resumeRewritingService.py

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage
from utils import readPrompts
import os
import base64
import pdfplumber
from io import BytesIO


class ResumeEditService:
    def __init__(self):
        prompts = readPrompts(path=os.path.join(os.getcwd(), "prompts.yaml"))
        self.prompt_template = prompts.get("prompt")
        self.llm = self._initialize_llm()
    
    def _initialize_llm(self):
        """Initialize the Google Generative AI model"""
        return ChatGoogleGenerativeAI(
            model="gemini-3-flash-preview",
            temperature=0.3,
            google_api_key=os.getenv("GOOGLE_API_KEY")
        )
    
    def _extract_text_from_pdf_base64(self, base64_pdf: str) -> str:
        """
        Extract text from base64 encoded PDF
        
        Args:
            base64_pdf: Base64 encoded PDF string
        
        Returns:
            Extracted text from the PDF
        """
        # Decode base64 to bytes
        pdf_bytes = base64.b64decode(base64_pdf)
        
        # Create a BytesIO object
        pdf_file = BytesIO(pdf_bytes)
        
        # Extract text using PdfPlumber
        text = ""

        with pdfplumber.open(pdf_file) as pdf:
            for page in pdf.pages:
                text += page.extract_text() or ""
        
        return text.strip()
    
    def generate_optimized_resume(self, resume_base64: str, job_description: str, current_date: str) -> str:
        """
        Generate optimized resume based on job description
        
        Args:
            resume_base64: Base64 encoded PDF resume
            job_description: The target job description
            current_date: Current date for timeline adjustments
        
        Returns:
            Raw response from the LLM (LaTeX code in markdown format)
        """
        # Extract text from PDF
        current_resume = self._extract_text_from_pdf_base64(resume_base64)
        
        # Manual string replacement (avoiding template parsing issues)
        prompt = self.prompt_template.replace("{{CURRENT_DATE}}", current_date)
        prompt = prompt.replace("{{JOB_DESCRIPTION}}", job_description)
        prompt = prompt.replace("{{CURRENT_RESUME}}", current_resume)
        
        # Invoke the LLM directly
        response = self.llm.invoke([HumanMessage(content=prompt)])
        
        return response.content[0]["text"]