# -*- coding: utf-8 -*- """Resume_generation_Gemini_pro.ipynb Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/16z793IRwVmvKYCaOLGZFDYj-XOj8zEJL """ # from google.colab import drive,userdata # drive.mount('/content/drive') # !pip install streamlit -qq # !pip install PyPDF2 -qq # !pip install langchain_community -qq # !pip install langchain_google_genai -qq # !pip install python-docx -qq # !pip install docx2txt -qq # !pip install faiss-gpu -qq # !pip install google-generativeai -qq # !pip install --upgrade google-generativeai -qq import docx2txt import PyPDF2 def extract_text(file_path): if file_path.endswith(".docx"): # Extract text from DOCX file return docx2txt.process(file_path) elif file_path.endswith(".pdf"): # Extract text from PDF file text = "" with open(file_path, 'rb') as file: reader = PyPDF2.PdfReader(file) for page_num in range(len(reader.pages)): text += reader.pages[page_num].extract_text() return text else: raise ValueError("Unsupported file type") # from google.colab import auth # auth.authenticate_user() import os GOOGLE_APPLICATION_CREDENTIALS = os.environ["GOOGLE_APPLICATION_CREDENTIALS"] private_key_id = os.environ.get('PRIVATE_KEY_ID') private_key = os.environ.get('PRIVATE_KEY') client_id = os.environ.get('CLIENT_ID') # !pip install python-docx import os import streamlit as st from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.vectorstores.faiss import FAISS # from google.colab import drive from docx import Document import google.generativeai as genai from datetime import datetime from docx.enum.text import WD_PARAGRAPH_ALIGNMENT api_key_google = os.environ.get('GOOGLE_GEMINI_KEY') genai.configure(api_key=api_key_google) # Mount Google Drive # drive.mount('/content/drive') model = genai.GenerativeModel('gemini-pro') def save_resume_to_docx(tailored_resume, file_path): doc = Document() doc.add_heading('Tailored Resume', level=1) doc.add_paragraph(tailored_resume) doc.save(file_path) # Function to read text from a .docx file def read_docx(file_path): doc = Document(file_path) return "\n".join([para.text for para in doc.paragraphs]) # def generate_resume_text(resume_text): # prompt = f""" # Given the following resume content: # [Resume Start] # {resume_text} # [Resume End] # Format this resume content with appropriate section titles. Only use the information provided and avoid placeholders like "[Your Name]". Ensure it retains the structure and details exactly as shown. # """ # try: # response = model.generate_content(prompt) # print(response) # # Accessing the generated text content # return response.candidates[0].content.parts[0].text # except Exception as e: # print("Error in generating resume text:", e) # return None def tailor_resume(resume_text, job_description): # Use the generate_resume_text function to get the formatted resume content # formatted_resume = generate_resume_text(resume_text) # print("formatted resume:",resume_text) prompt = f""" Below is the candidate's original resume content: [Resume Start] {resume_text} [Resume End] Using the candidate's resume above and the job description below, create a tailored resume. [Job Description Start] {job_description} [Job Description End] Please generate a resume that: 1. Uses real data from the candidate's resume, including name, and education. 2. Avoids placeholders like "[Your Name]" and includes actual details. This is important. 3. In the experience section, emphasizes professional experiences and skills that are directly relevant to the job description. 4. Keeps only a maximum of the top three accomplishments/ responsibilities for each job position held so as to make the candidate standout in the new job role 5. Removes special characters from the section titles. 6. Only includes publications if the job description is research based. 7. Summarizes the skills and technical skills section into a brief profile. 8. Does not include courses, certification, references, skills and a technical skills sections if they are not relevant. 9. Only includes true information about the candidate. 10.Provide the text in markdown format that clearly identifies the headings and subheadings. """ try: response = model.generate_content(prompt) print(response.candidates[0].content.parts[0].text) return response.candidates[0].content.parts[0].text except Exception as e: print("Error in tailoring resume:", e) return None def add_bold_and_normal_text(paragraph, text): """Adds text to the paragraph, handling bold formatting.""" while "**" in text: before, bold_part, after = text.partition("**") if before: paragraph.add_run(before) if bold_part == "**": bold_text, _, text = after.partition("**") paragraph.add_run(bold_text).bold = True else: text = after if text: paragraph.add_run(text) def convert_resume_to_word(markdown_text,output_file): # Create a new Word document doc = Document() # Split the text into lines for processing lines = markdown_text.splitlines() for line in lines: if line.startswith("## "): # Main heading (Level 1) paragraph = doc.add_heading(line[3:].strip(), level=1) paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY elif line.startswith("### "): # Subheading (Level 2) paragraph = doc.add_heading(line[4:].strip(), level=2) paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY elif line.startswith("- "): # Bullet points paragraph = doc.add_paragraph() add_bold_and_normal_text(paragraph, line[2:].strip()) paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY elif line.startswith("* "): # Sub-bullet points or normal list items paragraph = doc.add_paragraph(style="List Bullet") add_bold_and_normal_text(paragraph, line[2:].strip()) paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY elif line.strip(): # Normal text (ignores blank lines) paragraph = doc.add_paragraph() add_bold_and_normal_text(paragraph, line.strip()) paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.JUSTIFY # Save the Word document doc.save(output_file) print(f"Markdown converted and saved as {output_file}") #Entry function for the model def generate_gemini(current_resume,job_description): st.header('Resume Tailoring') # Load the resume and job description from Google Drive resume_text = extract_text(current_resume) job_description = extract_text(job_description) # Tailor resume based on job description tailored_resume = tailor_resume(resume_text, job_description) output_file = f"Tailored_Resume_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx" convert_resume_to_word(tailored_resume,output_file) st.success(f"Tailored resume saved to {output_file}") output_text = read_docx(output_file) st.write(output_text) return tailored_resume, output_file # Main function for Streamlit app # def Gemini_pro_main(current_resume,job_description): # st.header('Resume Tailoring') # # Load the resume and job description from Google Drive # resume_text = extract_text(current_resume) # job_description = extract_text(job_description) # # Tailor resume based on job description # tailored_resume = tailor_resume(resume_text, job_description) # st.write("**Tailored Resume:**") # st.write(tailored_resume) # print(tailored_resume) # # Save the tailored resume to a .docx file # if tailored_resume: # file_path = f"Tailored_Resume_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx" # save_resume_to_docx(tailored_resume, file_path) # st.success(f"Tailored resume saved to {file_path}") # if __name__ == '__main__': # main()