We will build a Generative AI application using a Large Language Model (LLM) to parse resumes. Leveraging our knowledge of LLMs and the technique of prompt engineering, we will develop a system that can extract and structure information from resumes, score them based on job descriptions, mask personal information, and generate interview questions.
Objective: Create an application that parses uploaded resumes, extracts and structures the content, and provides additional functionalities to enhance the evaluation process.
- Resume Parsing Task: The application should parse the content of an uploaded resume and output it in a well-formatted structured JSON format. Output: Key-value pairs representing different sections of the resume (e.g., personal information, education, work experience, skills).
- Resume Scoring Task: The LLM should score the resume based on a provided job description. Output: A score or rating indicating how well the resume matches the job description.
- Personal Information Masking Task: The application should identify and mask personal information such as name, email, phone number, and address in the resume. Output: The structured JSON format should have masked values for personal information fields.
- Question Generation Task: Generate a set of interview questions based on the resume content. Output: A list of questions tailored to the candidate's experience and skills.
- Python 3.7 or above
- Get an openai API key
git clone https://github.com/GPT-Wizard/Resume-Parser.git
If running for the first time,
- Create virtual environment
pip3 install virtualenv
python3 -m venv env
source env/bin/activate
- Install required libraries
pip3 install -r requirements.txt
Set your API keys in the .env file by copying .env.example
streamlit run app.py