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from docx.opc.exceptions import PackageNotFoundError
def read_file(file_path):
"""
Reads the content of a file. If the file is a PDF, it extracts the text using PyPDF2.
If the file is a docx, it extracts the text using python-docx.
Otherwise, it reads the file as a text file, trying different encodings if 'utf-8' fails.
"""
# Check if the file exists before proceeding
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
if file_path.lower().endswith('.pdf'):
with open(file_path, 'rb') as file: # Open in binary read mode for PDFs
reader = PyPDF2.PdfReader(file)
text = ""
for page_num in range(len(reader.pages)):
page = reader.pages[page_num]
text += page.extract_text()
return text
elif file_path.lower().endswith('.docx'):
# Handle docx files using python-docx
try:
doc = Document(file_path)
text = ""
for paragraph in doc.paragraphs:
text += paragraph.text + "\n" # Add newline for paragraph separation
return text
# Use the imported exception class
except PackageNotFoundError:
# Provide a more informative error message if the file is not a valid docx
raise PackageNotFoundError(f"The file {file_path} is not a valid docx file. It may be corrupted or of a different format.")
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/content/drive/MyDrive/Resume/firm-capsule-436804-b5-5f553d9f1043.json"
import os
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# from langchain_community.vectorstores.faiss import FAISS
from docx import Document
import google.generativeai as genai
from datetime import datetime
import PyPDF2
api_key_google = os.environ.get('GOOGLE_GEMINI_KEY')
genai.configure(api_key=api_key_google)
# Mount Google Drive
model = genai.GenerativeModel('gemini-pro')
def similarity_main(tailored_resume, job_description):
"""
Use Gemini Pro to evaluate the relevance score between a tailored resume and job description.
Args:
- tailored_resume (str): Tailored resume content.
- job_description (str): Job description content.
Returns:
- dict: A dictionary containing the 'score' and 'reason'.
"""
prompt = f"""
You are a recruitment expert evaluating how well a tailored resume aligns with a job description. Provide a realistic and concise evaluation based on the following criteria:
1. Relevance of skills and experience: Do the candidate’s skills, accomplishments, and experience meet the job's core requirements?
2. Domain Match: Are the candidate's experiences and achievements relevant to the industry or role?
3. Clarity and Conciseness: Is the resume well-structured and focused on the job requirements?
4. Highlight any gaps or mismatched qualifications realistically.
Provide your response in this exact format:
Score: [Score between 0 and 1]
Reason: [One or two sentences explaining the score]
Here is the tailored resume:
[Resume Start]
{tailored_resume}
[Resume End]
And the job description below:
[Job Description Start]
{job_description}
[Job Description End]
"""
try:
# Get the response from Gemini Pro
response = model.generate_content(prompt)
candidates = response.candidates
if not candidates or len(candidates) == 0:
raise ValueError("No candidates found in the response.")
# Extract content text
content_text = candidates[0].content.parts[0].text
# Extract score and reason with simple parsing
lines = content_text.split("\n")
score = None
reason = None
print(content_text)
for line in lines:
if line.lower().startswith("score:"):
try:
score = float(line.split(":", 1)[1].strip())
except ValueError:
raise ValueError(f"Invalid score format: {line}")
elif line.lower().startswith("reason:"):
reason = line.split(":", 1)[1].strip()
# Ensure both score and reason are extracted
if score is None:
raise ValueError("Failed to extract score from the response.")
if not reason:
reason = "No reason provided."
return score
except Exception as e:
print(f"Error in relevance checking: {e}")
return None
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