ResuMatch Pro

An AI-powered resume analysis and job matching web application that helps users evaluate and improve their resumes, match them against job opportunities, and plan their career roadmap.

🚀 Features

1. Resume Analysis

• Upload resumes in multiple formats (PDF, DOCX, images via OCR)
• AI-powered completeness scoring (0-100%)
• Section-by-section quality ratings (Summary, Education, Experience, Other)
• Brutally honest, actionable improvement suggestions
• Zero-hallucination policy: AI suggests improvements without inventing fake metrics

2. Job Matching

• Two modes: Custom job description OR curated role + location selection
• AI generates tailored job descriptions for role/location combinations
• Semantic alignment scoring (0-100%)
• Gap analysis with severity levels (High/Medium/Low)
• Interactive gap assessment: Rate your proficiency (None/Basic/Moderate/Advanced)
• Final AI verdict on whether to apply

3. Tailored Resume Generation

• ATS-optimized resumes customized for specific jobs
• Preserves all original sections (Volunteering, Awards, Certifications, etc.)
• Maintains all contact links (LinkedIn, GitHub, Google Scholar, etc.)
• Integrates user-confirmed skills from gap assessment
• Results-driven language enhancement (strong action verbs, impact-oriented)
• PDF download with dynamic filename: {UserName}_tailored resume.pdf

4. Career Roadmap

• Personalized career development plans
• Input: Dream role, location, timeframe (6 months to 2 years)
• AI analyzes current gaps vs. target role
• Phased action plans with specific steps
• Resource recommendations (courses, certifications, books)
• Milestone tracking

🏗️ Architecture

Backend (Python Migration in Progress)

• Framework: FastAPI (async, high-performance)
• Database: PostgreSQL via SQLAlchemy ORM
• Authentication: Password-based with bcrypt hashing
• AI Integration: Google Gemini AI (gemini-2.0-flash-exp model)
• File Processing:
  • PDF: PyPDF2
  • DOCX: python-docx
  • Images: Tesseract.js OCR
  • Max upload: 10MB

Frontend (React - Unchanged)

• Framework: React + TypeScript + Vite
• UI: shadcn/ui components + Tailwind CSS
• State: TanStack Query (React Query)
• Routing: Wouter
• PDF Generation: pdfmake (client-side)

Database Schema

users
├── id (UUID)
├── username
└── password_hash

resumes
├── id (UUID)
├── user_id (FK → users)
├── filename
├── filesize
├── mime_type
├── extracted_text
└── created_at

analyses
├── id (UUID)
├── resume_id (FK → resumes)
├── completeness_score (0-100)
├── completeness_rationale
├── section_scores (JSON)
├── suggestions (JSON array)
└── created_at

job_matches
├── id (UUID)
├── resume_id (FK → resumes)
├── job_description
├── job_role (nullable)
├── job_location (nullable)
├── alignment_score (0-100)
├── alignment_rationale
├── gaps (JSON array)
├── strengths (JSON array)
├── gap_responses (JSON array)
├── final_verdict
├── should_apply (boolean)
├── tailored_resume_content
└── created_at

career_roadmaps
├── id (UUID)
├── user_id (FK → users)
├── resume_id (FK → resumes)
├── dream_role
├── dream_location
├── timeframe
├── current_gaps (JSON array)
├── skills_to_acquire (JSON array)
├── action_plan (JSON array)
├── resources (JSON array)
├── milestones (JSON array)
└── created_at

🔧 Setup & Installation

Prerequisites

• Python 3.11+
• PostgreSQL database
• Gemini API key

Environment Variables

Create a .env file with:

DATABASE_URL=postgresql://user:password@host:port/database
SESSION_SECRET=your-secret-key-change-in-production
GEMINI_API_KEY=your-gemini-api-key

Installation

1. Install Python dependencies:

pip install -r python_requirements.txt

Or on Replit, packages are auto-installed.

2. Set up database: The application will auto-create tables on first run using SQLAlchemy.

3. Run the application:

# Development mode
uvicorn python_backend.main:app --reload --host 0.0.0.0 --port 8000

# Production mode
uvicorn python_backend.main:app --host 0.0.0.0 --port 8000

4. Build React frontend (if needed):

npm install
npm run build

📁 Project Structure

resumatch-pro/
├── python_backend/
│   ├── __init__.py
│   ├── main.py              # FastAPI app entry point
│   ├── config.py            # Configuration & environment variables
│   ├── models.py            # SQLAlchemy database models
│   ├── database.py          # Database session management
│   ├── auth.py              # Authentication utilities
│   ├── file_processor.py    # File upload & text extraction
│   ├── gemini_service.py    # Gemini AI integration
│   └── routes/              # API route handlers (to be created)
│       ├── auth.py
│       ├── resumes.py
│       ├── analyses.py
│       ├── job_matches.py
│       └── career_roadmaps.py
├── client/
│   └── src/
│       ├── App.tsx          # React app & routing
│       ├── pages/           # Page components
│       ├── components/      # Reusable UI components
│       └── lib/             # Utilities & API client
├── attached_assets/         # User-uploaded files & generated content
├── python_requirements.txt  # Python dependencies
├── package.json             # Node.js dependencies
└── README.md

🔐 Authentication

• Username + Password authentication
• Passwords hashed with bcrypt (cost factor 12)
• Session-based authentication with secure cookies
• Registration endpoint: POST /api/auth/register
• Login endpoint: POST /api/auth/login
• Logout endpoint: POST /api/auth/logout

🤖 AI Integration

Gemini AI Features

• Model: gemini-2.0-flash-exp (latest Flash model)
• Structured Output: JSON schema enforcement
• Zero-Hallucination Policy: Never invents metrics or data
• Brutally Honest Feedback: Identifies weak language, buzzwords, missing metrics
• Results-Driven Language: Transforms passive statements into impact-oriented achievements

API Endpoints (Planned)

Authentication

• POST /api/auth/register - Create new account
• POST /api/auth/login - Login with username/password
• POST /api/auth/logout - Logout
• GET /api/auth/me - Get current user

Resumes

• POST /api/resumes - Upload resume (multipart/form-data)
• GET /api/resumes - Get user's resumes
• GET /api/resumes/{id} - Get specific resume

Analyses

• POST /api/analyses - Analyze a resume
• GET /api/analyses/{resume_id} - Get analysis for resume

Job Matches

• POST /api/job-matches - Create job match analysis
• GET /api/job-matches/{resume_id} - Get job matches for resume
• PATCH /api/job-matches/{id}/responses - Submit gap proficiency responses
• POST /api/job-matches/{id}/tailored-resume - Generate tailored resume

Career Roadmaps

• POST /api/career-roadmaps - Generate career roadmap
• GET /api/career-roadmaps - Get user's career roadmaps

🎨 Frontend Pages

• Landing Page: Simple username/password login
• Dashboard: Resume upload and management
• Resume Analysis: AI analysis results with scores and suggestions
• Job Match Input: Custom JD or role+location selection
• Job Match Results: Alignment scores, gaps, strengths, recommendations
• Career Roadmap: Dream role form and career guidance

📝 Migration Notes

From Node.js to Python

• ✅ SQLAlchemy models match existing PostgreSQL schema
• ✅ UUID primary keys preserved
• ✅ All JSONB columns supported
• ✅ Authentication upgraded from username-only to username+password
• ✅ Gemini AI service ported with identical prompts
• ✅ File processing pipeline (PDF, DOCX, OCR) reimplemented
• 🚧 FastAPI routes in progress
• 🚧 Session management setup
• 🚧 React frontend integration

Database Compatibility

• Existing data preserved (users, resumes, analyses, job_matches, career_roadmaps)
• Schema unchanged - seamless migration
• Add password_hash column to users table for existing users

🧪 Testing

(To be implemented)

• Unit tests for AI service
• Integration tests for API endpoints
• E2E tests with Playwright

🚢 Deployment

The application is designed to run on Replit with:

• Auto-managed PostgreSQL database
• Environment secrets management
• One-click deployment

📄 License

Proprietary - All rights reserved

🤝 Contributing

This is a private project. Contact the owner for collaboration opportunities.

📞 Support

For issues or questions, please contact the project maintainer.

---

Built with ❤️ using FastAPI, React, and Gemini AI