ResumeGPT is a Python package designed to extract structured information from a PDF Curriculum Vitae (CVs)/Resumes documents. It leverages OCR technology and utilizes the capabilities of ChatGPT AI language model (GPT-3.5 and GPT-4) to extract pieces of information from the CV content and organize them in a structured Excel-friendly format.
- Extracts text from PDF CVs: Uses OCR technology to extract the CV's PDF content as text.
- Extracts information using GPT: Sends the extracted text to GPT for information extraction according to a predefined prompt.
- Structures information to Excel file: Processes the extracted information from GPT and structures it from JSON into a Excel-friendly format.
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OCR Reader (CVsReader module): This process reads CVs from a specified directory and extracts the text from PDF files.
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Engineered Prompt and ChatGPT Pipeline (CVsInfoExtractor module): This process takes as an input the extracted text generated by the OCR Reader and extracts specific information using ChatGPT in a JSON format.
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Extracted Information Structuring (CVsInfoExtractor module): This process takes the JSON output from the ChatGPT Pipeline, which contains the information extracted from each CV. This information is then structured and organized into a clear and easy-to-understand Excel format.
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Python: Python 3.8 or newer.
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GPT-4 API Access: If GPT-3.5 tokens don not fit the CV content, the package uses GPT-4 to extract the information from the CVs, so you'll need an access to the GPT-4 API.
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Prepare Your CVs: Make sure all the CVs you want to analyze are in the “CVs” directory.
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Run the Script: Run the following scripts. This will clone the project, prepare the environment, and execute the code.
- Clone the project
git clone https://github.com/Aillian/ResumeGPT.git- CD project directory
cd ResumeGPT - Create a virtual environment
python -m venv resumegpt_venv- Activate the virtual environment
source resumegpt_venv/Scripts/activate- Upgrade pip version
pip install --upgrade pip- Install requirements.txt
pip install -r requirements.txt- CD codes directory
cd ResumeGPT - Run main.py and provide the 3 required arguments:
- CVs Directory Path: use "../CVs" to read from 'CVs' directory
- Openai API Key: should include GPT-4 model access
- Desired Positions: written like the following "Data Scientist,Data Analyst,Data Engineer"
python main.py "../CVs" "sk-ldbuDCjkgJHiFnbLVCJvvcfKNBDFJTYCVfvRedevDdf" "Data Scientist, Data Analyst, Data Engineer"- Examine the Results: After the script finishes, you will find the output in “Output” directory which are two file (CSV & Excel) of the extracted information from each CV.
ResumeGPT is designed to extract 23 features from each CV:
- Education:
- Education Bachelor University: name of university where bachelor degree was taken
- Education Bachelor GPA: GPA of bachelor degree (Example: 4.5/5)
- Education Bachelor Major: major of bachelor degree
- Education Bachelor Graduation Date: date of graduation from bachelor degree (in format: Month_Name, YYYY)
- Education Masters University: name of university where masters degree was taken
- Education Masters GPA: GPA of masters degree (Example: 4.5/5)
- Education Masters Major: major of masters degree
- Education Masters Graduation Date: date of graduation from masters degree (in format: Month_Name, YYYY)
- Education PhD University: name of university where PhD degree was taken
- Education PhD GPA: GPA of PhD degree (Example: 4.5/5)
- Education PhD Major: major of PhD degree
- Education PhD Graduation Date: date of graduation from PhD degree (in format: Month_Name, YYYY)
- Work Experience:
- Years of Experience: total years of experience in all jobs (Example: 3)
- Experience Companies: list of all companies that the candidate worked with (Example: [Company1, Company2])
- Top 5 Responsibilities/Projects Titles: list of top 5 responsibilities/projects titles that the candidate worked on (Example: [Project1, Project2, Project3, Project4, Project5])
- Courses/Certifications:
- Top 5 Courses/Certifications Titles: list of top 5 courses/certifications titles that the candidate took (Example: [Course1, Course2, Course3, Course4, Course5])
- Skills:
- Top 3 Technical Skills: list of top 3 technical skills (Example: [Skill1, Skill2, Skill3])
- Top 3 Soft Skills: list of top 3 soft skills (Example: [Skill1, Skill2, Skill3])
- Employment Status:
- Current Employment Status: one of the following (Full-time, Part-Time, Intern, Freelancer, Consultant, Unemployed)
- Personal Information:
- Nationality: nationality of the candidate
- Current Residence: where the candidate currently live
- Suitable Position:
- Suitable Position: the most suitable position for the candidate, this will be taken from the user and dynamically replaced in the prompt
- Rating Score:
- Candidate Rating (Out of 10): score of the candidate suitability for the classified position in point 19 (Example: 7.5)
This information is then organized into a structured Excel file.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Possible additional features and optimizations:
- Add additional features to the prompt.
- Handling exceeded tokens limit, by further cleansing cv content.
- The code tries to call gpt-3.5-turbo model first, if token limit exceeds the acceptable limit, it calls gpt-4. But this has some problems: 1- it is costly 2- what if the provided API key does not have access to gpt-4 model?
- Catching GPT-4 "service is down" error by calling the API again after some sleeping time.
- Can the prompt be reduced so we save some tokens for the cv content?
- Separating "Information To Extract" in the prompt to a different file so the user gets the flexibility of adding new features and then dynamically imputing it into the prompt after that the added features in "CVs_Info_Extracted.csv" should be reflected as column names in the csv file.
- Additional errors handling.
- What about extending the usage to other LLMs?
ResumeGPT is released under the MIT License. See the LICENSE file for more details.
