Resume Parser Service
GPT-3 based resume parser as a REST API that transforms a resume PDF like this to a JSON like this.
Parsing a resume PDF takes around 15 seconds and costs about $0.01 for every 500 tokens using text-davinci-002 engine (that's why there is no live demo website). Note that a typical request and response may use 1500 tokens ($0.03), 3000 tokens ($0.06) or more.
Please note that more accurate results may be achieved by fine-tuning GPT-3, but the out-of-the-box results from this repo are already very impressive.
Quick Start
- Install Python 3 and pip3. For macOS, see note below.
- Install all dependencies of
pdftotext(see here). - In a new terminal, update pip3 if needed:
python3 -m pip install --upgrade pip - In another new terminal, clone the repository and move Terminal to the directory.
- Please close the other terminals and continue in this terminal.
- Check the versions:
python3 --versionandpip3 --version. - Run the
./build.shin the project root. - Get your OpenAI API Key.
- Create a file named
.envand set your API key in it:OPENAI_API_KEY=YOURKEYor set the key in an environment variable:export OPENAI_API_KEY=YOURKEY. - Run
./run.shin the project root.
A Flask server will start listening to port 5001 of localhost. Feel free to check it out with your browser.
Note for MacOS
You need to install either XCode or GCC tools (see here).
- If you install XCode, make sure to run it to complete the setup.
- Then run
xcode-select --installand complete command-line tools installation. - Finally install Homebrew, and use
brew install pythonto install Python 3.
Supported Fields
- Basic Information
- first name
- last name
- full name
- U.S. phone number
- location
- portfolio website URL
- LinkedIn URL
- GitHub main page URL
- Education
- university
- education level
- graduation year
- graduation month
- majors
- GPA
- Job Experience
- job title
- company
- location
- duration
- job content
- Project Experience
- project name
- project description