OptiCV 🚀

OptiCV is an advanced AI-powered resume optimization platform designed to help job seekers bypass Applicant Tracking Systems (ATS). By leveraging Google's Gemini 2.5 Flash model, OptiCV parses, analyzes, scores, and rewrites resumes to align with specific job descriptions.

Demo video (Google Drive): Watch the demo walkthrough

Deployed to Vercel: frontend and backend are deployed. Production frontend URL: https://opti-cv-flame.vercel.app

✨ Features

• 🔍 AI Resume Parsing: Extracts structured data (skills, experience, education) from PDF and DOCX files using pdfplumber and python-docx.
• 📊 Hybrid ATS Scoring: Combines algorithmic keyword matching with AI-driven semantic analysis to provide a realistic 0-100 ATS score.
• ✨ Intelligent Optimization: Rewrites resume bullet points to naturally include job description keywords without "stuffing".
• 📝 Live Resume Editor: Split-screen editor to fine-tune AI suggestions with real-time preview.
• 🎨 ATS-Friendly Downloads: Generates clean, single-column PDF and DOCX files guaranteed to be parseable by ATS software.
• 💬 AI Career Coach: Built-in chatbot to answer career questions and suggest improvements based on your specific resume context.
• 🔗 Hyperlink Preservation: Intelligently preserves LinkedIn, GitHub, and Portfolio links during the optimization process.

🛠️ Tech Stack

Frontend

• Framework: Next.js 14 (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• Components: Shadcn/UI + Lucide React
• State Management: React Hooks

Backend

• Framework: FastAPI (Python 3.12+)
• AI Model: Google Gemini 2.5 Flash (google-genai SDK)
• Parsing: pdfplumber, python-docx
• Generation: fpdf2 (PDF), python-docx (Word)
• Validation: Pydantic

📂 Project Structure

OptiCV/
├── backend/                 # FastAPI Python Backend
│   ├── src/
│   │   ├── app.py           # Entry point
│   │   ├── services/        # Logic (AI, Parsing, Scoring)
│   │   ├── routers/         # API Endpoints
│   │   └── models/          # Pydantic Schemas & Prompts
│   ├── requirements.txt     # Python Dependencies
│   └── .env                 # API Keys (GitIgnored)
│
├── frontend/                # Next.js Frontend
│   ├── src/
│   │   ├── app/             # App Router Pages
│   │   ├── components/      # UI Components
│   │   └── lib/             # API Utilities
│   ├── package.json         # Node Dependencies
│   └── vercel.json          # Deployment Config
│
├── PROMPTS.md               # Documentation of AI Prompts
└── README.md                # Project Documentation

🚀 Getting Started

Prerequisites

• Python 3.10+ installed
• Node.js 18+ installed
• A Google Gemini API Key (Get it free at aistudio.google.com)

1. Backend Setup

Open a terminal in the backend folder:

cd backend

# Create virtual environment
python -m venv venv

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

# Install dependencies
pip install -r requirements.txt

# Create .env file
echo GEMINI_API_KEY=your_actual_api_key_here > .env

# Start the server
uvicorn src.app:app --reload --port 8000

The backend runs at http://localhost:8000

2. Frontend Setup

Open a new terminal in the frontend folder:

cd frontend

# Install dependencies
npm install
# OR
# bun install

# Start the development server
npm run dev

The frontend runs at http://localhost:3000

🌐 Deployment (Vercel)

This project is optimized for deployment on Vercel as two separate projects (Frontend & Backend) linked via rewrites.

1. Deploy Backend:

   • Create a new Vercel project pointing to the backend/ subdirectory.
   • Set framework to Other.
   • Add Environment Variable: GEMINI_API_KEY.
   • Copy the deployment URL (e.g., https://opticv-api.vercel.app)

2. Deploy Frontend:

   • Create a new Vercel project pointing to the frontend/ subdirectory.
   • Set framework to Next.js.
   • Note: The vercel.json in frontend usually handles rewrites, but you may need to update the rewrite destination if cross-domain issues arise.

📜 License

MIT License. Free to use and modify.