From 97c4eef086564655f22716318a2f5d704afd89f9 Mon Sep 17 00:00:00 2001
From: Samuel Sloniker <sam@kj7rrv.com>
Date: Mon, 17 Apr 2023 21:35:38 -0700
Subject: [PATCH] Move deserialize to Model object

---
 gptc/__init__.py |  2 +-
 gptc/__main__.py |  4 +--
 gptc/model.py    | 82 ++++++++++++++++++++++++------------------------
 3 files changed, 44 insertions(+), 44 deletions(-)

diff --git a/gptc/__init__.py b/gptc/__init__.py
index eaee24c..7d84238 100644
--- a/gptc/__init__.py
+++ b/gptc/__init__.py
@@ -3,7 +3,7 @@
 """General-Purpose Text Classifier"""
 
 from gptc.pack import pack
-from gptc.model import Model, deserialize
+from gptc.model import Model
 from gptc.tokenizer import normalize
 from gptc.exceptions import (
     GPTCError,
diff --git a/gptc/__main__.py b/gptc/__main__.py
index f035be8..3c3f441 100644
--- a/gptc/__main__.py
+++ b/gptc/__main__.py
@@ -68,7 +68,7 @@ def main() -> None:
             ).serialize(output_file)
     elif args.subparser_name == "classify":
         with open(args.model, "rb") as model_file:
-            model = gptc.deserialize(model_file)
+            model = gptc.Model.deserialize(model_file)
 
         if sys.stdin.isatty():
             text = input("Text to analyse: ")
@@ -78,7 +78,7 @@ def main() -> None:
         print(json.dumps(model.confidence(text, args.max_ngram_length)))
     elif args.subparser_name == "check":
         with open(args.model, "rb") as model_file:
-            model = gptc.deserialize(model_file)
+            model = gptc.Model.deserialize(model_file)
         print(json.dumps(model.get(args.token)))
     else:
         print(json.dumps(gptc.pack(args.model, True)[0]))
diff --git a/gptc/model.py b/gptc/model.py
index b3813b1..f7c5dba 100644
--- a/gptc/model.py
+++ b/gptc/model.py
@@ -269,54 +269,54 @@ class Model:
         model = _get_weights(min_count, word_counts, category_lengths, names)
         return Model(model, names, max_ngram_length, hash_algorithm)
 
-
-def deserialize(encoded_model: BinaryIO) -> Model:
-    prefix = encoded_model.read(14)
-    if prefix != b"GPTC model v6\n":
-        raise InvalidModelError()
-
-    config_json = b""
-    while True:
-        byte = encoded_model.read(1)
-        if byte == b"\n":
-            break
-
-        if byte == b"":
+    @staticmethod
+    def deserialize(encoded_model: BinaryIO) -> "Model":
+        prefix = encoded_model.read(14)
+        if prefix != b"GPTC model v6\n":
             raise InvalidModelError()
 
-        config_json += byte
+        config_json = b""
+        while True:
+            byte = encoded_model.read(1)
+            if byte == b"\n":
+                break
 
-    try:
-        config = json.loads(config_json.decode("utf-8"))
-    except (UnicodeDecodeError, json.JSONDecodeError) as exc:
-        raise InvalidModelError() from exc
+            if byte == b"":
+                raise InvalidModelError()
 
-    try:
-        names = config["names"]
-        max_ngram_length = config["max_ngram_length"]
-        hash_algorithm = config["hash_algorithm"]
-    except KeyError as exc:
-        raise InvalidModelError() from exc
+            config_json += byte
 
-    if not (
-        isinstance(names, list) and isinstance(max_ngram_length, int)
-    ) or not all(isinstance(name, str) for name in names):
-        raise InvalidModelError()
+        try:
+            config = json.loads(config_json.decode("utf-8"))
+        except (UnicodeDecodeError, json.JSONDecodeError) as exc:
+            raise InvalidModelError() from exc
 
-    weight_code_length = 6 + 2 * len(names)
+        try:
+            names = config["names"]
+            max_ngram_length = config["max_ngram_length"]
+            hash_algorithm = config["hash_algorithm"]
+        except KeyError as exc:
+            raise InvalidModelError() from exc
 
-    weights: Dict[int, List[int]] = {}
-
-    while True:
-        code = encoded_model.read(weight_code_length)
-        if not code:
-            break
-        if len(code) != weight_code_length:
+        if not (
+            isinstance(names, list) and isinstance(max_ngram_length, int)
+        ) or not all(isinstance(name, str) for name in names):
             raise InvalidModelError()
 
-        weights[int.from_bytes(code[:6], "big")] = [
-            int.from_bytes(value, "big")
-            for value in [code[x : x + 2] for x in range(6, len(code), 2)]
-        ]
+        weight_code_length = 6 + 2 * len(names)
 
-    return Model(weights, names, max_ngram_length, hash_algorithm)
+        weights: Dict[int, List[int]] = {}
+
+        while True:
+            code = encoded_model.read(weight_code_length)
+            if not code:
+                break
+            if len(code) != weight_code_length:
+                raise InvalidModelError()
+
+            weights[int.from_bytes(code[:6], "big")] = [
+                int.from_bytes(value, "big")
+                for value in [code[x : x + 2] for x in range(6, len(code), 2)]
+            ]
+
+        return Model(weights, names, max_ngram_length, hash_algorithm)