SmartResume - AI-Powered Resume Analyzer

SmartResume is an advanced, AI-driven application designed to help job seekers optimize their resumes for Applicant Tracking Systems (ATS) and human recruiters. It combines Classical Machine Learning (XGBoost) with Generative AI (Google Gemini) to provide a holistic score and actionable feedback.

🚀 Key Features

• Hybrid Scoring Engine:
  • Quantitative Analysis (70%): Powered by an XGBoost model trained on 50k+ resumes to evaluate structure, keyword optimization, and formatting.
  • Qualitative Analysis (30%): Uses Google Gemini Pro to assess "soft" metrics like tone, impact, and language clarity.
• Adaptive Learning System: A self-improving engine that learns new trending skills from high-scoring resumes and adjusts its criteria dynamically over time.
• Real-time Feedback: Instant, actionable suggestions to improve your ATS score.
• Premium UI/UX: A monochrome, professional design with high-end animations (Framer Motion, GSAP).
• Secure & Private: Implements OAuth2 authentication and secure PDF storage via Supabase.

🏗️ Technical Architecture

graph TD
    User((User)) -->|Upload Resume| React[Frontend - React/Tailwind]
    React -->|API Request| FastAPI[Backend - FastAPI]
    FastAPI -->|Extract Text| PDF[pdfminer.six]
    FastAPI -->|Check History| Supabase[(Supabase - Auth/DB)]
    FastAPI -->|ML Scoring| XGB[XGBoost Model]
    FastAPI -->|Contextual Analysis| Gemini[[Google Gemini AI]]
    XGB -->|Score| Result[Hybrid Analysis Result]
    Gemini -->|Feedback| Result
    Result -->|Visualize| React

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🛠️ Technology Stack

Frontend

• Framework: React 18
• Build Tool: Parcel
• Styling: TailwindCSS v4
• Animations: Framer Motion, GSAP
• Visualization: Recharts

Backend

• API Framework: FastAPI (Python)
• ML Engine: XGBoost, Scikit-Learn
• GenAI: Google Gemini Pro API
• Database: Supabase (PostgreSQL)
• ORM: SQLAlchemy
• PDF Processing: PDFMiner.six

⚙️ Setup & Installation

Prerequisites

• Node.js (v16+)
• Python (3.9+)
• Git

1. Clone the Repository

git clone https://github.com/yourusername/smart-resume.git
cd smart-resume

2. Backend Setup

Navigate to the backend directory and set up the Python environment.

cd backend
python -m venv venv

# Activate Virtual Environment
# Windows:
venv\Scripts\activate
# Mac/Linux:
source venv/bin/activate

# Install Dependencies
pip install -r requirements.txt

Environment Variables: Create a .env file in the backend/ directory:

DATABASE_URL=postgresql://user:pass@host:5432/db
SECRET_KEY=your_secret_key_here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
GEMINI_API_KEY=your_gemini_api_key
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key

Run the Server:

uvicorn main:app --reload

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

3. Frontend Setup

Navigate to the frontend directory.

cd ../frontend
npm install

Run the Application:

npm start

The application will open at http://localhost:3000.

🧠 How It Works (The "Brain")

1. Upload: User uploads a PDF Resume.
2. Parsing: Backend extracts text using pdfminer.
3. Feature Extraction: The system extracts 8 key signals (Keyword Overlap, Semantic Similarity, Experience Gap, etc.).
4. XGBoost Prediction: Returns a probability score (0-70 points).
5. Gemini Evaluation: The AI acts as a recruiter to score Impact and Tone (0-30 points).
6. Final Score: The weighted sum constitutes the final ATS Score.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License.