🎓 AI-Powered Job Recommender System

An advanced, containerized AI application that analyzes uploaded resumes (PDFs) to provide personalized career recommendations, identify skill gaps, and automatically fetch real-time job opportunities from LinkedIn. Built with a strong focus on AI integration and MLOps best practices.

✨ Features

• 📄 Smart Resume Parsing: Extracts text from PDF resumes accurately.
• 🧠 AI Analysis: Utilizes LLMs (Groq / Llama 3.1) to generate professional summaries, analyze industry-specific skill gaps, and suggest career roadmaps.
• 💼 Real-Time Job Scraping: Integrates with Apify to scrape and present the latest matching job opportunities directly from LinkedIn.
• 🐳 Dockerized Environment: Fully containerized using Docker and Docker Compose for seamless deployment and reproducibility.
• 📱 Responsive UI: Clean and interactive user interface built with Streamlit.

🛠️ Tech Stack & Tools

• Language: Python 3.10
• AI/LLM: Groq API (Llama-3.1-8b-instant)
• Web Scraping: Apify Client (LinkedIn Jobs Scraper)
• Frontend: Streamlit
• PDF Processing: PyMuPDF (fitz)
• DevOps/MLOps: Docker, Docker Compose

🚀 How to Run Locally

Prerequisites

1. Install Docker and Docker Compose.
2. Get your API keys from Groq and Apify.

Installation

1. Clone this repository:

   git clone [https://github.com/your-username/job-recommender-system.git](https://github.com/your-username/job-recommender-system.git)
   cd job-recommender-system