# GPTC

General-purpose text classifier in Python

GPTC provides both a CLI tool and a Python library.

## CLI Tool

### Classifying text

    python -m gptc classify <compiled model file>

This will prompt for a string and classify it, then print (in JSON) a dict of
the format `{category: probability, category:probability, ...}` to stdout.

Alternatively, if you only need the most likely category, you can use this:

    python -m gptc classify [-c|--category] <compiled model file>

This will prompt for a string and classify it, outputting the category on
stdout (or "None" if it cannot determine anything).

### Compiling models

    python -m gptc compile <raw model file>

This will print the compiled model in JSON to stdout.

## Library

### `gptc.Classifier(model)`

Create a `Classifier` object using the given *compiled* model (as a dict, not
JSON).

#### `Classifier.confidence(text)`

Classify `text`. Returns a dict of the format `{category: probability,
category:probability, ...}`

#### `Classifier.classify(text)`

Classify `text`. Returns the category into which the text is placed (as a
string), or `None` when it cannot classify the text.

### `gptc.compile(raw_model)`
Compile a raw model (as a list, not JSON) and return the compiled model (as a
dict).

## Model format

This section explains the raw model format, which is how you should create and
edit models.

Raw models are formatted as a list of dicts. See below for the format:

    [
        {
            "text": "<text in the category>",
            "category": "<the category>"
        }
    ]

GPTC handles models as Python `list`s of `dict`s of `str`s (for raw models) or
`dict`s of `str`s and `float`s (for compiled models), and they can be stored
in any way these Python objects can be. However, it is recommended to store
them in JSON format for compatibility with the command-line tool.

## Example model

An example model, which is designed to distinguish between texts written by
Mark Twain and those written by William Shakespeare, is available in `models`.
The raw model is in `models/raw.json`; the compiled model is in
`models/compiled.json`.

## Benchmark

A benchmark script is available for comparing performance of GPTC between
different Python versions. To use it, run `benchmark.py` with all of the Python
installations you want to test. It tests both compilation and classification.
It uses the default Twain/Shakespeare model for both, and for classification it
uses [Mark Antony's "Friends, Romans, countrymen"
speech](https://en.wikipedia.org/wiki/Friends,_Romans,_countrymen,_lend_me_your_ears)
from Shakespeare's *Julius Caesar*.