Tailor your resume to match any job posting effortlessly with ResumeGPT.

ResumeGPT allows you to simply provide your resume and a job posting link, and it will produce a formatted ATS friendly PDF resume that is optimized and personalize your resume to align with the specific requirements and keywords of the job.

Features

• Extracts relevant skills, qualifications, and keywords from a job posting.
• Tailors your curent resume to match job requirements.
• Generates professional ATS friendly PDF resumes.
• Allows for user verification and customization before finalizing the resume.

Installation

pip install ResumeGPT

or:

pip install git+https://github.com/takline/ResumeGPT.git

or:

git clone https://github.com/takline/ResumeGPT.git
cd ResumeGPT
pip install -r requirements.txt

Usage

• Add your resume to ResumeGPT/data/sample_resume.yaml (make sure ResumeGPT.config.YOUR_RESUME_NAME is set to your resume filename in the .data/ folder)
• Provide ResumeGPT with the link to a job posting and it will tailot your resume to the job:

Single job posting usage

url = "https://[link to your job posting]"
resume_improver = ResumeGPT.services.ResumeImprover(url)
resume_improver.create_draft_tailored_resume()

ResumeGPT then creates a new resume YAML file in a new folder named after the job posting (ResumeGPT/data/[Company_Name_Job_Title]/resume.yaml) with a YAML key/value: editing: true. ResumeGPT will wait for you to update this key to verify the resume updates and allow them to make their own updates until users set editing=false. Then ResumeGPT will create a PDF version of their resume.

Custom resume location usage

Initialize ResumeImprover via a .yaml filepath.:

resume_improver = ResumeGPT.services.ResumeImprover(url=url, resume_location="custom/path/to/resume.yaml")
resume_improver.create_draft_tailored_resume()

Post-initialization usage

resume_improver.update_resume("./new_resume.yaml")
resume_improver.url = "https://[new link to your job posting]"
resume_improver.download_and_parse_job_post()
resume_improver.create_draft_tailored_resume()

Background usage

You can run multiple ResumeGPT.services.ResumeImprover's concurrently via ResumeGPT's BackgroundRunner class (as it takes a couple of minutes for ResumeImprover to complete a single run):

background_configs = [
    {
        "url": "https://[link to your job posting 1]",
        "auto_open": True,
        "manual_review": True,
        "resume_location": "/path/to/resume1.yaml",
    },
    {
        "url": "https://[link to your job posting 2]",
        "auto_open": False,
        "manual_review": False,
        "resume_location": "/path/to/resume2.yaml",
    },
    {
        "url": "https://[link to your job posting 3]",
        "auto_open": True,
        "manual_review": True,
        "resume_location": "/path/to/resume3.yaml",
    },
]
background_runner = ResumeGPT.services.ResumeImprover.create_draft_tailored_resumes_in_background(background_configs=background_configs)
#Check the status of background tasks (saves the output to `ResumeGPT/data/background_tasks/tasks.log`)
background_runner["background_runner"].check_status()
#Stop all running tasks
background_runner["background_runner"].stop_all_tasks()
#Extract a ResumeImprover
first_resume_improver = background_runner["ResumeImprovers"][0]

You will follow the same workflow when using ResumeGPT's BackgroundRunner (ex: verify the resume updates via editing=false in each ResumeGPT/data/[Company_Name_Job_Title]/resume.yaml file). You can also find logs for the BackgroundRunner in ResumeGPT/data/background_tasks/tasks.log.

Once all of the background tasks are complete:

background_runner["background_runner"].check_status()

Output:

['Task completed.',
 'Task completed.',
 'Task completed.',
 'Task completed.',
 'Task completed.',
 'Task completed.',
 'Task completed.',
 'Task completed.',
 'Task completed.']

Create the pdf for each ResumeImprovers instance:

for improver in background_runner["ResumeImprovers"]:
    pdf_generator = ResumeGPT.pdf_generation.ResumePDFGenerator()
    resume_yaml_path = os.path.join(improver.job_data_location, "resume.yaml")
    pdf_generator.generate_resume(improver.job_data_location, ResumeGPT.utils.read_yaml(filename=resume_yaml_path))

ResumeGPT PDF Output

Example ATS friendly resume created by ResumeGPT:

pdf_generator = ResumeGPT.pdf_generation.ResumePDFGenerator()
pdf_generator.generate_resume("/path/to/save/pdf/", ResumeGPT.utils.read_yaml(filename="/path/to/resume/resume.yaml"))

Discussions

Feel free to give feedback, ask questions, report a bug, or suggest improvements:

• Discussions
• Issues

Contributors

⭐️ Please star, fork, explore, and contribute to ResumeGPT. There's a lot of work room for improvement so any contributions are appreciated.