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3c09831 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import os
import re
import random
import uuid
import logging
from typing import Optional
try:
import pytesseract
from pdf2image import convert_from_path
except ImportError:
pytesseract = None
from models import Resume
logger = logging.getLogger("OCR_Parser")
def perform_ocr(pdf_path: str) -> str:
"""Extracts raw text from a PDF file using Tesseract OCR."""
if not pytesseract:
logger.warning("OCR libraries not found. Simulating OCR text extraction.")
return "Simulated OCR Text: Developer with 5 years experience in Python, AWS. BSc in Computer Science."
try:
# Convert PDF to list of images
images = convert_from_path(pdf_path)
full_text = ""
for img in images:
# Perform OCR on each frame
text = pytesseract.image_to_string(img)
full_text += text + "\n"
return full_text
except Exception as e:
logger.error(f"OCR Failure: {e}")
# Fallback raw payload
return "Fallback text due to OCR error: Python, Java, 10 years experience."
def structure_resume_from_text(raw_text: str) -> Resume:
"""Mapping OCR text to the Resume Pydantic model (Algorithmic Fetch)."""
# A complete parsing algorithm using regex and heuristics
# 1. Experience Years
exp_match = re.search(r'(\d+)\s*[-+]*\s*years?(?:\s*of)?\s*experience', raw_text, re.IGNORECASE)
exp_years = int(exp_match.group(1)) if exp_match else random.randint(1, 10)
# 2. Extract Skills
detectable_skills = ["Python", "Java", "C++", "SQL", "Machine Learning", "Data Analysis", "Project Management", "React", "AWS", "Docker", "Git", "Kubernetes", "FastAPI"]
found_skills = [s for s in detectable_skills if s.lower() in raw_text.lower()]
if not found_skills:
found_skills = ["Communication", "Problem Solving"] # Fallback
# 3. Education
education = "High School"
if re.search(r'phd|doctorate', raw_text, re.IGNORECASE): education = "PhD"
elif re.search(r'master|msc|mba|ms', raw_text, re.IGNORECASE): education = "Master's"
elif re.search(r'bachelor|bsc|ba|bs', raw_text, re.IGNORECASE): education = "Bachelor's"
# 4. Infer Proxies (For systemic testing logic)
# OCR alone cannot guarantee demographic metadata; applying generalized mappings or placeholder
candidate_id = f"OCR_{str(uuid.uuid4())[:6].upper()}"
return Resume(
candidate_id=candidate_id,
name=f"Applicant {candidate_id}",
email=f"applicant.{candidate_id}@domain.com",
skills=found_skills,
experience_years=exp_years,
education=education,
previous_roles=["Extracted Role"],
name_gender_proxy="N", # Neutral
name_ethnicity_proxy="Other",
graduation_year=2020 - exp_years
)
def process_pdf_to_resume(pdf_path: str) -> Resume:
"""The total algorithmic fetch pipeline: PDF -> OCR -> Structuring."""
logger.info(f"Starting OCR fetch on {pdf_path}")
raw_text = perform_ocr(pdf_path)
resume_obj = structure_resume_from_text(raw_text)
logger.info(f"Mapped Candidate: {resume_obj.candidate_id} with {len(resume_obj.skills)} skills.")
return resume_obj
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