transfuzzy 0.1.2

TransFuzzy is a robust transliteration system that bridges the gap between Indic scripts and the Latin alphabet.

TransFuzzy

TransFuzzy is a Python package for multilingual personal-name matching across Latin and several Indic scripts. It exposes the same matching pipeline through a CLI and a Flask API, and it supports switching between the bundled dataset and user-managed datasets.

What It Does

• Accepts names in Latin, Devanagari, Telugu, Tamil, Kannada, Malayalam, Gujarati, and Gurmukhi.
• Transliterates non-Latin input before matching.
• Scores candidate names with phonetic, edit-distance, and embedding-based features.
• Returns the best matches through transfuzzy predict or the HTTP API.
• Lets you upload, activate, list, and delete datasets without modifying package files.

Installation

From PyPI

pip install transfuzzy

Local development setup

uv sync

Python 3.11+ is required.

Runtime Notes

TransFuzzy currently loads the sentence-transformers/all-MiniLM-L6-v2 model during module import. On a fresh machine, the first CLI, API, or test run may download model files from Hugging Face before the command can complete.

That has two practical consequences:

• The first run can be noticeably slower than later runs.
• Offline or restricted-network environments can fail before the CLI help text, API startup, or tests finish loading.

Quick Start

Start the API server

transfuzzy

or

transfuzzy serve --port 3000

The Flask server listens on http://localhost:3000 and opens that URL in your default browser on startup.

Query from the CLI

transfuzzy predict "Rahul"

Limit results:

transfuzzy predict "Rahul" --top 5

Return JSON:

transfuzzy predict "Rahul" --json

Use a specific text dataset file directly:

transfuzzy predict "Rahul" --db .\names.txt --top 5 --json

Supported Input Scripts

Examples of valid input:

Rahul
राहुल
రాహుల్

The output is transliterated back to the original script when the input was converted from a supported Indic script.

CLI Reference

transfuzzy

Starts the API server on port 3000 and opens the browser automatically.

transfuzzy serve

Run the API server explicitly.

transfuzzy serve --port 3000

Use --no-browser to skip opening the browser:

transfuzzy serve --port 3000 --no-browser

transfuzzy predict

Find similar names for a single input.

transfuzzy predict <name> [--top N] [--json] [--db PATH]

Arguments:

• <name>: required input string.
• --top: maximum number of matches to return. Default: 10.
• --json: print a JSON object with similar_names.
• --db: use a dataset file path directly instead of the active managed dataset.

transfuzzy db

Manage datasets stored in the TransFuzzy home directory.

Add a dataset:

transfuzzy db add .\names.txt

List managed datasets:

transfuzzy db list

Set the active dataset:

transfuzzy db use names.txt

Delete a managed dataset:

transfuzzy db delete names.txt

API Reference

POST /similar_names

Request body:

{
  "name": "Rahul"
}

Success response:

{
  "similar_names": ["Rahul", "Raahul", "Rahool"]
}

Validation errors are returned as JSON with an error field and the appropriate HTTP status code.

POST /upload_db

Uploads a dataset file using multipart/form-data with the field name file.

Success response shape:

{
  "message": "Dataset 'demo.txt' uploaded",
  "dataset_name": "demo.txt",
  "active_db": null
}

GET /list_dbs

Returns the stored managed datasets and the active dataset name.

{
  "datasets": ["demo.txt"],
  "active_db": "demo.txt"
}

POST /use_db

Request body:

{
  "name": "demo.txt"
}

DELETE /delete_db

Request body:

{
  "name": "demo.txt"
}

Dataset Management

There are two ways to provide names:

1. Pass a file path directly with --db.
2. Store datasets with transfuzzy db ... or the dataset API routes and switch the active dataset.

Managed datasets are stored under:

%USERPROFILE%\.transfuzzy\datasets

The active dataset name is stored in:

%USERPROFILE%\.transfuzzy\config.json

To override the base directory, set:

$env:TRANSFUZZY_HOME = "C:\path\to\custom-home"

Each dataset should contain one name per line.

How Matching Works

The current pipeline is:

Input name
-> transliteration to Latin when needed
-> candidate pair generation from the selected dataset
-> feature computation
   - Soundex ratio
   - Metaphone ratio
   - Levenshtein ratio
   - Jaro-Winkler similarity
   - Cosine similarity
   - Euclidean similarity
   - Manhattan similarity
   - Pearson similarity
-> trained model ranking
-> optional transliteration back to the input script

Project Structure

src/transfuzzy/
├── app.py              Flask app and HTTP routes
├── cli.py              CLI entrypoint
├── core/
│   ├── config.py       package constants and paths
│   ├── db_manager.py   managed dataset storage
│   └── pipeline.py     top-level matching pipeline
├── datasets/
│   └── default.txt     bundled dataset asset
├── db/                 packaged training/runtime artifacts
├── dir/                feature generation and training scripts
├── static/             browser-side assets
├── templates/          HTML templates
└── utils/              helper and response utilities

Development

Run the app locally:

uv run transfuzzy serve

Run tests:

uv run python -m unittest discover -s tests -v

Run training-related scripts:

uv run python src/transfuzzy/dir/enrich_data.py
uv run python src/transfuzzy/dir/train_model.py

More development notes are in docs/DEVELOPMENT.md.

Current Limitations

• Import-time model loading makes commands and tests depend on model availability.
• The package metadata says the project is a transliteration system, while the implementation is broader name matching.
• The repository contains packaged model and dataset artifacts in src/transfuzzy/db, so development and release size are coupled to those files.

License

MIT. See LICENSE.