🛠️ Resume Tailor

Resume Tailor is a full‑stack AI‑assisted web application that helps users optimize their resumes against a specific job description. It analyzes resume content, extracts relevant technical keywords from the job description, identifies gaps, and generates context‑aware, professional resume suggestions using an LLM.

This project focuses on practical ATS optimization, clean backend design, and real‑world frontend–backend integration.

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✨ Why Resume Tailor?

Recruiters and ATS systems often filter resumes based on keyword relevance rather than intent. Resume Tailor bridges this gap by:

• Identifying missing technical skills from a job reinforce job description
• Enhancing resume bullets without changing the candidate’s core experience
• Producing action‑oriented, concise, and ATS‑friendly resume lines

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🚀 Features

• Upload or paste Resume and Job Description

• Automatic extraction of technical keywords from JD

• Line‑by‑line resume analysis

• AI‑generated suggestions with:

  • Original line
  • Improved version
  • Reason for change

• Clean REST API architecture

• CORS‑enabled for frontend integration

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🧠 Core Logic & Methods Used

1️⃣ Keyword Extraction (Rule‑Based NLP)

• Maintains a curated list of known technical skills
• Normalizes job description text
• Matches relevant skills using string containment

Why this approach?

• Lightweight
• Fast
• Transparent (easy to debug and extend)

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2️⃣ Resume Line Analysis

Each resume bullet is:

• Compared against extracted JD skills

• Checked for missing keywords

• Either:

  • Returned unchanged (if fully aligned)
  • Or rewritten with missing skills added naturally

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3️⃣ AI‑Powered Rewrite (LLM Integration)

• Uses OpenAI Chat Completions API

• Sends a structured prompt including:

  • Original resume line
  • Missing technical skills

• Ensures output is:

  • Concise
  • Professional
  • Action‑oriented

Why AI here?

• Rule‑based rewriting fails at natural language
• LLM ensures human‑like phrasing while preserving meaning

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🧱 Tech Stack

🔹 Frontend

• React – component‑based UI
• Axios – API communication
• CSS Modules – scoped styling

🔹 Backend

• Java 17+
• Spring Boot – REST API framework
• Spring Web – controller & request handling
• Jackson – JSON serialization/deserialization
• Java HTTP Client – OpenAI API integration

🔹 AI / External Services

• OpenAI API (Chat Completions)
• Secure API key handling via environment variables

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🗂️ Project Structure (Backend)

src/main/java/com/example/resume_tailor
│
├── ResumeTailorApplication.java   # Spring Boot entry point
├── ResumeController.java          # REST API controller
├── ResumeRequest.java             # Request DTO (resume + JD)
├── Suggestion.java                # Response model
├── SuggestionService.java         # Core business logic + AI integration
│
src/main/resources
│
└── application.properties

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🔌 API Design

POST /getSuggestions

Request Body

{
  "resume": [
    "Built REST APIs using Spring Boot",
    "Worked with databases"
  ],
  "jd": "Looking for a Java developer with Spring Boot, REST APIs, and MySQL"
}

Response

[
  {
    "original": "Built REST APIs using Spring Boot",
    "suggested": "Developed RESTful APIs using Spring Boot and MySQL to support scalable backend services",
    "reason": "Improved to include: mysql"
  }
]

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▶️ How to Run Locally

🔹 Backend Setup

1. Clone the repository

git clone https://github.com/your-username/resume-tailor.git
cd resume-tailor/backend

2. Set OpenAI API Key

Linux / macOS

export OPENAI_API_KEY=your_api_key_here

Windows (PowerShell)

setx OPENAI_API_KEY "your_api_key_here"

3. Run Spring Boot

./mvnw spring-boot:run

Backend will start at:

http://localhost:8080

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🔹 Frontend Setup

cd frontend
npm install
npm start

Frontend runs at:

http://localhost:3000

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🔐 Security Considerations

• OpenAI API key stored using environment variables
• No API keys exposed to frontend
• Backend handles all AI requests securely

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🌱 Future Enhancements

• ATS score calculation
• Resume PDF parsing
• Skill weighting instead of binary matching
• User authentication
• Cloud deployment (AWS / GCP)

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📸 Screenshots

• Resume upload & job description input (React UI)

• Tailored suggestion results displayed after analysis

• Suggested keywords and skills highlighted and returned by backend

• Backend APIs

👩‍💻 Author

Bhumika Deshmukh B.Tech Student | Full‑Stack & Backend Enthusiast