Format code

gptc/__main__.py

@@ -9,10 +9,16 @@ import gptc
 parser = argparse.ArgumentParser(description="General Purpose Text Classifier")
 parser.add_argument("model", help="model to use")
 parser.add_argument(
-    "-c", "--compile", help="compile raw model model to outfile", metavar="outfile"
+    "-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"
+    "-j",
+    "--confidence",
+    help="output confidence dict in json",
+    action="store_true",
 )
 args = parser.parse_args()
 

gptc/classifier.py

@@ -21,7 +21,9 @@ class Classifier:
 
     def __init__(self, model):
         if model.get("__version__", 0) != 3:
-            raise gptc.exceptions.UnsupportedModelError(f"unsupported model version")
+            raise gptc.exceptions.UnsupportedModelError(
+                f"unsupported model version"
+            )
         self.model = model
 
     def confidence(self, text):
@@ -57,7 +59,8 @@ class Classifier:
             except KeyError:
                 pass
         probs = {
-            model["__names__"][category]: value for category, value in probs.items()
+            model["__names__"][category]: value
+            for category, value in probs.items()
         }
         total = sum(probs.values())
         probs = {category: value / total for category, value in probs.items()}

gptc/compiler.py

@@ -39,9 +39,13 @@ 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():
@@ -55,7 +59,9 @@ def compile(raw_model):
         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