Project Name: ResumeAI
Description:
This project is a web application built using Streamlit that leverages the power of Hugging Face's Mistral 7B model and ChromaDB to analyze and rank resumes. The application provides an intuitive interface for users to upload and manage resumes, and it uses AI-powered analysis to identify skills, qualifications, and other relevant information.
Features:
- Resume Upload and Management: Users can easily upload and manage their resume collection through the Streamlit interface.
- AI-Powered Resume Analysis: The application utilizes the Mistral 7B model from Hugging Face to extract key information from resumes, such as skills, experience, education, and other relevant data points.
- Skill Matching and Ranking: The application leverages ChromaDB to perform skill matching and ranking based on user-defined criteria. This helps to identify the most suitable candidates for a specific role or position.
- Visualization and Insights: The application provides visualizations and insights into the analyzed resumes, allowing users to easily compare and contrast candidates.
Technologies Used:
- Streamlit: A Python library for building user-friendly web applications.
- Hugging Face: A platform for open-source machine learning models, including the Mistral 7B model for resume analysis.
- Mistral 7B: A large language model from Hugging Face specifically designed for text summarization and analysis.
- ChromaDB: A vector database optimized for similarity search and retrieval.
Installation:
- Clone this repository:
git clone https://github.com/MMaduranga/ResumeAI.git
- Install the required dependencies:
pip install -r requirements.txt
Usage:
- Run the main application script:
streamlit run app.py
- The Streamlit web app will launch in your browser.
- Upload your resumes and use the provided features to analyze and rank candidates.