def tokenize(corpus_name):
"""
POST /{corpus}/tokenize
GET /{corpus}/tokenize?s=...
Tokenize the given string for this corpus's language.
"""
corpus = get_corpus_or_404(corpus_name)
# Args... should be a file or strong
content = get_string_content()
return jsonify(tokens=corpus.tokenize(content, mid_line=False))
def cross_entropy(corpus_name):
"""
POST /{corpus}/xentropy/
Calculate the cross-entropy of the uploaded file with respect to the
corpus.
"""
corpus = get_corpus_or_404(corpus_name)
content = get_string_content()
tokens = corpus.tokenize(content)
return jsonify(cross_entropy=corpus.cross_entropy(tokens))
def cross_entropy(corpus_name):
"""
POST /{corpus}/xentropy/
Calculate the cross-entropy of the uploaded file with respect to the
corpus.
"""
corpus = get_corpus_or_404(corpus_name)
content = get_string_content()
tokens = corpus.tokenize(content)
return jsonify(cross_entropy=corpus.cross_entropy(tokens))
def tokenize(corpus_name):
"""
POST /{corpus}/tokenize
GET /{corpus}/tokenize?s=...
Tokenize the given string for this corpus's language.
"""
corpus = get_corpus_or_404(corpus_name)
# Args... should be a file or string
content = get_string_content()
return jsonify(tokens=corpus.tokenize(content, mid_line=False))
def train(corpus_name):
"""
POST /{corpus}/
Upload a file for training.
"""
corpus = get_corpus_or_404(corpus_name)
content = get_string_content()
tokens = corpus.tokenize(content)
# NOTE: train doesn't really have a useful return...
corpus.train(tokens)
return make_response(jsonify(tokens=len(tokens)), 202)
def train(corpus_name):
"""
POST /{corpus}/
Upload a file for training.
"""
corpus = get_corpus_or_404(corpus_name)
content = get_string_content()
tokens = corpus.tokenize(content)
# NOTE: train doesn't really have a useful return...
corpus.train(tokens)
return make_response(jsonify(tokens=len(tokens)), 202)
def predict(corpus_name, token_str=""):
"""
POST /{corpus}/predict/{tokens*}
POST /{corpus}/predict/f=?
Returns a number of suggestions for the given token prefix.
"""
corpus = get_corpus_or_404(corpus_name)
if token_str:
tokens = parse_tokens(token_str)
else:
tokens = corpus.tokenize(get_string_content())
# Predict returns a nice, JSONable dictionary, so just return that.
return jsonify(corpus.predict(tokens))
def predict(corpus_name, token_str=""):
"""
POST /{corpus}/predict/{tokens*}
POST /{corpus}/predict/f=?
Returns a number of suggestions for the given token prefix.
"""
corpus = get_corpus_or_404(corpus_name)
if token_str:
tokens = parse_tokens(token_str)
else:
tokens = corpus.tokenize(get_string_content())
# Predict returns a nice, JSONable dictionary, so just return that.
return jsonify(corpus.predict(tokens))