📂 Automated Resume Renamer & Organizer

For Ubuntu 24.04 using Local AI (Ollama)

1. The Problem

As an HR officer or Professor, you know that students and applicants rarely follow file naming conventions. You likely have a folder that looks like this:

Resume.pdf
CV_Final_v2.docx
MyResume(1).pdf
john_doe.pdf

The Goal: Automatically rename these files based on their content to a standard format:

YYMMDD Name Degree/Background.pdf

Example: 250101 Juan Dela Cruz BS Information Technology.pdf

2. Requirements Checklist

Please ensure you have the following ready before starting.

[ ] Ubuntu 24.04 System (Updated).
[ ] Python 3.12+ (Pre-installed on Ubuntu 24.04).
[ ] Ollama installed locally (The AI engine).
[ ] A Small Language Model pulled (e.g., granite3.3:2b or llama3.2).
[ ] Python Libraries: pdfplumber (for PDFs), python-docx (for Word), requests (to talk to Ollama).
[ ] No Images: The files must have embedded text. This script excludes OCR (Optical Character Recognition) to keep it fast and lightweight. Scanned images will be skipped.

3. How the Script Works (The Logic)

This script acts as a "Project Manager" that hires two distinct specialists to process each file. It does not blindly ask the AI for everything, as small AIs make mistakes with math and dates.

File Discovery:

The script looks for .pdf and .docx files in the folder where the script is located.

Text Extraction:

It pulls raw text. If the text is less than 50 characters (likely an image scan), it skips the file to prevent errors.

The Date Specialist (Python Regex):

Logic: It scans the text for explicit years (e.g., "2023", "2024").
Rule: It ignores the word "Present". Why? If a resume from 2022 says "2022 - Present", treating "Present" as "Today" (2026) would incorrectly date the old resume. We stick to the highest printed number.
Output: Sets the date to Jan 1st of the highest year found (e.g., 240101).

The Content Specialist (Ollama AI):

Logic: It sends the text to the local AI with strict instructions.
Rule 1 (Priority): It looks for a Degree (e.g., "BS IT") first. It is forbidden from using "Intern" or "Student" if a degree is found.
Rule 2 (Fallback): If the AI fails to find a name, the script grabs the first line of the document as a fallback.

Sanitization & Renaming:

It fixes "Spaced Names" (e.g., J O H N -> John).
It ensures the filename isn't too long.

It renames the file only if the name doesn't already exist.

4. Installation Guide (Ubuntu 24.04)

Open your terminal (Ctrl+Alt+T) and follow these steps exactly.

Step A: System Update

Ensure your system tools are fresh to avoid installation conflicts.

sudo apt update && sudo apt upgrade -y

Step B: Install Ollama & The Model

Install the Ollama Engine:

curl -fsSL [https://ollama.com/install.sh](https://ollama.com/install.sh) | sh

Download the Brain (The Model): We use granite3.3:2b because it is very fast and follows formatting rules well.

ollama pull granite3.3:2b

(Note: You can swap this for llama3 if you have a powerful computer, but Granite is sufficient for this task).

Step C: Setup Python Environment

Ubuntu 24.04 requires Virtual Environments (venv) for Python scripts to prevent breaking system tools.

Create a Project Folder:

mkdir ~/resume_renamer

cd ~/resume_renamer

Create the Virtual Environment:

python3 -m venv venv

Activate the Environment:

source venv/bin/activate

(You should see (venv) at the start of your command line now).
Install Required Libraries:

pip install requests pdfplumber python-docx

Step D: Create the Script

Create the python file:

nano rename_resumes.py

Paste the code found in the file block below.

======== START CODE

import os

import requests

import json

import pdfplumber

import re

from datetime import datetime

import time

# --- OPTIONAL DEPENDENCY: python-docx ---

DOCX_AVAILABLE = False

try:

from docx import Document

DOCX_AVAILABLE = True

except ImportError:

print("Warning: 'python-docx' not found. .docx files will be skipped.")

print("To support Word docs, run: pip install python-docx")

# --- CONFIGURATION ---

FOLDER_PATH = os.path.dirname(os.path.abspath(__file__))

# You can change this to "llama3" or "mistral" if installed

OLLAMA_MODEL = "granite3.3:2b"

# ---------------------

def get_os_creation_date(filepath):

"""Last resort: Gets OS file creation date in YYMMDD format."""

try:

timestamp = os.path.getctime(filepath)

return datetime.fromtimestamp(timestamp).strftime('%y%m%d')

except:

return datetime.now().strftime('%y%m%d')

def extract_latest_year_heuristic(text):

"""

Scans for years (2000-2059), including spaced years (2 0 2 4).

Returns the HIGHEST year found.

"""

current_year = datetime.now().year

found_years = []

# 1. Standard Years (e.g., "2024", "2023-2024")

matches_standard = re.findall(r'(?<!\d)(20[0-5][0-9])(?!\d)', text)

if matches_standard:

found_years.extend([int(y) for y in matches_standard])

# 2. Spaced Years (e.g., "2 0 2 4")

matches_spaced = re.findall(r'(?<!\d)2\s+0\s+[0-5]\s+[0-9](?!\d)', text)

if matches_spaced:

for m in matches_spaced:

clean_year = int(m.replace(" ", ""))

found_years.append(clean_year)

if found_years:

valid_years = [y for y in found_years if y <= current_year + 5]

if valid_years:

latest_year = max(valid_years)

short_year = str(latest_year)[2:]

return f"{short_year}0101"

return None

def extract_text_from_docx(filepath):

"""Reads text from .docx files, including tables."""

if not DOCX_AVAILABLE:

return ""

try:

doc = Document(filepath)

full_text = []

for para in doc.paragraphs:

full_text.append(para.text)

for table in doc.tables:

for row in table.rows:

for cell in row.cells:

full_text.append(cell.text)

return "\n".join(full_text)

except Exception as e:

print(f"[ERROR] Reading DOCX: {e}")

return ""

def clean_text_for_llm(text):

clean = " ".join(text.split())

# Limit to 4000 chars to prevent choking small models

return clean[:4000]

def ask_ollama(text):

system_instruction = (

"You are a data extraction assistant. "

"Extract the applicant's **Full Name** and **Background**."

"\n\n**Background Extraction Rules (STRICT):**\n"

"1. **MANDATORY:** You MUST prefer the **Educational Degree** over any job title.\n"

" - Example: If text says 'IT Intern' AND 'Diploma in Information Technology', output 'Diploma in Information Technology'.\n"

" - Example: If text says 'Mechanical Engineering Student', output 'Diploma in Mechanical Engineering' (if listed) or 'Mechanical Engineering'.\n"

"2. **FORBIDDEN:** Do NOT use 'Intern', 'Student', 'Assistant', or 'Worker' as the background unless NO degree is mentioned.\n"

"\nOutput strictly in this format: Name | Background."

"\nDo NOT include notes, explanations, or numbered lists."

)

prompt = f"Resume Text:\n{text}\n\n{system_instruction}"

url = "http://localhost:11434/api/generate"

data = {

"model": OLLAMA_MODEL,

"prompt": prompt,

"stream": False,

"options": {

"temperature": 0.1,

"num_ctx": 4096

}

try:

# Added timeout to prevent hanging on one file

response = requests.post(url, json=data, timeout=60)

response.raise_for_status()

result = response.json()['response'].strip()

return result

except Exception as e:

print(f" [Warning] Ollama call failed: {e}")

return None

def fix_spaced_names(text):

# Fixes "J O H N" -> "JOHN"

return re.sub(r'(?<=\b[A-Za-z])\s+(?=[A-Za-z]\b)', '', text)

def clean_extracted_string(s):

# Remove lists (1.), labels (Name:), and fix spacing

s = re.sub(r'^(1\.|2\.|Name:|Background:|\d\W)', '', s, flags=re.IGNORECASE)

s = fix_spaced_names(s)

s = s.split('\n')[0]

s = re.split(r'(?i)note\s*:', s)[0]

# Truncate to safe filename length

if len(s) > 60:

s = s[:60].strip()

return s.strip().title()

def get_name_fallback(text):

"""

If AI returns 'Name' or 'Unknown', this function grabs the

first non-empty line of the resume, which is usually the name.

"""

lines = [line.strip() for line in text.split('\n') if line.strip()]

ignore_list = ['resume', 'curriculum vitae', 'cv', 'profile', 'bio', 'page', 'summary', 'objective', 'name', 'contact']

for line in lines:

lower_line = line.lower()

if len(line) < 3 or any(w in lower_line for w in ignore_list):

continue

word_count = len(line.split())

if word_count > 5: continue # Names rarely have >5 words

if "looking for" in lower_line or "seeking" in lower_line: continue

if len(line) < 50 and not re.search(r'[0-9!@#$%^&*()_+={};"<>?]', line):

print(f" [Fallback] AI failed. Guessed name from first line: {line}")

return line

return "Unknown Applicant"

def process_folder():

print(f"--- Resume Renamer (Strict Degree Priority + Resilient) ---")

print(f"Working in: {FOLDER_PATH}\n")

count_success = 0

count_fail = 0

script_name = os.path.basename(__file__)

for filename in os.listdir(FOLDER_PATH):

# 1. Check Extension

file_ext = os.path.splitext(filename)[1].lower()

if filename == script_name:

continue

if file_ext == '.docx' and not DOCX_AVAILABLE:

continue

if file_ext not in ['.pdf', '.docx']:

continue

filepath = os.path.join(FOLDER_PATH, filename)

text = ""

# 2. Extract Text

print(f"Processing: {filename}...")

try:

if file_ext == '.pdf':

with pdfplumber.open(filepath) as pdf:

for i in range(min(2, len(pdf.pages))):

text += pdf.pages[i].extract_text() or ""

elif file_ext == '.docx':

text = extract_text_from_docx(filepath)

if len(text) < 50:

print(f" [SKIP] Text too short.")

count_fail += 1

continue

except Exception as e:

print(f" [ERROR] Reading file: {e}")

count_fail += 1

continue

# 3. GET DATE

date_str = extract_latest_year_heuristic(text)

if not date_str:

date_str = get_os_creation_date(filepath)

print(f" [Fallback] Using OS Date: {date_str}")

# 4. GET NAME/BG

# Add a tiny delay to give Ollama a breather between files

time.sleep(0.5)

llm_output = ask_ollama(clean_text_for_llm(text))

name = None

bg = "General"

if llm_output:

if "|" in llm_output:

parts = llm_output.split('|', 1)

name = parts[0].strip()

bg = parts[1].strip()

elif "\n" in llm_output:

lines = [line.strip() for line in llm_output.split('\n') if line.strip()]

if len(lines) >= 2:

name = lines[0]

bg = lines[1]

# --- IMPROVED FALLBACK CHECK ---

forbidden_names = ["name", "unknown", "resume", "applicant", "candidate", "full name"]

if not name or name.strip().lower() in forbidden_names:

name = get_name_fallback(text)

# -------------------------------

if name:

name = clean_extracted_string(name)

bg = clean_extracted_string(bg)

safe_name = re.sub(r'[^\w\s-]', '', name)

safe_bg = re.sub(r'[^\w\s-]', '', bg)

new_filename = f"{date_str} {safe_name} {safe_bg}{file_ext}"

new_filepath = os.path.join(FOLDER_PATH, new_filename)

if filepath != new_filepath:

if not os.path.exists(new_filepath):

os.rename(filepath, new_filepath)

print(f" -> Renamed: [{new_filename}]")

count_success += 1

else:

print(f" -> Duplicate: [{new_filename}]")

else:

print(" -> No change.")

else:

print(f" -> AI Format Fail: {llm_output}")

count_fail += 1

else:

print(" -> AI returned nothing.")

count_fail += 1

print(f"\nDone! Renamed: {count_success} | Failed: {count_fail}")

if __name__ == "__main__":

process_folder()

======== END CODE

Save and exit: Press Ctrl+O, Enter, then Ctrl+X.

5. Running the Renamer

This script is portable. It works on the files sitting next to it.

Copy the Script: Move the rename_resumes.py file into your folder full of PDFs (e.g., ~/Documents/Student_CVs).
Open Terminal in that folder: cd ~/Documents/Student_CVs
Activate your Python Environment (Point to where you created it): source ~/resume_renamer/venv/bin/activate
Run the script: python3 rename_resumes.py