🎯 Job Market Analyzer & Career Advisor

An AI-powered application that analyzes real-time job market trends and provides personalized career guidance based on a user's resume.

🔍 Turn job data into insights 🎯 Turn your resume into a roadmap

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📌 Overview

This project fetches real-time job listings and uses AI to:

• Analyze market demand
• Extract in-demand skills
• Compare with user resume
• Generate a personalized career roadmap

👨‍🎓 Designed for students and early professionals who want clarity in their career path.

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🎯 Objective

This project helps students understand:

• Multi-agent AI systems
• LLM-based reasoning
• API integration
• Resume analysis
• Building real-world AI applications

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🚀 Features

• 🔍 Real-time job search (API-based)
• 📊 Market insights (skills, trends, salary)
• 📄 Resume analysis (PDF/TXT)
• 🎯 Career guidance (skill gap + roadmap)
• 💼 Job listings with descriptions
• 🧠 Multi-agent workflow (Market + Career Advisor)

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🧠 Architecture / Workflow

🔄 System Flow

1. User inputs:

   • Job role
   • Location
   • Resume

2. System:

   • Fetches job data from API
   • Extracts job descriptions

3. Agent 1: Market Analyzer

   • Identifies key skills
   • Detects market trends

4. Agent 2: Career Advisor

   • Compares resume with market
   • Finds skill gaps
   • Generates roadmap

5. Output:

   • Market insights
   • Job listings
   • Career advice

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🛠 Tech Stack

• Language: Python
• UI: Streamlit
• LLM: Groq (LLaMA 3)
• API: RapidAPI (JSearch)
• **LANGCHAIN : Connecting everything

📦 Libraries Used

• streamlit
• requests
• python-dotenv
• groq
• PyPDF2

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📁 Project Structure

job-market-analyzer/
│
├── app.py
├── requirements.txt
├── .env              # (Not uploaded to GitHub)
├── README.md

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⚙️ Setup Instructions

🔧 Prerequisites

• Python 3.8+

• Git installed

• API Keys:

  • RapidAPI Key
  • Groq API Key

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📦 Installation (Step-by-Step)

1️⃣ Clone the Repository

git clone https://github.com/your-username/job-market-analyzer.git
cd job-market-analyzer

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2️⃣ Create Virtual Environment (Recommended)

python -m venv venv
venv\Scripts\activate      # Windows
source venv/bin/activate   # Mac/Linux

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3️⃣ Install Dependencies

pip install -r requirements.txt
pip install httpx==0.27.0
pip install streamlit groq python-dotenv requests PyPDF2

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4️⃣ Setup Environment Variables

Create a .env file:

RAPIDAPI_KEY=your_rapidapi_key
GROQ_API_KEY=your_groq_api_key

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▶️ Running the Project

streamlit run app.py

Then open: 👉 http://localhost:8501

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💻 How It Works (Code-Level)

🔹 fetch_jobs()

• Calls job API
• Retrieves job listings

🔹 analyze_market()

• Sends job data to LLM
• Returns skills, trends, salary insights

🔹 career_advice()

• Compares resume with job data

• Outputs:

  • Skill gaps
  • Learning roadmap
  • Improvements

🔹 extract_resume_text()

• Reads PDF/TXT resume
• Converts into text

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🧪 Example Usage

Input:

• Role: AI Engineer
• Location: Chennai
• Resume: Uploaded file

Output:

• 📊 Top Skills: Python, ML, NLP

• 📈 Trends: Growing AI demand

• 🎯 Missing Skills: MLOps, Deployment

• 🛣 Roadmap:

  • Learn ML fundamentals
  • Build projects
  • Deploy models

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🔧 Customization / Extensions

Students can enhance this project by:

• Adding skill match percentage
• Resume scoring system
• Downloadable reports (PDF)
• Chatbot interface
• Dashboard visualization

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🚀 Future Improvements

• Add database integration
• Deploy online (Streamlit Cloud)
• Improve resume parsing
• Add analytics dashboard
• Multi-role comparison

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👨‍💻 Contributors

• Sreshta Sridhar

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💡 Workshop Instructions

To run this project:

pip install -r requirements.txt
streamlit run app.py

⚠️ Important:

• Create .env file
• Add API keys
• Use virtual environment (recommended)

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🔥 Final Note

This project demonstrates a real-world AI system combining:

• APIs
• LLMs
• Multi-agent architecture

A perfect starting point for AI Engineering & Career Intelligence Tools 🚀