# coding: utf8 from __future__ import print_function # NB! This breaks in plac on Python 2!! #from __future__ import unicode_literals import plac from spacy.cli import download as cli_download from spacy.cli import link as cli_link from spacy.cli import info as cli_info from spacy.cli import package as cli_package from spacy.cli import train as cli_train from spacy.cli import train_config as cli_train_config class CLI(object): """Command-line interface for spaCy""" commands = ('download', 'link', 'info', 'package', 'train', 'train_config') @plac.annotations( model=("model to download (shortcut or model name)", "positional", None, str), direct=("force direct download. Needs model name with version and won't " "perform compatibility check", "flag", "d", bool) ) def download(self, model=None, direct=False): """ Download compatible model from default download path using pip. Model can be shortcut, model name or, if --direct flag is set, full model name with version. """ cli_download(model, direct) @plac.annotations( origin=("package name or local path to model", "positional", None, str), link_name=("name of shortuct link to create", "positional", None, str), force=("force overwriting of existing link", "flag", "f", bool) ) def link(self, origin, link_name, force=False): """ Create a symlink for models within the spacy/data directory. Accepts either the name of a pip package, or the local path to the model data directory. Linking models allows loading them via spacy.load(link_name). """ cli_link(origin, link_name, force) @plac.annotations( model=("optional: shortcut link of model", "positional", None, str), markdown=("generate Markdown for GitHub issues", "flag", "md", str) ) def info(self, model=None, markdown=False): """ Print info about spaCy installation. If a model shortcut link is speficied as an argument, print model information. Flag --markdown prints details in Markdown for easy copy-pasting to GitHub issues. """ cli_info(model, markdown) @plac.annotations( input_dir=("directory with model data", "positional", None, str), output_dir=("output parent directory", "positional", None, str), force=("force overwriting of existing folder in output directory", "flag", "f", bool) ) def package(self, input_dir, output_dir, force=False): """ Generate Python package for model data, including meta and required installation files. A new directory will be created in the specified output directory, and model data will be copied over. """ cli_package(input_dir, output_dir, force) @plac.annotations( lang=("language", "positional", None, str), output_dir=("output directory", "positional", None, str), train_data=("training data", "positional", None, str), dev_data=("development data", "positional", None, str), n_iter=("number of iterations", "flag", "n", int), tagger=("train tagger", "flag", "t", bool), parser=("train parser", "flag", "p", bool), ner=("train NER", "flag", "n", bool) ) def train(self, lang, output_dir, train_data, dev_data, n_iter=15, tagger=True, parser=True, ner=True): """Train a model.""" cli_train(output_dir, train_data, dev_data, tagger, parser, ner) @plac.annotations( config=("config", "positional", None, str), ) def train_config(self, config): """Train a model from config file.""" cli_train_config(config) def __missing__(self, name): print("\n Command %r does not exist\n" % name) if __name__ == '__main__': import plac import sys cli = CLI() sys.argv[0] = 'spacy' plac.Interpreter.call(CLI)