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LLM Powered Resume Parser Tool

This tool is designed to simplify the process of extracting key information from resumes in PDF and Word formats. It provides both a command line interface (CLI) script and a Streamlit web application for user-friendly interaction.

It relies on the OpenAI API, and an API key is required for proper functionality. Make sure to secure your API key and follow OpenAI's usage guidelines.

Getting Started

  1. Clone the Repository:

    git clone https://github.com/Sajjad-Amjad/Resume-Parser.git
cd Resume-Parser

  2. Install Dependencies:

    pip install -r requirements.txt

  3. Set up Environment Variables:

    • Create a .env file in the root directory.
    • Add your OpenAI API key to the .env file: 
      OPENAI_API_KEY=your_api_key_here

Usage

CLI Script

Run the following command in the terminal:

python3 parser.py path/to/resume.pdf

Streamlit Web Application

Run the following command in the terminal:

streamlit run app.py

Access the application in your web browser at http://localhost:8501 to upload your resume.

Output

The tool extracts and parses any resume in the following output class model:

## Ouput Pydantic models:
class Output(BaseModel):
    'candidate_name': str,
    'job_title': str,
    'bio': str,
    'contact_info': ContactInfo,
    'work_output': List[WorkEperience],
    'skills': List[str],
    'education': List[Education],
    'professional_development': List[str],  # list of certifications, research publications, awards, open source contributions
    'other_info': List[str],  # list of language skills, interests, hobbies, extracurricular activities

class ContactInfo(BaseModel):
    'location': str,
    'phone_number': str,
    'email_address': str,
    'personal_urls': List[str]

class WorkEperience(BaseModel):
    'company_name': str,
    'job_title': str,
    'start_date': str,
    'end_date': str,
    'description': str

class WorkEperience(BaseModel):
    'qualification': str,
    'establishment': str,
    'country': str,
    'year': str,