:3000
Click "Signup" to create a new account
Login with your credentials
Access the dashboard and upload functionality

Resume Analysis

Upload Resume: Click "Choose File" and select a PDF resume
Optional Job Description: Paste a job description for matching
Analyze: Click "Upload & Analyze" to process the resume
View Results: See ATS score, job match percentage, skills found, and recommendations

Dashboard Features

View all uploaded resumes
Check individual scores and match results
Track analysis history
Download processed resumes

API Documentation

Authentication Endpoints

POST /auth/login - User login (OAuth2 form data)
POST /users - User registration

Resume Endpoints

POST /resume/upload - Upload and analyze resume
POST /resume/match - Match resume with job description
GET /resume/history - Get user's resume history
GET /resume/dashboard - Get dashboard analytics
GET /resume/progress - Get processing progress
PUT /resume/{id} - Update resume
DELETE /resume/{id} - Delete resume
GET /resume/download/{id} - Download resume file

User Endpoints

GET /users/me - Get current user information
PUT /users/me - Update user profile

Full API documentation available at: http://localhost:8000/docs (Swagger UI)

Machine Learning Components

Resume Scoring Algorithm

Skill Weights: Industry-specific skill importance (Python: 10, Git: 6, etc.)
Length Bonus: Optimal resume length (200-600 words)
Diversity Bonus: Points for multiple skill categories
Maximum Score: 100 points

Skill Extraction

Method: Regex pattern matching against predefined skill database
Coverage: Technical skills (programming languages, frameworks, tools)
Accuracy: Case-insensitive matching with word boundaries

Job Matching

Algorithm: TF-IDF vectorization with cosine similarity
Input: Resume text vs. job description text
Output: Match percentage (0-100%)

Development

Code Quality

Backend: Type hints, Pydantic validation, SQLAlchemy best practices
Frontend: TypeScript strict mode, ESLint, component composition
Testing: Unit tests for ML algorithms, API endpoint testing

Adding New Features

Backend: Add models, services, and API routes following existing patterns
Frontend: Create components in /components, pages in /app
ML: Extend algorithms in /backend/ml directory

Environment Variables

# Backend
DATABASE_URL=postgresql://user:pass@localhost:5432/db
SECRET_KEY=your-256-bit-secret
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# Frontend (if needed)
NEXT_PUBLIC_API_URL=http://localhost:8000

Deployment

Backend Deployment (Render)

Connect GitHub repository
Set environment variables
Configure PostgreSQL database
Deploy with uvicorn

Frontend Deployment (Vercel)

Connect GitHub repository
Set build settings (Next.js default)
Configure environment variables
Deploy automatically

Database

Use managed PostgreSQL (Render, Railway, or AWS RDS)
Set connection pooling for production
Enable SSL connections

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow existing code structure and naming conventions
Add type hints and TypeScript types
Write descriptive commit messages
Test your changes thoroughly
Update documentation for new features

Troubleshooting

Common Issues

Backend Connection Issues:

Verify PostgreSQL is running and credentials are correct
Check .env file configuration
Ensure virtual environment is activated

Frontend API Errors:

Confirm backend is running on correct port
Check API_URL in frontend/lib/api.ts
Verify CORS configuration in backend

PDF Processing Errors:

Ensure PDF files are not corrupted
Check file size limits
Verify pdfplumber installation

Authentication Issues:

Clear browser localStorage
Check JWT token expiration
Verify backend secret key configuration

Logs and Debugging

Backend: Check uvicorn console output
Frontend: Use browser developer tools
Database: Check PostgreSQL logs

License

[Add your license information here]

Support

For questions, issues, or contributions:

Create an issue in the GitHub repository
Check the backend and frontend READMEs for detailed documentation
Review API documentation at /docs endpoint

Roadmap

Planned Features

Advanced ML models for better skill extraction
Resume optimization suggestions
Multiple file format support (DOCX, TXT)
Team collaboration features
Analytics dashboard improvements
Mobile app development

Name		Name	Last commit message	Last commit date
.vscode		.vscode
backend		backend
frontend		frontend
.gitignore		.gitignore
README.md		README.md

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