🌟 AI-Powered Talent Acquisition Platform 🚀

Capstone Project: Applied AI for Human Resources

This project aims to revolutionize the recruitment industry by integrating Large Language Models (LLMs) and Artificial Intelligence (AI) to streamline and enhance the talent acquisition process. The platform leverages cutting-edge AI technologies for automated resume parsing, candidate profile enrichment, job description matching, and personalized communication.

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📅 Project Overview

Project Duration: September 2024 - December 2024
Group name: FA24AI35
Supervisors:

• 🧑‍🏫 Nguyễn Quốc Trung ([email protected])
• 🧑‍🏫 Trần Trọng Huỳnh

Team Members:

• Trần Huy (Leader) - SE172236 ([email protected] )
• Thái Thành Nguyên - SE172325 ([email protected])
• Tô Nguyễn Ngọc Nguyên - SE172347 ([email protected])
• Nguyễn Duy Minh - SE172370 ([email protected])

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🎯 Project Objectives

• 🔄 Automated Resume Parsing: Extract and standardize key information from resumes and LinkedIn profiles to ensure comprehensive data collection.
• 📈 Candidate Profile Enrichment: Enhance profiles with insights and skills using external data sources like GitHub, Facebook, etc.
• 📑 Job Description Matching: Leverage AI agents and human-in-the-loop systems to match candidate profiles with job descriptions.
• 🎯 Private Talent Pools: Curate specialized groups of candidates tailored to specific roles or industries.

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🚀 Deploy Model to Ollama

1. Install Ollama:

   Download Ollama from link

2. Create A Model to Ollama

   Download Model.gguf and ModelFile from link

   Edit {FILE_LOCATION} to the path of file 'Model.gguf' in file 'ModelFile'

   FROM {FILE_LOCATION}
   TEMPLATE """{{ if .Messages }}
   {{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
   {{- if .System }}

   Create a new model to Ollama

   ollama create QueryExtractionLlama -f /ModelFile

⚙️ Setup Instructions

1. Clone the repository:

   git clone https://github.com/your-repo-link/ai-talent-platform.git
   cd ai-talent-platform

2. Install dependencies:

   pip install -r requirements.txt

3. Set PYTHONPATH (Windows): If you are using Windows, set the PYTHONPATH to the current directory:

    $env:PYTHONPATH="."

4. Run the application:

   streamlit run .\frontend\chat.py

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🎥 Demo

Our platform provides an intuitive and seamless experience for both recruiters and candidates. Below is a step-by-step demonstration of the platform's core functionalities:

1. Initial Screen This is the landing page where users are greeted with a clean, user-friendly interface. From here, they can navigate to upload resumes, view the candidate pool, or start analyzing profiles.
2. Analyze Process Once the user enters a query, the AI first choose an action to execute, then extracts key entities in pre-defined output structured, searches the database, ranks candidates based on compatibility, and presents the best-matched profiles with key insights.
3. Results
4. Upload Section Here is where users can upload resumes in PDF format.