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| import streamlit as st | |
| import google.generativeai as genai | |
| from pdf_reader import read_pdf | |
| import os | |
| import logging | |
| # Configure the Generative AI model | |
| genai.configure(api_key=os.environ.get('GOOGLE_API_KEY')) | |
| model = genai.GenerativeModel("gemini-1.5-flash") | |
| def generate_response(prompt): | |
| """Generate content using the Generative AI model.""" | |
| try: | |
| response = model.generate_content(prompt) | |
| return response.text.strip() | |
| except Exception as e: | |
| return f"Error generating response: {str(e)}" | |
| def profile(pdf_doc, job_desc, user_type): | |
| logging.info("Starting profile analysis.") | |
| try: | |
| if not pdf_doc or not job_desc.strip(): | |
| logging.warning("No PDF document or job description provided.") | |
| raise ValueError("Missing resume or job description.") | |
| pdf = read_pdf(pdf_doc) | |
| st.markdown("Resume Upload Finished!") | |
| logging.info("PDF content successfully read.") | |
| ats_score = generate_response( | |
| f"Compare the resume '{pdf}' with the job description '{job_desc}' and & suggest the ATS(Applicant Tracking System) Score(in pecentage) of the resume. Just mention only the score in integer. Nothing else" | |
| ) | |
| logging.info(" Processed ..ats_score computation ") | |
| fittment = generate_response( | |
| f"Assess if the candidate is a good fit for the job role based on the resume '{pdf}' and job description '{job_desc}'. mention clearly if the candidate will be a good fit for this Job role(Yes/NO).Followed by justification in Just one line/scentance of maxmimum 15 tokens." | |
| ) | |
| logging.info(" Processed ..fittment computation ") | |
| # Results for Applicants | |
| if user_type == "Applicant": | |
| improvement_tips = generate_response( | |
| f"Suggest improvements to the resume '{pdf}' to better align with the job description '{job_desc}'. Provide comments in bullet points. Total not more than 300 words" | |
| ) | |
| logging.info(" Processed ..improvement_tips computation ") | |
| resume_narrative = generate_response( | |
| f"Rewrite the resume '{pdf}' to highlight relevant skills and experience accordng to the job description'{job_desc}'.Total not more than 500 tokens" | |
| ) | |
| logging.info(" Processed ..resume_narrative computation ") | |
| return f""" | |
| ### ATS Score: {ats_score} | |
| **Fit for the Role:** {fittment} | |
| --- | |
| ### Suggestions for Improvement | |
| {improvement_tips} | |
| --- | |
| ### Suggested Resume | |
| {resume_narrative} | |
| """ | |
| # Results for Recruiters | |
| elif user_type == "Recruiter": | |
| return f""" | |
| ### ATS Score: {ats_score} | |
| **Fit for the Role:** {fittment} | |
| """ | |
| else: | |
| raise ValueError("Invalid user type selected.") | |
| except ValueError as ve: | |
| st.warning(str(ve)) | |
| except Exception as e: | |
| st.error(f"An unexpected error occurred: {str(e)}") | |