llm.py

import pathlib
import ast
import os
from pypdf import PdfReader
import docx2txt
from sqlalchemy import select
from sqlalchemy.orm import Session
import openai
import cohere
from models import Job, ENGINE
openai.api_key = os.environ["OPEN_API_KEY"]
co = cohere.Client(os.environ["COHERE_API_KEY"])
def gpt(user_query):
    response = openai.Completion.create(
                                        engine="text-davinci-003",
                                        prompt = user_query,
                                        max_tokens=1024,
                                        n=1,
                                        stop=None,
                                        temperature=0.5,
                                    )
    return response['choices'][0]['text']


def parse_pdf(file_name):
  reader = PdfReader(file_name)
  page = reader.pages[0]
  resume_text = page.extract_text()

  

  return resume_text


def parse_docx(file_name):
    file_text = docx2txt.process(file_name)
    return file_text

# def get_dict(resume_text):
#     resume_dict = ast.literal_eval(gpt(f"""parse the resume and convert it into a Python string with the headings as "experience," "skills," "certifications," and "education".

#                         resume: "{resume_text}"
#                         resume_dict: """).strip())
#     return resume_dict

def parse(filename):
    resume_file = pathlib.Path(filename)
    text = parse_pdf(resume_file) if resume_file.suffix == ".pdf" else parse_docx(resume_file)
    print("parse"+"~"*10,text)
    # dct = get_dict(text)
    # print(dct)
    return text

def rerank(job_id,docs,top_n):
    with Session(ENGINE) as session:
        stmt = select(Job).where(Job.job_id == job_id)
        job = session.scalars(stmt).one()
    
    post = job.post_name
    
    response = co.rerank(
    model = 'rerank-english-v2.0',
    query = f'Which profile suits most for the role of {post}?',
    documents = docs,
    top_n = top_n,
    )
    print(response)
    return response