📝 Smart CV

An AI-powered resume enhancement tool leveraging NLP & ML for intelligent career advancement.

📌 Table of Contents

• About the Project
• Key Features
• System Architecture
• Technologies Used
• Installation Guide
• Usage Instructions
• API Documentation
• Dataset Information
• Testing & Performance
• Future Enhancements
• Contributing
• License
• Contact

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🚀 About the Project

Smart CV is an AI-powered resume analysis and enhancement tool that utilizes Natural Language Processing (NLP) and Machine Learning (ML). It extracts key skills, qualifications, and experiences from resumes, offers personalized recommendations, and matches resumes to job descriptions with high accuracy.

🔹 For Job Seekers: Get instant feedback and AI-powered resume enhancement.
🔹 For Recruiters: Streamline resume screening with automated skill extraction.

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🔥 Key Features

✅ Resume Parsing & Analysis – Extracts and categorizes skills, experiences, and education.
✅ AI-Powered Recommendations – Contextual improvements using TF-IDF & BERT.
✅ Job Matching – Matches resumes with job descriptions based on skill relevance.
✅ Real-time Feedback – Interactive resume improvements.
✅ Secure & Scalable – Encrypted resume storage, fast processing.
✅ User-Friendly Interface – Built using React.js for a smooth experience.

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⚙️ System Architecture

🔹 Frontend:

Developed using React.js, providing a dynamic and intuitive UI for users to upload resumes, view suggestions, and download improved versions.

🔹 Backend:

• Built with Django (Python), handling resume processing, NLP models, and database interactions.
• Implements RESTful APIs for seamless communication between the frontend and backend.

🔹 Database:

Uses SQLite for storing user data, resumes, job descriptions, ensuring fast access and processing.

🔹 NLP & ML Models:

• TF-IDF and BERT for skill extraction and job matching.
• Named Entity Recognition (NER) for extracting important resume details.

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🏗 Technologies Used

Component	Technology
Frontend	React.js, HTML, CSS, JavaScript
Backend	Django (Python)
Database	SQLite
ML/NLP Models	TF-IDF, BERT, Named Entity Recognition (NER)
APIs	RESTful APIs

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🛠 Installation Guide

Prerequisites:

Ensure you have: ✔ Python 3.x
✔ Node.js & npm
✔ Virtual Environment (venv or virtualenv)

Steps to Setup:

1️⃣ Clone the Repository

git clone https://github.com/KeshavSwami21/Smart-CV.git  
cd Smart-CV  

2️⃣ Backend Setup

cd backend  
python -m venv venv  
source venv/bin/activate  # On Windows use `venv\Scripts\activate`  
pip install -r requirements.txt  
python manage.py migrate  
python manage.py runserver  

3️⃣ Frontend Setup

cd ../frontend  
npm install  
npm start  

4️⃣ Run the Application

Open http://localhost:3000/ in your browser.

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🎯 Usage Instructions

1️⃣ Upload your resume in PDF/DOCX format.
2️⃣ View AI-generated skill suggestions.
3️⃣ Modify and enhance your resume with AI-based recommendations.
4️⃣ Download the improved version and apply for jobs.

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🔌 API Documentation

Endpoint	Method	Description
/upload_resume	POST	Uploads a resume for parsing
/analyze_resume	GET	Extracts skills & experiences
/suggest_changes	GET	Provides AI-powered resume recommendations
/match_jobs	GET	Matches resumes with job descriptions

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📂 Dataset Information

The Smart CV project leverages a dataset of real-world resumes and job descriptions, allowing AI models to:
✔ Recognize common skill trends.
✔ Extract key competencies using Named Entity Recognition (NER).
✔ Identify resume gaps and suggest improvements.

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🛡 Testing & Performance

✔ Unit Testing – Verifies components (Frontend, Backend, ML Models).
✔ Integration Testing – Ensures seamless interaction between frontend and backend.
✔ Load Testing – Confirms stability under high traffic conditions.
✔ Security Testing – Evaluates data encryption & protection.

🛠 Results:

• 85% accuracy in skill extraction.
• 70% reduction in resume screening time for recruiters.
• High user satisfaction from beta testing.

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🔮 Future Enhancements

✔ Real-time Job Matching – Connect with job portals for instant applications.
✔ Multilingual Support – Resume analysis in multiple languages.
✔ Mobile App – Increase accessibility.
✔ Advanced NLP Models – Improve accuracy in skill extraction.

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🤝 Contributing

We welcome contributions! 🎉
Steps to contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-branch).
3. Commit changes (git commit -m "Added feature").
4. Push to GitHub (git push origin feature-branch).
5. Open a Pull Request.