README.md

metadata

title: Job Catalyst AI
emoji: 💼
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.39.0
app_file: app.py
pinned: false

Job Catalyst AI

AI-powered Job Recommendation System built with Streamlit 🚀

1) AI-Powered Job Recommendation System

This is an AI-driven job recommendation system that analyzes resumes and suggests the most relevant job positions based on skills and experience. The system utilizes Natural Language Processing (NLP) and Semantic Search to find the best job matches.

🚀 Features

• Upload a PDF resume for job recommendations
• AI-based analysis using SentenceTransformer for job matching
• Fast and efficient search powered by FAISS
• TF-IDF filtering to narrow down relevant jobs
• Streamlit-powered Web Interface for easy use

🛠️ Technologies Used

• Python
• Streamlit (for the UI)
• FAISS (for efficient job matching)
• Sentence Transformers (for embedding resumes and job descriptions)
• PyMuPDF (to extract text from PDF resumes)
• scikit-learn (for TF-IDF filtering)

📌 How to Run

1. Clone this repository: ```sh git clone https://github.com/YomnaWaleed/job-recommendation-system-ai.git cd job-recommendation-system-ai

2) Install the dependencies:

pip install -r requirements.txt

3) Run the Streamlit app:

streamlit run streamlit.py

4) Open the URL shown in the terminal

(e.g., http://localhost:8501)

📄 Dataset

The system uses a CSV file (JobsFE.csv) containing job descriptions, skills, and work details. Ensure this file is present in the project folder.

🤖 How It Works

Resume Processing: Extracts text from the uploaded PDF resume. TF-IDF Filtering: Reduces the number of job descriptions for faster and better recommendations. Semantic Search: Uses SentenceTransformer embeddings with FAISS for similarity search. Top Job Matches: The best job recommendations are displayed on the Streamlit app.

📌 Future Improvements

✅ Support for multiple resume formats (DOCX, TXT) ✅ Advanced customized filtering based on user preferences ✅ Integration with LinkedIn job postings