add Nakdimon.h5 to package data

Signed-off-by: Elazar Gershuni <elazarg@gmail.com>

examples/usage.py

@@ -1,9 +1,6 @@
 # pip install git+https://github.com/nakdimon/nakdimon.git
-# mkdir models
-# wget https://github.com/elazarg/nakdimon/raw/master/models/Nakdimon.h5
-# mv Nakdimon.h5 models/Nakdimon.h5
 
 from nakdimon import diacritize
 
-result = diacritize("שלום עולם!", "models/Nakdimon.h5")
+result = diacritize("שלום עולם!")
 print(result)

nakdimon/__init__.py

@@ -2,7 +2,7 @@
 import sys
 import os
 import logging
-
+from nakdimon.config import MAIN_MODEL
 
 def do_train(**kwargs) -> None:
     from nakdimon import train
@@ -48,7 +48,7 @@ def main() -> None:
 
     parser_train = subparsers.add_parser('train', help='train Nakdimon')
     parser_train.add_argument('--wandb', action='store_true', help='use wandb.', default=False)
-    parser_train.add_argument('--model', help='path to output model (.h5 file)', default='models/Full.h5', dest='model_path')
+    parser_train.add_argument('--model', help='path to output model (.h5 file)', default=MAIN_MODEL, dest='model_path')
     parser_train.add_argument('--ablation', help='ablation test', default=None, dest='ablation_name')
     parser_train.set_defaults(func=do_train)
 
@@ -60,7 +60,7 @@ def main() -> None:
     parser_test = subparsers.add_parser('run_test', help='diacritize a test set')
     parser_test.add_argument('--test_set', choices=available_tests, help='choose test set', default='tests/new')
     parser_test.add_argument('--system', choices=test_systems, help='diacritization system to use', default='Nakdimon')
-    parser_test.add_argument('--model', help='path to model (.h5 file)', default='models/Nakdimon.h5', dest='model_path')
+    parser_test.add_argument('--model', help='path to model (.h5 file)', default=MAIN_MODEL, dest='model_path')
     parser_test.add_argument('--skip-existing', action='store_true', help='skip existing files')
     parser_test.set_defaults(func=do_run_test)
 
@@ -114,6 +114,6 @@ def diacritize_main():
     sys.exit(0)
 
 
-def diacritize(text: str, model_path: str = 'models/Nakdimon.h5') -> str:
+def diacritize(text: str, model_path: str = MAIN_MODEL) -> str:
     import nakdimon.predict
     return nakdimon.predict.predict(text, model_path)

nakdimon/config.py

@@ -0,0 +1,4 @@
+from importlib.resources import files
+
+MODELS_DIR = 'models'
+MAIN_MODEL = files('nakdimon').joinpath('Nakdimon.h5')

nakdimon/external_apis.py

@@ -10,6 +10,7 @@
 
 from nakdimon.hebrew import Niqqud
 from nakdimon import hebrew
+from nakdimon.config import MAIN_MODEL
 
 
 class DottingError(RuntimeError):
@@ -184,7 +185,7 @@ def run_nakdimon(text: str) -> str:
     'Snopi': fetch_snopi,  # Too slow
     'Morfix': fetch_morfix,  # terms-of-use issue
     'Dicta': fetch_dicta,
-    'Nakdimon': make_nakdimon_no_server('models/Nakdimon.h5'),
+    'Nakdimon': make_nakdimon_no_server(MAIN_MODEL),
 }
 all_oov = set()
 

nakdimon/predict.py

@@ -1,17 +1,21 @@
 import logging
+import pathlib
 from functools import lru_cache
 
 import tensorflow as tf
 
 from nakdimon import utils, dataset, hebrew
-
+from nakdimon.config import MAIN_MODEL
 
 if tf.config.set_visible_devices([], 'GPU'):
     logging.warning('No GPU available.')
 
 
 @lru_cache()
-def load_cached_model(m):
+def load_cached_model(m: pathlib.Path | str) -> tf.Module:
+    if isinstance(m, str):
+        return load_cached_model(pathlib.Path(m))
+    assert isinstance(m, pathlib.Path)
     model = tf.keras.models.load_model(m, custom_objects={'loss': None})
     return model
 
@@ -31,13 +35,12 @@ def merge_unconditional(texts, tnss, nss, dss, sss):
     return res
 
 
-def predict(text: str, model_or_model_path: tf.Module|str = 'models/Nakdimon.h5', maxlen=10000) -> str:
-    if isinstance(model_or_model_path, str):
-        model = load_cached_model(model_or_model_path)
-    elif isinstance(model_or_model_path, tf.Module):
-        model = model_or_model_path
-    else:
+def predict(text: str, model_or_model_path: tf.Module | str = MAIN_MODEL, maxlen=10000) -> str:
+    if isinstance(model_or_model_path, (pathlib.Path, str)):
+        model_or_model_path = load_cached_model(model_or_model_path)
+    if not isinstance(model_or_model_path, tf.Module):
         raise TypeError(f'Expected str or tf.Module, got {type(model_or_model_path)}')
+    model = model_or_model_path
     data = dataset.Data.from_text(hebrew.iterate_dotted_text(text), maxlen)
     prediction = model.predict(data.normalized)
     [actual_niqqud, actual_dagesh, actual_sin] = [dataset.from_categorical(prediction[0]), dataset.from_categorical(prediction[1]), dataset.from_categorical(prediction[2])]

nakdimon/pretrain.py

@@ -9,9 +9,9 @@
 from nakdimon import hebrew
 from nakdimon.train import TrainingParams
 from nakdimon import metrics
+from nakdimon.config import MODELS_DIR
 
-
-pretrain_path = f'./models/wiki'
+pretrain_path = f'{MODELS_DIR}/wiki'
 model_name = pretrain_path + 'pretrain.h5'
 
 
@@ -138,7 +138,7 @@ def pretrain():
 def train_ablation(params):
     from train import train
     model = train(params)
-    model.save(f'./models/ablations/{params.name}.h5')
+    model.save(f'./{MODELS_DIR}/ablations/{params.name}.h5')
 
 
 if __name__ == '__main__':
@@ -152,7 +152,7 @@ def train_ablation(params):
         import ablations
         tf.config.set_visible_devices([], 'GPU')
         model_name = 'PretrainedModernOnly'
-        model = tf.keras.models.load_model(f'models/ablations/{model_name}.h5',
+        model = tf.keras.models.load_model(f'{MODELS_DIR}/ablations/{model_name}.h5',
                                            custom_objects={'loss': TrainingParams().loss})
         print(model_name, *metrics.metricwise_mean(ablations.calculate_metrics(model)).values(), sep=', ')
 

nakdimon/server.py

@@ -4,6 +4,7 @@
 import logging
 
 from nakdimon import predict
+from nakdimon.config import MAIN_MODEL
 
 app = flask.Flask(__name__)
 
@@ -26,9 +27,9 @@ def diacritize():
 
 
 def main():
-    logging.info("Loading models/Nakdimon.h5")
+    logging.info(f"Loading {MAIN_MODEL}")
     try:
-        predict.predict("שלום", 'models/Nakdimon.h5')
+        predict.predict("שלום")
         logging.info("Done loading.")
     except OSError:
         logging.warning("Could not load default model")

nakdimon/train.py

@@ -12,6 +12,7 @@
 from nakdimon.dataset import NIQQUD_SIZE, DAGESH_SIZE, SIN_SIZE, LETTERS_SIZE
 from nakdimon import schedulers
 from nakdimon import transformer
+from nakdimon.config import MODELS_DIR
 
 # assert tf.config.list_physical_devices('GPU')
 
@@ -270,7 +271,7 @@ def train(params: NakdimonParams, group, ablation=False, wandb_enabled=False):
 
 def train_ablation(params, group):
     model = train(params, group, ablation=True)
-    model.save(f'./models/ablations/{params.name}.h5')
+    model.save(f'./{MODELS_DIR}/ablations/{params.name}.h5')
 
 
 class Full(NakdimonParams):

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "nakdimon"
-version = "0.1.1"
+version = "0.1.2"
 authors = [
   { name="Elazar Gershuni", email="elazarg@gmail.com" },
 ]
@@ -28,6 +28,9 @@ include = ["nakdimon"]
 exclude = []  # exclude packages matching these glob patterns (empty by default)
 namespaces = false
 
+[tool.setuptools.package-data]
+nakdimon = ["Nakdimon.h5"]
+
 [project.urls]
 "Homepage" = "https://github.com/elazarg/nakdimon"
 "Bug Tracker" = "https://github.com/elazarg/nakdimon/issues"