📄 Resume-JD-CV Platform

An AI-powered recruitment tool that parses resumes, analyzes job descriptions, ranks candidates, and generates tailored CVs — built with FastAPI, OpenAI, and FAISS.

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📌 Overview

Recruiters spend hours manually screening resumes against job descriptions. Resume-JD-CV Platform automates that pipeline end-to-end:

1. Upload a resume (PDF/DOCX) → AI extracts and structures the candidate's profile.
2. Upload or paste a Job Description → the system analyzes core requirements.
3. Candidates are embedded and ranked against the JD using FAISS vector search.
4. Recruiters can compare candidates side-by-side and auto-generate a tailored CV in DOCX format matched to the job.

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✨ Key Features

Feature	Description
🧠 AI Resume Parsing	Extracts text from PDF/DOCX and uses OpenAI to structure candidate profiles (skills, experience, education).
📋 JD Analysis	Parses uploaded or pasted job descriptions to identify core requirements and keywords.
🔍 Semantic Candidate Ranking	Uses embeddings + FAISS to rank candidates by relevance to a given JD.
⚖️ Candidate Comparison	Side-by-side comparison view for shortlisting.
📝 Tailored CV Generation	Auto-generates a DOCX CV customized to match a specific job description.
🗄️ Duplicate Detection	Flags duplicate or near-duplicate resumes in the pipeline.

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🏗️ Architecture

┌──────────────┐      ┌──────────────┐      ┌───────────────────┐
│   Frontend    │ ───▶ │   FastAPI    │ ───▶ │   OpenAI API       │
│ (HTML/CSS/JS) │      │   (main.py)  │      │  (parsing, CV gen) │
└──────────────┘      └──────┬───────┘      └───────────────────┘
                              │
                 ┌────────────┼─────────────┐
                 ▼            ▼             ▼
          ┌───────────┐ ┌───────────┐ ┌────────────┐
          │ services/ │ │ embeddings/│ │   db.py    │
          │ (business │ │ (FAISS +   │ │ (SQLAlchemy│
          │  logic)   │ │  vectors)  │ │  models)   │
          └───────────┘ └───────────┘ └────────────┘

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🛠️ Tech Stack

• Backend: FastAPI, Python 3.8+
• AI/ML: OpenAI API (GPT-4o), FAISS for vector similarity search
• Frontend: HTML, CSS, JavaScript (vanilla)
• Database: SQLAlchemy (SQLite by default, MySQL optional)
• Document Handling: DOCX/PDF parsing and generation
• Deployment: Docker-ready

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📂 Project Structure

.
├── main.py                  # FastAPI app entry point — routes & core API logic
├── db.py                    # Database configuration and SQLAlchemy models
├── build_index.py           # Builds the FAISS vector index from resume embeddings
├── requirements.txt         # Python dependencies
├── Dockerfile                # Container build configuration
├── frontend/                 # Static frontend (index.html, styles.css, app.js)
├── services/                  # Business logic: AI integration, doc processing, vector search
├── embeddings/                 # Embedding generation & FAISS index storage
├── test_cv_generation.py      # CV generation tests
├── test_embedding.py          # Embedding pipeline tests
├── test_matcher.py            # Candidate-JD matching tests
├── test_parser.py             # Resume parsing tests
├── test_search.py             # Vector search tests
└── profiling_log.txt          # Performance profiling notes

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✅ Prerequisites

• Python 3.8+
• An OpenAI API key for AI parsing and CV generation
• (Optional) MySQL or another relational database — falls back to SQLite by default

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🚀 Setup Instructions

1. Clone the repository

git clone https://github.com/RAYhaaNJR10/RESUME--JD--CV.git
cd RESUME--JD--CV

2. Set up a virtual environment

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4o

5. Build the vector index (optional, if using pre-existing resume data)

python build_index.py

6. Run the application

uvicorn main:app --reload

7. Access the app

• 🖥️ Frontend Dashboard: http://127.0.0.1:8000/dashboard
• 📑 API Docs (Swagger UI): http://127.0.0.1:8000/docs

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🐳 Run with Docker

docker build -t resume-jd-cv .
docker run -p 8000:8000 --env-file .env resume-jd-cv

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🧪 Testing

python test_cv_generation.py
python test_embedding.py
python test_matcher.py
python test_parser.py
python test_search.py

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🗺️ Roadmap

• Add authentication for multi-recruiter access
• Support batch resume uploads
• Export candidate rankings as CSV/PDF reports
• Add support for additional file formats (RTF, plain text)

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🤝 Contributing

Contributions, issues, and feature requests are welcome. Feel free to open a PR or issue.

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📄 License

This project is licensed under the MIT License.

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👤 Author

Rayhaan — AI/ML student & Data Science Intern, building tools at the intersection of AI and recruitment tech.