Deserialize from file

README.md

@@ -49,7 +49,7 @@ Write binary data representing the model to `file`.
 
 ### `gptc.deserialize(encoded_model)`
 
-Deserialize a `Model` from a `bytes` returned by `Model.serialize()`.
+Deserialize a `Model` from a file containing data from `Model.serialize()`.
 
 ### `Model.confidence(text, max_ngram_length)`
 

gptc/__main__.py

@@ -69,7 +69,7 @@ def main() -> None:
         gptc.compile(model, args.max_ngram_length, args.min_count).serialize(sys.stdout.buffer)
     elif args.subparser_name == "classify":
         with open(args.model, "rb") as f:
-            model = gptc.deserialize(f.read())
+            model = gptc.deserialize(f)
 
         if sys.stdin.isatty():
             text = input("Text to analyse: ")
@@ -85,7 +85,7 @@ def main() -> None:
             print(json.dumps(probabilities))
     elif args.subparser_name == "check":
         with open(args.model, "rb") as f:
-            model = gptc.deserialize(f.read())
+            model = gptc.deserialize(f)
         print(json.dumps(model.get(args.token)))
     else:
         print(json.dumps(gptc.pack(args.model, True)[0]))

gptc/model.py

@@ -104,14 +104,20 @@ class Model:
             )
 
 
-def deserialize(encoded_model: bytes) -> Model:
-    try:
-        prefix, config_json, encoded_weights = encoded_model.split(b"\n", 2)
-    except ValueError:
+def deserialize(encoded_model: BinaryIO) -> Model:
+    prefix = encoded_model.read(14)
+    if prefix != b"GPTC model v4\n":
         raise InvalidModelError()
 
-    if prefix != b"GPTC model v4":
-        raise InvalidModelError()
+    config_json = b""
+    while True:
+        byte = encoded_model.read(1)
+        if byte == b"\n":
+            break
+        elif byte == b"":
+            raise InvalidModelError()
+        else:
+            config_json += byte
 
     try:
         config = json.loads(config_json.decode("utf-8"))
@@ -131,20 +137,18 @@ def deserialize(encoded_model: bytes) -> Model:
 
     weight_code_length = 6 + 2 * len(names)
 
-    if len(encoded_weights) % weight_code_length != 0:
-        raise InvalidModelError()
+    weights: Dict[int : List[int]] = {}
 
-    weight_codes = [
-        encoded_weights[x : x + weight_code_length]
-        for x in range(0, len(encoded_weights), weight_code_length)
-    ]
+    while True:
+        code = encoded_model.read(weight_code_length)
+        if not code:
+            break
+        elif len(code) != weight_code_length:
+            raise InvalidModelError()
 
-    weights = {
-        int.from_bytes(code[:6], "big"): [
+        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)]
         ]
-        for code in weight_codes
-    }
 
     return Model(weights, names, max_ngram_length)