resume_generation_gemini_pro.py

# -*- 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."

#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)
    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}")

    return tailored_resume

# 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()