📊 Smart Job Description Analyzer

The Smart Job Description Analyzer is a powerful LLM-based application that helps users intelligently evaluate how well their resumes match a given job description. It provides actionable insights, visual feedback, and auto-generated suggestions to help improve resume alignment and job fit.

This project showcases advanced applications of LangChain, OpenAI GPT-4, FAISS, and Streamlit, along with semantic skill categorization, fuzzy matching, and PDF report generation for a complete end-to-end experience.

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🚀 Features

• 🔍 Job Description Analysis – Extracts relevant skills, qualifications, and expectations using LLMs.
• 📄 Resume Parsing – Processes resume PDFs and retrieves core skills using chunked embedding.
• 🧠 Skill Gap Detection – Uses fuzzy matching to detect unmatched skills between JD and resume.
• 🧠 AI Suggestions – Provides resume improvement tips powered by Retrieval-Augmented Generation (RAG).
• 📌 Resume Match Score – Computes a job fit score out of 10 with personalized feedback.
• 🗂️ Auto-Categorized Skills – Dynamically categorizes resume skills into general domains (e.g., Programming, Marketing, Cloud).
• 📊 Visual Insights – Includes interactive bar charts and radar plots for skill match analysis.
• 📄 PDF Report Export – Download a cleanly formatted resume-JD analysis report.
• ✅ Fuzzy Matching – Uses fuzzywuzzy for partial match detection, enabling more accurate comparisons.

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🧠 Technologies Used

Layer	Stack
LLMs	OpenAI GPT-4 via LangChain
Embeddings	OpenAI text-embedding-ada-002
Vector Store	FAISS
Frontend	Streamlit
Visualization	Plotly
Parsing & Utils	PDFPlumber, ReportLab, Regex
NLP & Matching	FuzzyWuzzy

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🧱 Architecture

1. User inputs a job description (as text).
2. Uploads a resume (PDF).
3. JD and Resume are parsed and embedded.
4. LangChain is used to extract and categorize skills.
5. FAISS enables similarity comparison.
6. Match score, gaps, and LLM-generated suggestions are calculated.
7. Charts and download options are rendered via Streamlit.

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🧪 How to Run Locally

1. Clone the repo

git clone https://github.com/your-username/smart-jd-analyzer.git
cd smart-jd-analyzer

2. Install dependencies

pip install -r requirements.txt

3. Set up environment variables

Create a .env file with:

OPENAI_API_KEY=your_openai_api_key

4. Run the app

streamlit run app.py

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🧾 Sample Use Cases

• ✅ Job seekers tailoring resumes for better ATS and recruiter screening
• ✅ Recruiters checking candidate relevance to job postings
• ✅ Career counselors assisting clients with resume optimization
• ✅ HR tech innovators building smarter hiring tools

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📄 PDF Report Includes:

1. Match Score and Feedback
2. Missing Skills & Improvement Suggestions
3. Categorized Skills by Domain
4. Clean, wrapped, emoji-free formatting

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💡 Future Improvements

• Add chatbot assistant for interactive guidance
• Support for multiple resume comparison
• Integration with ATS or job boards
• Historical tracking of resume improvements

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🤝 Contributing

Pull requests and suggestions are welcome! For major changes, please open an issue first to discuss what you would like to change.