Refactor CLI

spacy/__main__.py

@@ -13,18 +13,12 @@ from spacy.cli import model as cli_model
 from spacy.cli import convert as cli_convert
 
 
-class CLI(object):
-    """
-    Command-line interface for spaCy
-    """
-    commands = ('download', 'link', 'info', 'package', 'train', 'model', 'convert')
-
 @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, direct=False):
+def download(model, 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
@@ -38,7 +32,7 @@ class CLI(object):
     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):
+def link(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
@@ -51,7 +45,7 @@ class CLI(object):
     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):
+def info(model=None, markdown=False):
     """
     Print info about spaCy installation. If a model shortcut link is
     speficied as an argument, print model information. Flag --markdown
@@ -66,7 +60,7 @@ class CLI(object):
     meta=("path to meta.json", "option", "m", str),
     force=("force overwriting of existing folder in output directory", "flag", "f", bool)
 )
-    def package(self, input_dir, output_dir, meta=None, force=False):
+def package(input_dir, output_dir, meta=None, force=False):
     """
     Generate Python package for model data, including meta and required
     installation files. A new directory will be created in the specified
@@ -88,7 +82,7 @@ class CLI(object):
     no_parser=("Don't train parser", "flag", "P", bool),
     no_entities=("Don't train NER", "flag", "N", bool)
 )
-    def train(self, lang, output_dir, train_data, dev_data=None, n_iter=15,
+def train(lang, output_dir, train_data, dev_data=None, n_iter=15,
           nsents=0, parser_L1=0.0, use_gpu=False,
           no_tagger=False, no_parser=False, no_entities=False):
     """
@@ -98,36 +92,32 @@ class CLI(object):
     cli_train(lang, output_dir, train_data, dev_data, n_iter, nsents,
               use_gpu, no_tagger, no_parser, no_entities, parser_L1)
 
-    @plac.annotations(
-        lang=("model language", "positional", None, str),
-        model_dir=("output directory to store model in", "positional", None, str),
-        freqs_data=("tab-separated frequencies file", "positional", None, str),
-        clusters_data=("Brown clusters file", "positional", None, str),
-        vectors_data=("word vectors file", "positional", None, str)
-    )
-    def model(self, lang, model_dir, freqs_data, clusters_data=None, vectors_data=None):
-        """
-        Initialize a new model and its data directory.
-        """
-        cli_model(lang, model_dir, freqs_data, clusters_data, vectors_data)
-
 @plac.annotations(
     input_file=("input file", "positional", None, str),
     output_dir=("output directory for converted file", "positional", None, str),
     n_sents=("Number of sentences per doc", "option", "n", float),
     morphology=("Enable appending morphology to tags", "flag", "m", bool)
 )
-    def convert(self, input_file, output_dir, n_sents=10, morphology=False):
+def convert(input_file, output_dir, n_sents=10, morphology=False):
     """
     Convert files into JSON format for use with train command and other
     experiment management functions.
     """
     cli_convert(input_file, output_dir, n_sents, morphology)
 
+@plac.annotations(
+    lang=("model language", "positional", None, str),
+    model_dir=("output directory to store model in", "positional", None, str),
+    freqs_data=("tab-separated frequencies file", "positional", None, str),
+    clusters_data=("Brown clusters file", "positional", None, str),
+    vectors_data=("word vectors file", "positional", None, str)
+)
+def model(lang, model_dir, freqs_data, clusters_data=None, vectors_data=None):
+    """
+    Initialize a new model and its data directory.
+    """
+    cli_model(lang, model_dir, freqs_data, clusters_data, vectors_data)
 
-    def __missing__(self, name):
-        print("\n   Command %r does not exist."
-              "\n   Use the --help flag for a list of available commands.\n" % name)
 
 @plac.annotations(
     lang=("model language", "positional", None, str),
@@ -147,6 +137,7 @@ def train(self, lang, output_dir, train_data, dev_data=None, n_iter=15,
     """
     Train a model. Expects data in spaCy's JSON format.
     """
+    print(train_data, dev_data)
     nsents = nsents or None
     cli_train(lang, output_dir, train_data, dev_data, n_iter, nsents,
               use_gpu, no_tagger, no_parser, no_entities)
@@ -157,3 +148,5 @@ if __name__ == '__main__':
     import sys
     if sys.argv[1] == 'train':
         plac.call(train)
+    if sys.argv[1] == 'convert':
+        plac.call(convert)