📄 Resume Analyzer & Job Description Matcher

🚀 AI-Powered Resume Parser & Job Description Matcher

Upload a Resume → Extract Structured Information → Compare with Job Description → Get ATS Match Score & Recruiter Insights

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📌 Overview

Resume Analyzer is an AI-powered web application that automatically extracts structured information from a resume PDF and compares it against a Job Description (JD).

Instead of manually reading resumes, this application leverages Large Language Models (LLMs) through Groq + LangChain to identify:

• Candidate Information
• Education
• Experience
• Skills
• ATS Match Score
• Missing Skills
• Recruiter Summary
• Strengths & Gaps

The project demonstrates practical usage of Generative AI, Structured Output, Prompt Engineering, and Streamlit.

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✨ Features

📄 Resume Parsing

• Upload Resume PDF
• Extract Name
• Extract Email
• Extract Phone Number
• Extract Education
• Extract Experience
• Extract Technical Skills

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🤖 AI Structured Extraction

Uses:

• LangChain
• Groq Llama Model
• Function Calling
• Pydantic Models

The AI returns structured JSON instead of plain text.

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📊 Job Description Matching

Compare Resume with JD and generate:

• ATS Match Score
• Matched Skills
• Missing Skills
• Strengths
• Gaps
• Recruiter Summary

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📥 Export Results

Download

• Parsed Resume JSON
• JD Match Result JSON

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🧠 Project Workflow

                Resume PDF
                     │
                     ▼
             PyPDFLoader
                     │
                     ▼
            Extract Resume Text
                     │
                     ▼
          LangChain PromptTemplate
                     │
                     ▼
          Groq Llama 3.3 LLM
                     │
                     ▼
     Structured Resume (Pydantic)
                     │
          Resume + Job Description
                     │
                     ▼
          AI Resume Matcher
                     │
                     ▼
      JDMatchResult (Structured)
                     │
                     ▼
             Streamlit Dashboard

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🛠 Tech Stack

Programming Language

• Python

AI & LLM

• LangChain
• Groq LLM
• Prompt Engineering

PDF Processing

• PyPDF
• PyPDFLoader

Data Validation

• Pydantic

Web Framework

• Streamlit

Environment Management

• Python Dotenv

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📂 Project Structure

Resume-Analyzer/
│
├── .venv/
├── .env
├── requirements.txt
│
├── models.py
├── prompts.py
├── parser.py
├── matcher.py
├── main.py
│
├── resume.pdf
├── jd.txt
│
└── README.md

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⚙ Installation

Clone Repository

git clone https://github.com/Ronit049/Resume-Analyzer.git

cd Resume-Analyzer

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Create Virtual Environment

Windows

python -m venv .venv

Activate

.venv\Scripts\activate

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Linux / macOS

python3 -m venv .venv

source .venv/bin/activate

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Install Dependencies

pip install -r requirements.txt

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🔑 Environment Variables

Create a file named

.env

Add your Groq API Key

GROQ_API_KEY=your_groq_api_key

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▶ Running the Project

Run Streamlit

streamlit run main.py

or

python -m streamlit run main.py

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📖 How It Works

Step 1

Upload Resume PDF

↓

Step 2

PDF is converted into plain text

↓

Step 3

Prompt is generated using LangChain

↓

Step 4

Groq LLM extracts structured information

↓

Step 5

Resume object is created using Pydantic

↓

Step 6

User pastes Job Description

↓

Step 7

LLM compares Resume & JD

↓

Step 8

Generate

• Match Score
• Missing Skills
• Recruiter Summary

↓

Step 9

Display results in Streamlit

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📊 Example Output

Parsed Resume

{
  "name": "John Doe",
  "email": "john@gmail.com",
  "phone_number": "9876543210",
  "skills": [
    "Python",
    "SQL",
    "LangChain"
  ]
}

---

JD Match Result

{
  "match_score": 87,
  "matched_skills": [
    "Python",
    "SQL"
  ],
  "missing_skills": [
    "Docker",
    "FastAPI"
  ],
  "summary": "Candidate is a strong fit for the role."
}

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📸 Application Preview

📄 Upload Resume

⬇

🤖 Parse Resume

⬇

📋 View Structured Resume

⬇

📝 Paste Job Description

⬇

📊 Generate Match Score

⬇

📥 Download JSON Report

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📸 Application Preview

🌐 Frontend Interface

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💻 Code Overview

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🎯 Learning Outcomes

This project demonstrates:

• Prompt Engineering
• Structured Output
• Function Calling
• LangChain Chains
• Groq Integration
• Resume Parsing
• ATS Resume Matching
• Streamlit Development
• Pydantic Models
• Environment Variables
• PDF Processing
• AI Application Development

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🚀 Future Improvements

• Support DOCX resumes
• OCR for scanned PDFs
• Multi-resume ranking
• Resume improvement suggestions
• Skill gap visualization
• ATS compatibility analysis
• Company-specific scoring
• Cover Letter Generator
• Interview Question Generator
• LinkedIn Profile Analysis

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push your branch
5. Open a Pull Request

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⭐ Support the Project

If you found this project useful, informative, or inspiring, please consider giving it a ⭐ on GitHub.

Your support helps improve the project, motivates future development, and makes it easier for others to discover it.

👨‍💻 Author

Ronit Raj

Passionate Computer Science Student focused on building practical AI-powered applications, modern web experiences, and open-source projects.

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🌟 Show Your Support

If this repository helped you:

⭐ Star the repository

🍴 Fork it

🐛 Report bugs

💡 Suggest new features

🤝 Contribute to make it even better

Every contribution—big or small—is greatly appreciated.

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💙 Thank You

Thank you for visiting this repository!

I truly appreciate your time and interest in this project. I hope it helps you learn something new or solve a real problem.

If you have ideas, feedback, or would like to collaborate on future projects, feel free to reach out. Contributions, discussions, and suggestions are always welcome.

Happy Coding! 🚀