🎯 Cvette

Match your resume. Land the interview.

Cvette is a small, public AI-powered web app that compares a resume against a job description and returns a match score, missing keywords, a short summary, and rewritten resume bullet suggestions — with a downloadable PDF report. Built as a fast, focused tool rather than a full platform: paste or upload, get results, done. No login, no saved history.

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🚀 Features (planned)

Core Analysis

• Paste resume + job description as plain text, or upload as PDF/DOCX
• AI-powered match score (0–100)
• Matched and missing keyword extraction
• Short plain-English summary of fit
• 3–5 rewritten resume bullet suggestions tailored to the job description

Output

• On-screen results view
• Downloadable PDF report of the analysis

Public-facing

• Fully stateless — no accounts, no saved data
• Rate-limited per IP to keep the tool sustainable on a free AI API tier

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🛠 Tech Stack

Backend

• FastAPI
• Groq API (llama-3.3-70b-versatile)
• pdfplumber (PDF text extraction)
• python-docx (DOCX text extraction)
• ReportLab (PDF report generation)
• slowapi (rate limiting)

Frontend

• React
• Vite

Development Tools

• Git
• GitHub
• VS Code
• Postman

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📁 Project Structure

cvette/
├── backend/
│   ├── main.py
│   ├── config.py
│   ├── requirements.txt
│   ├── .env                 # local only — gitignored, never committed
│   ├── schemas.py
│   └── services/
│       ├── extract.py       # PDF/DOCX -> plain text
│       ├── groq_client.py   # Groq API call + prompt
│       └── report.py        # ReportLab PDF generation
├── frontend/                # Vite + React
├── .gitignore
└── README.md

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⚙️ Installation

Clone Repository

git clone https://github.com/studyhaxer/cvette.git
cd cvette

Backend Setup

Create virtual environment

python -m venv venv

Activate virtual environment

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

Install dependencies

pip install -r requirements.txt

Configure Environment Variables

Create a .env file inside backend/:

GROQ_API_KEY=your_groq_api_key

Never commit .env. It's listed in .gitignore — double-check with git status that it shows as untracked before your first commit. If a real API key is ever accidentally committed, treat it as compromised: regenerate the key immediately rather than relying on removing it from history.

Run the Backend

From backend/:

uvicorn main:app --reload

• Server: http://127.0.0.1:8000
• Swagger Documentation: http://127.0.0.1:8000/docs

Run the Frontend

From frontend/:

npm install
npm run dev

• App: http://localhost:5173

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🗺 Development Roadmap

Step 1

• Project scaffold (backend + frontend folder structure)

Step 2

• /analyze endpoint — plain text input, Groq integration, JSON response

Step 3

• File upload support (PDF via pdfplumber, DOCX via python-docx)

Step 4

• Frontend — input forms (paste/upload toggle), results display

Step 5

• /report endpoint — PDF report generation via ReportLab, download button on frontend

Step 6

• Deployment (Railway), rate limiting (slowapi), CORS configuration

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📖 API Documentation

Interactive API documentation is available after running the server.

Swagger UI

/docs

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📈 Future Improvements

• Automated testing (pytest suite)
• Docker Compose setup
• Support for additional file formats (e.g. .txt, .rtf)
• Multi-language resume support

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🤝 Contributing

Contributions are welcome.

1. Fork the repository
2. Create a feature branch

git checkout -b feature/your-feature

3. Commit your changes

git commit -m "Add new feature"

4. Push to GitHub

git push origin feature/your-feature

5. Create a Pull Request

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👤 Author

Hafiz Atta Ur Rahman Backend Developer | Python | FastAPI | React

• GitHub: https://github.com/studyhaxer
• LinkedIn: https://linkedin.com/in/studyhaxer

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📄 License

This project is licensed under the MIT License.

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⭐ Project Status

🚧 Day 2 — Core AI Matching Endpoint Live

Cvette is being built incrementally and documented publicly as a build-in-public project. Follow along for daily updates as each step of the roadmap above is completed.