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# -*- 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
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "firm-capsule-436804-b5-5f553d9f1043.json"
# !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
api_key_google = 'AIzaSyC8rXXpyVnAnnMG1rxPOF0JpWWPnCH1h_Y'
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:",formatted_resume)
if formatted_resume:
prompt = f"""
Below is the candidate's original formatted resume content:
[Resume Start]
{formatted_resume}
[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, education, and specific skills.
2. Avoids placeholders like "[Your Name]" and includes actual details.
3. Emphasizes experiences that are directly relevant to the job description.
"""
try:
response = model.generate_content(prompt)
return response.candidates[0].content.parts[0].text
except Exception as e:
print("Error in tailoring resume:", e)
return None
else:
return "Failed to generate resume text."
# 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()
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