Built to order

Bulk File Renamer Utility

This project is a desktop utility that renames hundreds or thousands of files in one pass using a stack of rules — prefixes, numbering, text replacement, case changes and date patterns. A live preview shows every new name before anything is touched, conflicts are detected up front, and a backup log makes the whole operation undoable. Rule stacks can be saved as shareable templates. Suitable for B.E./B.Tech final-year projects in Computer Science and IT.

Bulk File Renamer Utility — project thumbnail preview
More project photos (2)

The problem

Anyone who has come back from a trip with 1,200 files named IMG_20250112_084211.jpg knows the pain: manual renaming is not an option, and the existing free renamers are either ad-ridden, Windows-only abandonware, or destructive without undo. This project builds a proper bulk renamer as a desktop application: load files or whole folders, stack rename rules (add prefix/suffix, sequential numbering with padding, find-and-replace, case conversion, EXIF/date insertion), and watch every new filename preview live as rules are edited. Before renaming, the engine checks for collisions and reserved names; during the run it writes a backup CSV mapping old names to new ones, so one click (or Ctrl+Z) restores everything. Saved templates capture whole rule stacks — "photo trip sort", "music library tidy" — as shareable JSON. The demo ships with a realistic photo-folder scenario so the preview, log and undo all work out of the box.

How it works

  1. The user adds files or a folder (optionally including subfolders); the file list loads with sizes and current names.
  2. Rules are added to the stack — e.g. prefix "Goa2025_", then 3-digit numbering, then strip "IMG_", then lowercase extensions.
  3. The preview engine applies the rule stack to every filename in order and renders the before/after table live.
  4. On Rename, a pre-flight pass checks for collisions, illegal characters and reserved names; conflicts are reported, not executed.
  5. A backup CSV (old name → new name) is written, then files are renamed with per-file error handling.
  6. The run log records timing, affected counts per rule and any skips; the Undo button (or Ctrl+Z) reverses the run from the backup.
  7. The rule stack can be saved as a named JSON template and reapplied to any future folder in one click.

Tech stack:

  • Desktop: Python with Tkinter/PyQt (or Electron + Node.js)
  • File operations: OS-level rename with atomic per-file handling
  • Preview engine: pure-function rule pipeline, testable without touching disk
  • Templates: JSON rule-stack serialization
  • Logging: timestamped run logs with CSV backup
  • Packaging: PyInstaller / Electron Builder notes
Parameter Value
Rule types 8+ (prefix, suffix, numbering, replace, case, date, trim, extension)
Preview Live, per-file, updates on each keystroke
Conflict check Pre-flight collision + reserved-name detection
Undo Full restore from backup CSV
Demo scenario 1,248 files renamed in approximately 6 s (design target)
Templates JSON, shareable, one-click apply

Project features

  • [Rule stacking] Combine prefix/suffix, sequential numbering with zero-padding, find-and-replace, case conversion and date-pattern rules in any order.
  • [Live before/after preview] Every filename's new name updates instantly as rules are typed — nothing is renamed blind.
  • [Conflict detection] Pre-flight check flags name collisions and reserved/illegal names before the run starts.
  • [One-click undo] A backup CSV maps every old name to its new name; undo restores the originals exactly.
  • [Rename log] Timestamped run log with per-rule affected counts, errors and skips, exportable as text.
  • [Shareable templates] Save any rule stack as JSON, apply it in one click, share it with others.
  • [Folder recursion] Include subfolders, filter by extension or name pattern, and select/deselect individual files.

What is included

  • Complete desktop application source code
  • Rule engine with live preview and conflict detection
  • Undo system with backup CSV logging
  • Template save/apply module with sample templates
  • Packaging notes (installer build steps)
  • Project report PDF (background, rule-engine design, safety design, methodology)
  • PPT presentation for final review
  • Viva Q&A preparation document (rule pipelines, filesystem safety, undo design)

Limitations & prerequisites

  • The demo's 1,248-files-in-6-seconds figure is a design target measured on the developer's machine class, not a guaranteed speed on all hardware.
  • Undo covers renames performed by the app from its own backup CSV; files renamed or moved externally afterwards may not restore cleanly.
  • EXIF-date rules depend on metadata actually present in the files; files without EXIF fall back to file dates.
  • Network-drive and cloud-synced folders (OneDrive/Dropbox) can lock files mid-rename; the log reports these as skips.
  • The demo UI is a desktop window; a web version of the renamer is future scope.

Frequently Asked Questions

How does the live preview work?

The rule stack is a pure-function pipeline: each rule transforms the filename list in order, and the table re-renders on every keystroke. No file is touched until you press Rename.

What happens if two files would get the same new name?

The pre-flight check detects collisions before anything runs and reports them. You can fix the rules or enable skip-on-conflict; the app never overwrites silently.

How does undo work?

Before renaming, the app writes a backup CSV mapping every old name to its new name. Undo reads that file and renames everything back — exact restore, including the original order.

What are templates?

A saved rule stack as JSON — e.g. "photo trip sort" (prefix + numbering + lowercase ext). Apply it to any folder in one click, and share the JSON file with others.

Can it handle subfolders?

Yes — folder recursion is a toggle, with extension and name-pattern filters so you can target just .jpg files three levels deep, for example.

Is this project suitable for a final-year project?

Yes — for Computer Science and IT programs. It is a real desktop utility with a rule engine, filesystem safety design and undo — the kind of careful engineering examiners respect. Suitable for B.E./B.Tech final-year projects in Computer Science and IT.

Components & software requirements
  • Desktop: Python with Tkinter/PyQt (or Electron + Node.js)
  • File operations: OS-level rename with atomic per-file handling
  • Preview engine: pure-function rule pipeline, testable without touching disk
  • Templates: JSON rule-stack serialization
  • Logging: timestamped run logs with CSV backup
  • Packaging: PyInstaller / Electron Builder notes
Delivery information

Built-to-order project. Delivery timeline is shared after order confirmation based on current queue.

Support terms

Complete documentation, setup guide, and viva preparation included. Support for setup and explanation provided.

Download abstract (PDF)

Related guides

All guides
Illustration of JWT authentication: a brass key handing a glowing sealed token to a server rack and a laptop login screen, linked by a chain motif.B.E./B.Tech Computer Science and IT students adding login and protected APIs to their final-year web projects

JWT Authentication for Students: Tokens, Signatures, Refresh Flows and a Node.js Implementation

How does JWT login actually work? When a user logs in, the server issues a signed token in three parts — header, payload, signature. The client sends it back as an Authorization: Bearer header, and the server verifies the signature instead of looking up a session. This guide decodes a real token by hand, walks through the full login and refresh flow, and builds a working Node.js implementation with bcrypt password hashing, token rotation, and storage rules that survive a viva.

Read guide
Illustration of SQL versus NoSQL: neat filing-cabinet table rows on one side against flexible nested document cards on the other, joined by dotted lines.B.E./B.Tech Computer Science and IT students choosing and designing the database for their final-year project

SQL vs NoSQL for Final-Year Projects: Which Database Should You Pick?

MySQL or MongoDB for your final-year project? SQL databases store data in related tables with enforced schemas, joins and transactions — the right default when your data is structured and money or records must stay consistent. NoSQL document stores trade the rigid schema for flexible, nested documents that ship faster when your data shape keeps changing. This guide compares them with a worked hospital-appointment example in both, a decision table, and rules matched to common project archetypes.

Read guide
Editorial illustration of shipping containers transforming into glowing software windows beside a laptop showing container layers, in blue and teal tones.B.E./B.Tech Computer Science and IT final-year students shipping web/backend projects

Docker for Student Projects: Images, Containers and Compose from Zero

End ‘it works on my machine’ failures: learn what Docker images and containers actually are, write lean Dockerfiles that exploit layer caching, persist data with volumes, orchestrate app-plus-database with Compose, and package an evaluator-proof submission — with the debugging table for every error you will definitely meet.

Read guide
Get a quotation