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import gradio as gr
import openai
# Set your OpenAI API key
openai.api_key = os.environ['OPENAI_API_KEY']
jd_summary_global = "" # Global variable to store the job description summary
def process_jd(text):
global jd_summary_global # Declare the global variable
if not text.strip(): # Check if the text is empty or contains only whitespace
jd_summary_global = "No JD" # Update the global variable
return "No JD"
try:
# Structuring a prompt to ask GPT-3.5 to summarize the job description
prompt = f"Summarize the following job description into its job nature, responsibilities, and requirements:\n\n{text}"
# Uploading text to OpenAI
response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
jd_summary = response['choices'][0]['message']['content'].strip()
jd_summary_global = jd_summary # Update the global variable
return jd_summary
except Exception as e:
return str(e)
def cv_rating(cv_data):
global jd_summary_global # Declare the global variable
if len(jd_summary_global) <= 1 or jd_summary_global == "No JD":
return "No JD in the previous tab."
if len(cv_data) <= 1:
return "No CV data"
try:
# Construct a prompt to ask GPT-3.5 to rate the CV based on the job description summary
prompt = f"""
Job Description Summary: {jd_summary_global}
CV Data: {cv_data}
Rate the compatibility of the CV with the job description and provide strengths, weaknesses, and recommendations to strengthen the CV.
"""
# Uploading text to OpenAI
response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
return response['choices'][0]['message']['content'].strip()
except Exception as e:
return str(e)
jd_sum = gr.Interface(
fn=process_jd, # function to process the text
inputs=gr.Textbox(lines=30, label="Job Description"),
outputs=gr.Textbox(lines=30, label="JD Summary", show_copy_button=True),
live=False,
title="Job Description Summarizer",
description="An app to summarize job descriptions into job nature, responsibilities, and requirements.",
)
cv_rate_interface = gr.Interface(
fn=cv_rating,
inputs=gr.Textbox(lines=30, label="CV Data", placeholder="Paste the CV data here"),
outputs=gr.Textbox(lines=30, label="ATS Rating System", show_copy_button=True),
live=False,
title="CV Rating",
description="An app to rate CV compatibility with job description, providing strengths, weaknesses, and recommendations.",
)
bespokecv = gr.TabbedInterface([jd_sum, cv_rate_interface],tab_names=['Job Description Summarizer','CV ATS Rating'])
if __name__ == "__main__":
bespokecv.launch(auth=(os.environ['username'], os.environ['password'])) |