app.py

import pandas as pd

import concurrent.futures
import time

import os
import tqdm

from dotenv import load_dotenv

import re

import openai

load_dotenv()

openai.api_type = os.getenv("AZURE_API_TYPE")
openai.api_base = os.getenv("AZURE_API_BASE")
openai.api_version = os.getenv("AZURE_API_VERSION")
openai.api_key = os.getenv("AZURE_API_KEY")

def get_completion(prompt, bot_role="You are an AI assistant that helps recruiters write job descriptions.", model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(engine="gpt-35-turbo",
                                            messages=[{
                                                "role": "system",
                                                "content": bot_role
                                            }, {
                                                "role": "user",
                                                "content": prompt
                                            }],
                                            temperature=0.7,
                                            max_tokens=800,
                                            top_p=0.95,
                                            frequency_penalty=0,
                                            presence_penalty=0,
                                            stop=None)
    return response.choices[0].message["content"]

prompt_cols_jd = [
    'industry', 'location', 'job_function', 'education_name',
    'education_area', 'years_of_experience', 'employment_type',
    'start_date', 'number_of_weeks', 'skills'
] 
prompt_cols_req = ['industry',   'job_function',   'years_of_experience', 'skills']

start_prompt_jd_orig = "Generate a job description for this role. Include the job description, responsibilities, and skills. Do not include a requirements section. "
start_prompt_req_orig = "Create a list of requirements for this role. Please list each requirement one by one. The section starts with Requirements: "

def generate_prompt(jd_dict, req_dict):
    start_prompt_jd_orig = "Generate a job description for this role. Include the job description, responsibilities, and skills. Do not include a requirements section. "
    start_prompt_req_orig = "Create a list of requirements for this role. Please list each requirement one by one. The section starts with Requirements: "
    start_prompt_jd = start_prompt_jd_orig
    start_prompt_req = start_prompt_req_orig
    for key, value in jd_dict.items():
        if value:
            start_prompt_jd += "\n" + key +": " + str(value)
    
    for key, value in req_dict.items():
        if value:
            start_prompt_req += "\n" + key +": " + str(value)
    return start_prompt_jd, start_prompt_req

import gradio as gr

def generate_text(industry, location, job_function, years_of_experience, employment_type, start_date, 
                number_of_weeks, skills):
    jd_dict = {'industry':industry, 'location':location,
               'job_function':job_function, 'years_of_experience': years_of_experience,
               'employment_type':employment_type, 'start_date':start_date,
               'number_of_weeks':number_of_weeks,'skills':skills}
    req_dict = {'industry':industry, 'job_function':job_function, 'years_of_experience': years_of_experience,
                'skills': skills} 
    
    start_prompt_jd, start_prompt_req = generate_prompt(jd_dict, req_dict)
    jd_result = get_completion(start_prompt_jd)
    req_result = get_completion(start_prompt_req)

    return jd_result, req_result

demo = gr.Interface(fn=generate_text, inputs=["text","text","text","text","text","text","text","text"], outputs=["text", "text"])
demo.launch( share = True)