Intelligent Resume Analyzer (React + FastAPI)

A professional, full-stack NLP application for resume analysis, matching, and optimization.

🚀 How to Run the Project

Since this is a full-stack application, you need to start two servers: the Backend (Python) and the Frontend (React).

0. Environment Setup (for GitHub clone)

Create a .env file in the project root (or copy from .env.example) and add:

• GEMINI_API_KEY=your_gemini_api_key_here (optional, enables AI insights)
• FIREBASE_KEY_PATH=backend/firebase_key.json (optional, enables history persistence)

1. Start the Backend (FastAPI)

Open a new terminal and run:

cd backend
# Install dependencies if you haven't yet
pip install -r requirements.txt
# Start the server
uvicorn main:app --reload

The backend will run on http://localhost:8000.

2. Start the Frontend (React + Vite)

Open a second terminal and run:

cd frontend
# Install npm packages if you haven't yet
npm install
# Start the development server
npm run dev

The frontend will run on http://localhost:5173.

3. Access the App

Open your browser and go to: http://localhost:5173

---

🛠️ Tech Stack

• Frontend: React.js (Vite), Tailwind CSS (for layout), Lucide Icons, Framer Motion (animations).
• Backend: FastAPI (Python), NLTK, Scikit-learn.
• Database: Firebase Firestore (for analysis history).
• NLP Techniques: TF-IDF, Cosine Similarity, Regex-based Extraction, Extractive Summarization.

📁 Project Structure

• /backend: Contains the FastAPI app, NLP engine, and Firebase config.
• /frontend: Contains the React source code, components, and design system.
• PROJECT_REPORT.md: Full academic project report.

🔑 Firebase Setup (Optional for History)

To enable the "History" feature:

1. Go to your Firebase Console.
2. Generate a Service Account Key (JSON).
3. Save it as backend/firebase_key.json. The app will work perfectly fine without this key, but it won't save your history.