Reformat code with black

gptc/__main__.py

@@ -5,20 +5,24 @@ import sys
 import gptc
 
 parser = argparse.ArgumentParser(description="General Purpose Text Classifier")
-parser.add_argument('model', help='model to use')
+parser.add_argument("model", help="model to use")
-parser.add_argument('-c', '--compile', help='compile raw model model to outfile', metavar='outfile')
+parser.add_argument(
-parser.add_argument('-j', '--confidence', help='output confidence dict in json', action='store_true')
+    "-c", "--compile", help="compile raw model model to outfile", metavar="outfile"
+)
+parser.add_argument(
+    "-j", "--confidence", help="output confidence dict in json", action="store_true"
+)
 args = parser.parse_args()
 
-with open(args.model, 'r') as f:
+with open(args.model, "r") as f:
     raw_model = json.load(f)
 if args.compile:
-    with open(args.compile, 'w+') as f:
+    with open(args.compile, "w+") as f:
         json.dump(gptc.compile(raw_model), f)
 else:
     classifier = gptc.Classifier(raw_model)
     if sys.stdin.isatty():
-        text = input('Text to analyse: ')
+        text = input("Text to analyse: ")
     else:
         text = sys.stdin.read()
     if args.confidence:

gptc/classifier.py

@@ -1,6 +1,7 @@
 import gptc.tokenizer, gptc.compiler, gptc.exceptions
 import warnings
 
+
 class Classifier:
     """A text classifier.
 
@@ -18,7 +19,7 @@ class Classifier:
 
     def __init__(self, model):
         try:
-            model_version = model['__version__']
+            model_version = model["__version__"]
         except:
             model_version = 1
 
@@ -27,11 +28,15 @@ class Classifier:
         else:
             # The model is an unsupported version
             try:
-                raw_model = model['__raw__']
+                raw_model = model["__raw__"]
             except:
-                raise gptc.exceptions.UnsupportedModelError('this model is unsupported and does not contain a raw model for recompiling')
+                raise gptc.exceptions.UnsupportedModelError(
+                    "this model is unsupported and does not contain a raw model for recompiling"
+                )
 
-            warnings.warn("model needed to be recompiled on-the-fly; please re-compile it and use the new compiled model in the future")
+            warnings.warn(
+                "model needed to be recompiled on-the-fly; please re-compile it and use the new compiled model in the future"
+            )
             self.model = gptc.compiler.compile(raw_model)
 
     def confidence(self, text):
@@ -63,9 +68,12 @@ class Classifier:
                         probs[category] = value
             except KeyError:
                 pass
-        probs = {model['__names__'][category]: value/65535 for category, value in probs.items()}
+        probs = {
+            model["__names__"][category]: value / 65535
+            for category, value in probs.items()
+        }
         total = sum(probs.values())
-        probs = {category: value/total for category, value in probs.items()}
+        probs = {category: value / total for category, value in probs.items()}
         return probs
 
     def classify(self, text):

gptc/compiler.py

@@ -1,5 +1,6 @@
 import gptc.tokenizer
 
+
 def compile(raw_model):
     """Compile a raw model.
 
@@ -18,8 +19,8 @@ def compile(raw_model):
     categories = {}
 
     for portion in raw_model:
-        text = gptc.tokenizer.tokenize(portion['text'])
+        text = gptc.tokenizer.tokenize(portion["text"])
-        category = portion['category']
+        category = portion["category"]
         try:
             categories[category] += text
         except KeyError:
@@ -36,27 +37,27 @@ def compile(raw_model):
         categories_by_count[category] = {}
         for word in text:
             try:
-                categories_by_count[category][word] += 1/len(categories[category])
+                categories_by_count[category][word] += 1 / len(categories[category])
             except KeyError:
-                categories_by_count[category][word] = 1/len(categories[category])
+                categories_by_count[category][word] = 1 / len(categories[category])
     word_weights = {}
     for category, words in categories_by_count.items():
         for word, value in words.items():
             try:
                 word_weights[word][category] = value
             except KeyError:
-                word_weights[word] = {category:value}
+                word_weights[word] = {category: value}
 
     model = {}
     for word, weights in word_weights.items():
         total = sum(weights.values())
         model[word] = []
         for category in names:
-            model[word].append(round((weights.get(category, 0)/total)*65535))
+            model[word].append(round((weights.get(category, 0) / total) * 65535))
 
-    model['__names__'] = names
+    model["__names__"] = names
 
-    model['__version__'] = 3
+    model["__version__"] = 3
-    model['__raw__'] = raw_model
+    model["__raw__"] = raw_model
 
     return model

gptc/exceptions.py

@@ -1,8 +1,10 @@
 class GPTCError(BaseException):
     pass
 
+
 class ModelError(GPTCError):
     pass
 
+
 class UnsupportedModelError(ModelError):
     pass

utils/pack.py

@@ -3,7 +3,7 @@ import os
 import json
 
 if len(sys.argv) != 2:
-    print('usage: pack.py <path>', file=sys.stderr)
+    print("usage: pack.py <path>", file=sys.stderr)
     exit(1)
 
 paths = os.listdir(sys.argv[1])
@@ -24,6 +24,6 @@ for path in paths:
 raw_model = []
 
 for category, cat_texts in texts.items():
-    raw_model += [{'category': category, 'text': i} for i in cat_texts]
+    raw_model += [{"category": category, "text": i} for i in cat_texts]
 
 print(json.dumps(raw_model))