🌟 Career Stat Card

⚡ Generate FIFA-style career cards from your resume, powered by Google's Gemini AI.

Career Stat Card is an AI-powered application that parses your resume and a job title or description, then scores your abilities across six core career dimensions (e.g., Technical Proficiency, Adaptability). These scores are visualized as a card with hoverable explanations — much like a FIFA Ultimate Team card. This is just a fun little project that is a result of me wondering what my own card would look like in FIFA.

🧠 Built using Google Gemini + FastAPI + React.js.

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🚀 Live Demo

Frontend: Deployed on GitHub Pages
Backend (FastAPI Swagger): Deployed on Render

Note: The backend may take a few seconds to respond on the first call due to Render’s free tier limits on API use.

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🧩 Features

• 📝 Upload your resume (PDF) + a job title or description
• ⚙️ Uses the Google Gemini AI API (gemini-2.5-flash-preview-05-20) to extract and rate your resume
• 🎮 Outputs a FIFA-style card with stats and career fit, scaling stats to fit experience in the field
• 🧾 Hover over stats to reveal explanations
• 📦 Containerized backend with Docker + deployed frontend via GitHub Pages

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🧠 Stats Included

Attribute	Description
Technical Proficiency	Tools, libraries, and engineering skillset
Problem Solving	Your ability to solve complex challenges
Communication	How clearly you convey technical work
Domain Fit	Alignment to the given job title or description
Initiative & Impact	Measurable contributions and leadership
Adaptability	How flexible and versatile your background is
Overall Score	Weighted average of the above

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🛠️ Tech Stack

Layer	Tools
Backend	Python ·FastAPI · Google Gemini API · PyMuPDF · Docker
Frontend	React.js · JavaScript · Vite · CSS
Hosting	Render.com · GitHub Pages

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💻 Running Locally

1. Clone this repo:

git clone https://github.com/unnamed-catalyst/Career-Stat-Card.git
cd Career-Stat-Card

2. Start the backend:

cd backend
docker build -t career-stat-api .
docker run -p 8000:8000 career-stat-api

The API will be available at http://localhost:8000.

3. Start the frontend:

cd frontend
npm install
npm run dev

The frontend should be available at http://localhost:5173.