🚀 ResumeForge AI - Intelligent Resume Optimization Platform

Transform your resume with cutting-edge multi-agent AI technology. Get ATS-optimized resumes, career guidance, and comprehensive quality scores—all in seconds.

✨ Features

🤖 Multi-Agent AI System

Document Sanitization Specialist: Cleans and normalizes resume text
ATS Optimization Strategist: Engineers high-performance, ATS-friendly resumes
Achievement Architect: Transforms bullets into quantified impact statements
Compatibility Analyst: Provides detailed ATS scoring and gap analysis
Career Navigator: Offers personalized career guidance and next steps
Quality Metrics Evaluator: Conducts 10-dimensional quality assessment

📊 Key Capabilities

✅ 85+ ATS Compatibility Score
✅ Strategic Keyword Matching
✅ Quantified Achievement Enhancement
✅ Multi-Format Support (PDF, DOCX, TXT)
✅ Professional PDF Export
✅ Comprehensive Quality Scoring
✅ Career Guidance & Roadmaps

🎨 Modern Tech Stack

Backend:

FastAPI (High-performance async API)
CrewAI (Multi-agent orchestration)
GPT-4o-mini (Cost-effective AI)
ReportLab (Professional PDF generation)

Frontend:

React 18 with Vite
Tailwind CSS (Modern UI)
Framer Motion (Smooth animations)
Axios (API communication)

🚀 Quick Start

Prerequisites

Python 3.10+
Node.js 18+
OpenAI API Key

Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

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

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

# Run backend server
uvicorn backend.api.main:app --reload --port 8000

Backend will run at: http://localhost:8000

API Documentation: http://localhost:8000/api/docs

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Run development server
npm run dev

Frontend will run at: http://localhost:3000

📁 Project Structure

resume_parse_crewai/
├── backend/                      # Python FastAPI backend
│   ├── api/
│   │   └── main.py              # FastAPI application & endpoints
│   ├── core/
│   │   ├── ai_specialists.py    # 6 AI agent definitions
│   │   ├── workflow_orchestrator.py  # Pipeline orchestration
│   │   └── workflow_tasks.py    # Task definitions
│   ├── services/
│   │   └── document_processor.py # File parsing & PDF generation
│   └── requirements.txt         # Python dependencies
├── frontend/                     # React frontend
│   ├── src/
│   │   ├── components/          # React components
│   │   │   ├── Header.jsx
│   │   │   ├── UploadSection.jsx
│   │   │   ├── ResultsSection.jsx
│   │   │   └── FeaturesSection.jsx
│   │   ├── services/
│   │   │   └── api.js           # API client
│   │   ├── App.jsx
│   │   ├── main.jsx
│   │   └── index.css
│   ├── package.json             # Node dependencies
│   └── vite.config.js           # Vite configuration
├── vercel.json                  # Vercel deployment config
├── railway.json                 # Railway deployment config
├── nixpacks.toml               # Railway build config
├── STEP_BY_STEP_DEPLOYMENT.md  # Beginner-friendly deployment guide
├── QUICK_DEPLOY_VERCEL_RAILWAY.md # Quick deployment steps
├── DEPLOYMENT_GUIDE_VERCEL_RAILWAY.md # Complete deployment guide
└── README.md                    # This file

🔧 API Endpoints

Core Endpoints

Method	Endpoint	Description
`GET`	`/`	Health check
`GET`	`/api/health`	Detailed health status
`POST`	`/api/optimize-file`	Optimize resume from file upload
`POST`	`/api/optimize`	Optimize resume from text
`POST`	`/api/career-guidance`	Get career guidance
`POST`	`/api/quality-score`	Get quality assessment
`POST`	`/api/download/pdf`	Download resume as PDF
`POST`	`/api/download/docx`	Download resume as DOCX

Example API Usage

# Upload and optimize resume
curl -X POST http://localhost:8000/api/optimize-file \
  -F "file=@resume.pdf" \
  -F "job_title=Senior Software Engineer" \
  -F "job_description=Looking for Python expert..."

🎯 How It Works

1. Document Upload

User uploads resume (PDF, DOCX, or TXT) via drag-and-drop interface.

2. AI Processing Pipeline

Document → Sanitization → ATS Optimization → Achievement Enhancement → Evaluation

Stage 1: Sanitization

Remove artifacts and normalize formatting
Preserve all substantive content
Clean structure for AI processing

Stage 2: ATS Optimization

Extract keywords from job description
Strategic keyword placement
Standard ATS-friendly section headers
Optimize for 85+ compatibility score

Stage 3: Achievement Enhancement

Transform bullets using STAR method
Add quantified metrics (%, $, time)
Employ strong action verbs
Highlight business impact

Stage 4: Compatibility Evaluation

Multi-dimensional scoring (5 categories)
Identify missing keywords
Provide actionable quick wins
Generate comprehensive report

3. Results Display

4 tabs showing each processing stage
Interactive evaluation dashboard
One-click PDF/DOCX download
Professional formatting

🌟 New Features (v2.0)

1. Enhanced Resume Scoring System

Comprehensive 10-dimensional quality assessment:

Clarity
Impact
Keyword Optimization
Quantification
Formatting
Achievement Focus
Storytelling
Skills Relevance
Experience Depth
Overall Polish

2. AI Career Coach

Personalized career guidance including:

Skill gap analysis
Recommended certifications/courses
Career transition timeline
Transferable skills identification
Target company suggestions
Networking strategies

3. Professional PDF Export

Clean, ATS-friendly formatting
Professional typography
Optimized for printing and digital viewing
Consistent spacing and structure

🚀 Deployment - Vercel + Railway (Recommended)

Quick Deploy (2-3 minutes)

Backend (Railway):

Go to railway.app → "New Project"
Connect GitHub repository
Add environment variable: OPENAI_API_KEY = sk-your-key
Deploy automatically

Frontend (Vercel):

Go to vercel.com → "New Project"
Connect GitHub repository
Add environment variable: VITE_API_URL = https://your-backend.railway.app
Deploy automatically

Why Vercel + Railway?

✅ No sleep time (unlike Render free tier)
✅ Faster deployment (2-3 minutes vs 10+ minutes)
✅ Better performance (global CDN)
✅ Easier setup (no complex config files)
✅ Free forever (generous limits)

📖 Step-by-Step Guide: STEP_BY_STEP_DEPLOYMENT.md (Beginner-friendly) ⚡ Quick Deploy: QUICK_DEPLOY_VERCEL_RAILWAY.md 📚 Full Guide: DEPLOYMENT_GUIDE_VERCEL_RAILWAY.md

Manual Deployment

Backend:

cd backend
pip install -r requirements.txt
gunicorn backend.api.main:app --workers 2 --worker-class uvicorn.workers.UvicornWorker

Frontend:

cd frontend
npm install
npm run build
npm run preview

🔒 Environment Variables

Railway Backend

OPENAI_API_KEY=sk-your-actual-key-here
ENVIRONMENT=production
ALLOWED_ORIGINS=https://your-frontend.vercel.app

Vercel Frontend

VITE_API_URL=https://your-backend.railway.app

Local Development

# Backend (.env)
OPENAI_API_KEY=sk-your-actual-key-here
PORT=8000
ENVIRONMENT=development

# Frontend
VITE_API_URL=http://localhost:8000

📊 Performance Metrics

Processing Time: 25-50 seconds per resume
ATS Score Target: 85+/100
Keyword Match Rate: 80-95%
Cost per Resume: ~$0.01-0.03 (GPT-4o-mini)
Supported File Sizes: Up to 200MB
Concurrent Users: Optimized for 100+ simultaneous users

🛠️ Development

Run Tests

# Backend
cd backend
pytest

# Frontend
cd frontend
npm test

Linting

# Backend
flake8 backend/

# Frontend
npm run lint

Build for Production

# Frontend
npm run build

🐛 Troubleshooting

Common Issues

1. API Key Error

AuthenticationError: Incorrect API key

Solution: Verify OPENAI_API_KEY in .env file

2. CORS Error

Access-Control-Allow-Origin error

Solution: Update ALLOWED_ORIGINS in Railway backend environment variables

3. File Upload Fails

Could not extract text from file

Solution: Ensure file is valid PDF/DOCX with readable text

4. Port Already in Use

Address already in use

Solution: Kill process on port 8000/3000 or use different port

📈 Roadmap

Coming Soon

🤝 Contributing

We welcome contributions! Please follow these steps:

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

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

CrewAI for multi-agent orchestration
OpenAI for GPT models
FastAPI for modern Python APIs
React for dynamic UIs
Tailwind CSS for beautiful styling

📧 Contact

For questions or support, please open an issue on GitHub.

🎯 Transform your career with AI-powered resume optimization!

Ready to get started?

Follow the Step-by-Step Deployment Guide
Deploy to Vercel + Railway (free)
Upload your resume
Get your ATS-optimized resume in seconds!

Deployment Guides:

📖 Step-by-Step Guide (Beginner-friendly)
⚡ Quick Deploy (Fast setup)
📚 Complete Guide (Detailed)

After Deployment:

🌐 Your App: https://your-app.vercel.app
🔧 API: https://your-app.railway.app
📚 API Docs: https://your-app.railway.app/api/docs

Built with ❤️ using FastAPI, React, and CrewAI

Name		Name	Last commit message	Last commit date
backend		backend
frontend		frontend
.gitignore		.gitignore
COMMAND_DEPLOYMENT_GUIDE.md		COMMAND_DEPLOYMENT_GUIDE.md
DEPLOYMENT_GUIDE_VERCEL_RAILWAY.md		DEPLOYMENT_GUIDE_VERCEL_RAILWAY.md
DEPLOYMENT_SUMMARY.md		DEPLOYMENT_SUMMARY.md
QUICK_DEPLOY_VERCEL_RAILWAY.md		QUICK_DEPLOY_VERCEL_RAILWAY.md
README.md		README.md
STEP_BY_STEP_DEPLOYMENT.md		STEP_BY_STEP_DEPLOYMENT.md
vercel.json		vercel.json

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