Juno Cheung

Brandon Morales

Isaac Vivar

Patrick Cao

Resume Analyzer Project

A comprehensive resume analysis system that uses machine learning to match resumes with job market requirements, providing personalized feedback and recommendations.

Project Description

The Resume Analyzer is a comprehensive web application that helps job seekers improve their resumes by analyzing them against current job market trends. The system identifies skills present in the resume, highlights missing in-demand skills, and provides personalized recommendations for improvement.

Key Features

• Resume Parsing: Extracts text and key information from PDF, DOCX, and TXT resumes
• Skill Detection: Identifies technical and soft skills present in the resume
• Market Comparison: Compares identified skills against current job market demands
• Gap Analysis: Highlights missing skills that are in high demand
• Personalized Recommendations: Provides tailored advice to improve resume effectiveness

System Architecture

The project consists of three main components:

1. API Server (FastAPI): Handles user interactions, file uploads, and results display
2. ML Matcher Service (Flask): Performs resume parsing, skill extraction, and analysis
3. MongoDB Database: Stores resume data, analysis results, and job market information

Installation and Setup

Prerequisites

• Docker and Docker Compose
• Git

Clone the Repository

git clone https://github.com/software-students-spring2025/5-final-deployables.git

Environment Variables

Edit the .env file to set your specific configuration values.

Running the Application

Start all services using Docker Compose:

docker-compose up -d

This will start the API server, ML matcher service, and MongoDB database.

Accessing the Application

Once all services are running, you can access the application at:

• Web Interface: http://localhost:8000
• API Server Documentation: http://localhost:8000/docs
• ML Service API: http://localhost:5000/analyze (POST endpoint)

Development and Testing

Running Tests

To run tests for the API server:

cd api_server
pytest tests/ --cov=app

To run tests for the ML matcher:

cd ml_matcher
pytest tests/ --cov=app

Uploading a Sample Resume

1. Visit http://localhost:8000 in your browser
2. Enter your name and email
3. Upload a resume file (PDF, DOCX, or TXT format)
4. Click "Analyze Resume"
5. View the analysis results, including:
   • Overall match score
   • Identified skills
   • Missing in-demand skills
   • Personalized recommendations

API Documentation

API Server Endpoints

• GET /: Main web interface
• POST /upload: Upload and analyze a resume
• GET /results/{resume_id}: View analysis results for a specific resume
• GET /api/analyses: List recent analyses (JSON)

ML Service Endpoints

• POST /analyze: Analyze a resume file

CI/CD Pipeline

The project uses GitHub Actions for CI/CD:

• Automatically builds and tests on push or pull request to main branch
• Pushes Docker images to Docker Hub
• Deploys to Digital Ocean

Required GitHub Secrets

For CI/CD to work, set the following GitHub Secrets:

• DOCKER_USERNAME: Docker Hub username
• DOCKER_PASSWORD: Docker Hub password
• DIGITALOCEAN_HOST: Digital Ocean droplet IP
• DIGITALOCEAN_USERNAME: SSH username
• DIGITALOCEAN_SSHKEY: SSH private key

MongoDB Data Structure

The MongoDB database contains three collections:

• resumes: Stores information about uploaded resumes
• analyses: Stores analysis results
• job_skills: Stores job market data including skill demand levels