Metadata Writer: Add metadata writer for Text Classifier.

PiperOrigin-RevId: 486844428

mediapipe/tasks/python/metadata/metadata_writers/BUILD

@@ -43,3 +43,9 @@ py_library(
     srcs = ["image_classifier.py"],
     deps = [":metadata_writer"],
 )
+
+py_library(
+    name = "text_classifier",
+    srcs = ["text_classifier.py"],
+    deps = [":metadata_writer"],
+)

mediapipe/tasks/python/metadata/metadata_writers/image_classifier.py

@@ -62,10 +62,10 @@ class MetadataWriter(metadata_writer.MetadataWriterBase):
         https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
 
     Returns:
-      An MetadataWrite object.
+      A MetadataWriter object.
     """
     writer = metadata_writer.MetadataWriter(model_buffer)
-    writer.add_genernal_info(_MODEL_NAME, _MODEL_DESCRIPTION)
+    writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION)
     writer.add_image_input(input_norm_mean, input_norm_std)
     writer.add_classification_output(labels, score_calibration)
     return cls(writer)

mediapipe/tasks/python/metadata/metadata_writers/metadata_info.py

@@ -228,6 +228,45 @@ class ScoreThresholdingMd:
     return score_thresholding
 
 
+class RegexTokenizerMd:
+  """A container for the Regex tokenizer [1] metadata information.
+
+  [1]:
+    https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500
+  """
+
+  def __init__(self, delim_regex_pattern: str, vocab_file_path: str):
+    """Initializes a RegexTokenizerMd object.
+
+    Args:
+      delim_regex_pattern: the regular expression to segment strings and create
+        tokens.
+      vocab_file_path: path to the vocabulary file.
+    """
+    self._delim_regex_pattern = delim_regex_pattern
+    self._vocab_file_path = vocab_file_path
+
+  def create_metadata(self) -> _metadata_fb.ProcessUnitT:
+    """Creates the Regex tokenizer metadata based on the information.
+
+    Returns:
+      A Flatbuffers Python object of the Regex tokenizer metadata.
+    """
+    vocab = _metadata_fb.AssociatedFileT()
+    vocab.name = self._vocab_file_path
+    vocab.description = _VOCAB_FILE_DESCRIPTION
+    vocab.type = _metadata_fb.AssociatedFileType.VOCABULARY
+
+    # Create the RegexTokenizer.
+    tokenizer = _metadata_fb.ProcessUnitT()
+    tokenizer.optionsType = (
+        _metadata_fb.ProcessUnitOptions.RegexTokenizerOptions)
+    tokenizer.options = _metadata_fb.RegexTokenizerOptionsT()
+    tokenizer.options.delimRegexPattern = self._delim_regex_pattern
+    tokenizer.options.vocabFile = [vocab]
+    return tokenizer
+
+
 class TensorMd:
   """A container for common tensor metadata information.
 
@@ -397,6 +436,56 @@ class InputImageTensorMd(TensorMd):
     return tensor_metadata
 
 
+class InputTextTensorMd(TensorMd):
+  """A container for the input text tensor metadata information.
+
+  Attributes:
+    tokenizer_md: information of the tokenizer in the input text tensor, if any.
+  """
+
+  def __init__(self,
+               name: Optional[str] = None,
+               description: Optional[str] = None,
+               tokenizer_md: Optional[RegexTokenizerMd] = None):
+    """Initializes the instance of InputTextTensorMd.
+
+    Args:
+      name: name of the tensor.
+      description: description of what the tensor is.
+      tokenizer_md: information of the tokenizer in the input text tensor, if
+        any. Only `RegexTokenizer` [1] is currenly supported. If the tokenizer
+        is `BertTokenizer` [2] or `SentencePieceTokenizer` [3], refer to
+        `BertInputTensorsMd` class.
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500
+      [2]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477
+      [3]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485
+    """
+    super().__init__(name, description)
+    self.tokenizer_md = tokenizer_md
+
+  def create_metadata(self) -> _metadata_fb.TensorMetadataT:
+    """Creates the input text metadata based on the information.
+
+    Returns:
+      A Flatbuffers Python object of the input text metadata.
+
+    Raises:
+      ValueError: if the type of tokenizer_md is unsupported.
+    """
+    if not isinstance(self.tokenizer_md, (type(None), RegexTokenizerMd)):
+      raise ValueError(
+          f"The type of tokenizer_options, {type(self.tokenizer_md)}, is "
+          f"unsupported")
+
+    tensor_metadata = super().create_metadata()
+    if self.tokenizer_md:
+      tensor_metadata.processUnits = [self.tokenizer_md.create_metadata()]
+    return tensor_metadata
+
+
 class ClassificationTensorMd(TensorMd):
   """A container for the classification tensor metadata information.
 

mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py

@@ -29,6 +29,9 @@ from mediapipe.tasks.python.metadata.metadata_writers import writer_utils
 
 _INPUT_IMAGE_NAME = 'image'
 _INPUT_IMAGE_DESCRIPTION = 'Input image to be processed.'
+_INPUT_REGEX_TEXT_NAME = 'input_text'
+_INPUT_REGEX_TEXT_DESCRIPTION = ('Embedding vectors representing the input '
+                                 'text to be processed.')
 _OUTPUT_CLASSIFICATION_NAME = 'score'
 _OUTPUT_CLASSIFICATION_DESCRIPTION = 'Score of the labels respectively.'
 
@@ -82,6 +85,22 @@ class ScoreThresholding:
   global_score_threshold: float
 
 
+@dataclasses.dataclass
+class RegexTokenizer:
+  """Parameters of the Regex tokenizer [1] metadata information.
+
+  [1]:
+    https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500
+
+  Attributes:
+    delim_regex_pattern: the regular expression to segment strings and create
+      tokens.
+    vocab_file_path: path to the vocabulary file.
+  """
+  delim_regex_pattern: str
+  vocab_file_path: str
+
+
 class Labels(object):
   """Simple container holding classification labels of a particular tensor.
 
@@ -355,11 +374,11 @@ class MetadataWriter(object):
     if os.path.exists(self._temp_folder.name):
       self._temp_folder.cleanup()
 
-  def add_genernal_info(
+  def add_general_info(
       self,
       model_name: str,
       model_description: Optional[str] = None) -> 'MetadataWriter':
-    """Adds a genernal info metadata for the general metadata informantion."""
+    """Adds a general info metadata for the general metadata informantion."""
     # Will overwrite the previous `self._general_md` if exists.
     self._general_md = metadata_info.GeneralMd(
         name=model_name, description=model_description)
@@ -415,6 +434,34 @@ class MetadataWriter(object):
     self._input_mds.append(input_md)
     return self
 
+  def add_regex_text_input(
+      self,
+      regex_tokenizer: RegexTokenizer,
+      name: str = _INPUT_REGEX_TEXT_NAME,
+      description: str = _INPUT_REGEX_TEXT_DESCRIPTION) -> 'MetadataWriter':
+    """Adds an input text metadata for the text input with regex tokenizer.
+
+    Args:
+      regex_tokenizer: information of the regex tokenizer [1] used to process
+        the input string.
+      name: Name of the input tensor.
+      description: Description of the input tensor.
+
+    Returns:
+      The MetaWriter instance, can be used for chained operation.
+
+    [1]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500
+    """
+    tokenizer_md = metadata_info.RegexTokenizerMd(
+        delim_regex_pattern=regex_tokenizer.delim_regex_pattern,
+        vocab_file_path=regex_tokenizer.vocab_file_path)
+    input_md = metadata_info.InputTextTensorMd(
+        name=name, description=description, tokenizer_md=tokenizer_md)
+    self._input_mds.append(input_md)
+    self._associated_files.append(regex_tokenizer.vocab_file_path)
+    return self
+
   def add_classification_output(
       self,
       labels: Optional[Labels] = None,

mediapipe/tasks/python/metadata/metadata_writers/text_classifier.py

@@ -0,0 +1,64 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Writes metadata and label file to the Text classifier models."""
+
+from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
+
+_MODEL_NAME = "TextClassifier"
+_MODEL_DESCRIPTION = ("Classify the input text into a set of known categories.")
+
+
+class MetadataWriter(metadata_writer.MetadataWriterBase):
+  """MetadataWriter to write the metadata into the text classifier."""
+
+  @classmethod
+  def create_for_regex_model(
+      cls, model_buffer: bytearray,
+      regex_tokenizer: metadata_writer.RegexTokenizer,
+      labels: metadata_writer.Labels) -> "MetadataWriter":
+    """Creates MetadataWriter for TFLite model with regex tokentizer.
+
+    The parameters required in this method are mandatory when using MediaPipe
+    Tasks.
+
+    Note that only the output TFLite is used for deployment. The output JSON
+    content is used to interpret the metadata content.
+
+    Args:
+      model_buffer: A valid flatbuffer loaded from the TFLite model file.
+      regex_tokenizer: information of the regex tokenizer [1] used to process
+        the input string. If the tokenizer is `BertTokenizer` [2] or
+        `SentencePieceTokenizer` [3], please refer to
+        `create_for_bert_model`.
+      labels: an instance of Labels helper class used in the output
+        classification tensor [4].
+
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500
+      [2]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477
+      [3]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485
+      [4]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
+
+    Returns:
+      A MetadataWriter object.
+    """
+    writer = metadata_writer.MetadataWriter(model_buffer)
+    writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION)
+    writer.add_regex_text_input(regex_tokenizer)
+    writer.add_classification_output(labels)
+    return cls(writer)

mediapipe/tasks/python/test/metadata/metadata_writers/BUILD

@@ -53,3 +53,17 @@ py_test(
         "//mediapipe/tasks/python/test:test_utils",
     ],
 )
+
+py_test(
+    name = "text_classifier_test",
+    srcs = ["text_classifier_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/metadata:data_files",
+        "//mediapipe/tasks/testdata/metadata:model_files",
+    ],
+    deps = [
+        "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer",
+        "//mediapipe/tasks/python/metadata/metadata_writers:text_classifier",
+        "//mediapipe/tasks/python/test:test_utils",
+    ],
+)

mediapipe/tasks/python/test/metadata/metadata_writers/metadata_info_test.py

@@ -191,6 +191,43 @@ class InputImageTensorMdTest(parameterized.TestCase):
         f"{len(norm_mean)} and {len(norm_std)}", str(error.exception))
 
 
+class InputTextTensorMdTest(absltest.TestCase):
+
+  _NAME = "input text"
+  _DESCRIPTION = "The input string."
+  _VOCAB_FILE = "vocab.txt"
+  _DELIM_REGEX_PATTERN = r"[^\w\']+"
+  _EXPECTED_TENSOR_JSON = test_utils.get_test_data_path(
+      os.path.join(_TEST_DATA_DIR, "input_text_tensor_meta.json"))
+  _EXPECTED_TENSOR_DEFAULT_JSON = test_utils.get_test_data_path(
+      os.path.join(_TEST_DATA_DIR, "input_text_tensor_default_meta.json"))
+
+  def test_create_metadata_should_succeed(self):
+    regex_tokenizer_md = metadata_info.RegexTokenizerMd(
+        self._DELIM_REGEX_PATTERN, self._VOCAB_FILE)
+
+    text_tensor_md = metadata_info.InputTextTensorMd(self._NAME,
+                                                     self._DESCRIPTION,
+                                                     regex_tokenizer_md)
+
+    metadata_json = _metadata.convert_to_json(
+        _create_dummy_model_metadata_with_tensor(
+            text_tensor_md.create_metadata()))
+    with open(self._EXPECTED_TENSOR_JSON, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+  def test_create_metadata_by_default_should_succeed(self):
+    text_tensor_md = metadata_info.InputTextTensorMd()
+
+    metadata_json = _metadata.convert_to_json(
+        _create_dummy_model_metadata_with_tensor(
+            text_tensor_md.create_metadata()))
+    with open(self._EXPECTED_TENSOR_DEFAULT_JSON, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+
 class ClassificationTensorMdTest(parameterized.TestCase):
 
   _NAME = "probability"

mediapipe/tasks/python/test/metadata/metadata_writers/metadata_writer_test.py

@@ -113,7 +113,7 @@ class MetadataWriterForTaskTest(absltest.TestCase):
   def test_initialize_and_populate(self):
     writer = metadata_writer.MetadataWriter.create(
         self.image_classifier_model_buffer)
-    writer.add_genernal_info(
+    writer.add_general_info(
         model_name='my_image_model', model_description='my_description')
     tflite_model, metadata_json = writer.populate()
     self.assertLen(tflite_model, 1882986)
@@ -142,7 +142,7 @@ class MetadataWriterForTaskTest(absltest.TestCase):
   def test_add_feature_input_output(self):
     writer = metadata_writer.MetadataWriter.create(
         self.image_classifier_model_buffer)
-    writer.add_genernal_info(
+    writer.add_general_info(
         model_name='my_model', model_description='my_description')
     writer.add_feature_input(
         name='input_tesnor', description='a feature input tensor')
@@ -191,7 +191,7 @@ class MetadataWriterForTaskTest(absltest.TestCase):
   def test_image_classifier(self):
     writer = metadata_writer.MetadataWriter.create(
         self.image_classifier_model_buffer)
-    writer.add_genernal_info(
+    writer.add_general_info(
         model_name='image_classifier',
         model_description='Imagenet classification model')
     writer.add_image_input(
@@ -282,7 +282,7 @@ class MetadataWriterForTaskTest(absltest.TestCase):
 
   def test_image_classifier_with_locale_and_score_calibration(self):
     writer = metadata_writer.MetadataWriter(self.image_classifier_model_buffer)
-    writer.add_genernal_info(
+    writer.add_general_info(
         model_name='image_classifier',
         model_description='Classify the input image.')
     writer.add_image_input(

mediapipe/tasks/python/test/metadata/metadata_writers/text_classifier_test.py

@@ -0,0 +1,51 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for metadata_writer.text_classifier."""
+
+from absl.testing import absltest
+
+from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
+from mediapipe.tasks.python.metadata.metadata_writers import text_classifier
+from mediapipe.tasks.python.test import test_utils
+
+_TEST_DIR = "mediapipe/tasks/testdata/metadata/"
+_MODEL = test_utils.get_test_data_path(_TEST_DIR + "movie_review.tflite")
+_LABEL_FILE = test_utils.get_test_data_path(_TEST_DIR +
+                                            "movie_review_labels.txt")
+_VOCAB_FILE = test_utils.get_test_data_path(_TEST_DIR + "regex_vocab.txt")
+_DELIM_REGEX_PATTERN = r"[^\w\']+"
+_JSON_FILE = test_utils.get_test_data_path("movie_review.json")
+
+
+class TextClassifierTest(absltest.TestCase):
+
+  def test_write_metadata(self,):
+    with open(_MODEL, "rb") as f:
+      model_buffer = f.read()
+    writer = text_classifier.MetadataWriter.create_for_regex_model(
+        model_buffer,
+        regex_tokenizer=metadata_writer.RegexTokenizer(
+            delim_regex_pattern=_DELIM_REGEX_PATTERN,
+            vocab_file_path=_VOCAB_FILE),
+        labels=metadata_writer.Labels().add_from_file(_LABEL_FILE))
+    _, metadata_json = writer.populate()
+
+    with open(_JSON_FILE, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+
+if __name__ == "__main__":
+  absltest.main()

mediapipe/tasks/testdata/metadata/BUILD

@@ -31,6 +31,7 @@ mediapipe_files(srcs = [
     "mobilenet_v2_1.0_224_quant.tflite",
     "mobilenet_v2_1.0_224_quant_without_metadata.tflite",
     "mobilenet_v2_1.0_224_without_metadata.tflite",
+    "movie_review.tflite",
 ])
 
 exports_files([
@@ -54,6 +55,11 @@ exports_files([
     "labels.txt",
     "mobilenet_v2_1.0_224.json",
     "mobilenet_v2_1.0_224_quant.json",
+    "input_text_tensor_meta.json",
+    "input_text_tensor_default_meta.json",
+    "movie_review_labels.txt",
+    "regex_vocab.txt",
+    "movie_review.json",
 ])
 
 filegroup(
@@ -67,6 +73,7 @@ filegroup(
         "mobilenet_v2_1.0_224_quant.tflite",
         "mobilenet_v2_1.0_224_quant_without_metadata.tflite",
         "mobilenet_v2_1.0_224_without_metadata.tflite",
+        "movie_review.tflite",
     ],
 )
 
@@ -86,9 +93,14 @@ filegroup(
         "input_image_tensor_float_meta.json",
         "input_image_tensor_uint8_meta.json",
         "input_image_tensor_unsupported_meta.json",
+        "input_text_tensor_default_meta.json",
+        "input_text_tensor_meta.json",
         "labels.txt",
         "mobilenet_v2_1.0_224.json",
         "mobilenet_v2_1.0_224_quant.json",
+        "movie_review.json",
+        "movie_review_labels.txt",
+        "regex_vocab.txt",
         "score_calibration.txt",
         "score_calibration_file_meta.json",
         "score_calibration_tensor_meta.json",

mediapipe/tasks/testdata/metadata/input_text_tensor_default_meta.json

@@ -0,0 +1,17 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "stats": {
+          }
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/input_text_tensor_meta.json

@@ -0,0 +1,34 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "input text",
+          "description": "The input string.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "RegexTokenizerOptions",
+              "options": {
+                "delim_regex_pattern": "[^\\w\\']+",
+                "vocab_file": [
+                  {
+                    "name": "vocab.txt",
+                    "description": "Vocabulary file to convert natural language words to embedding vectors.",
+                    "type": "VOCABULARY"
+                  }
+                ]
+              }
+            }
+          ],
+          "stats": {
+          }
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/movie_review.json

@@ -0,0 +1,63 @@
+{
+  "name": "TextClassifier",
+  "description": "Classify the input text into a set of known categories.",
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "input_text",
+          "description": "Embedding vectors representing the input text to be processed.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "RegexTokenizerOptions",
+              "options": {
+                "delim_regex_pattern": "[^\\w\\']+",
+                "vocab_file": [
+                  {
+                    "name": "regex_vocab.txt",
+                    "description": "Vocabulary file to convert natural language words to embedding vectors.",
+                    "type": "VOCABULARY"
+                  }
+                ]
+              }
+            }
+          ],
+          "stats": {
+          }
+        }
+      ],
+      "output_tensor_metadata": [
+        {
+          "name": "score",
+          "description": "Score of the labels respectively.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS"
+            }
+          ]
+        }
+      ]
+    }
+  ],
+  "min_parser_version": "1.2.1"
+}

mediapipe/tasks/testdata/metadata/movie_review_labels.txt

@@ -0,0 +1,2 @@
+Negative
+Positive

third_party/external_files.bzl

@@ -346,6 +346,18 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_unsupported_meta.json?generation=1665422835757143"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_input_text_tensor_default_meta_json",
+        sha256 = "9723e59960b0e6ca60d120494c32e798b054ea6e5a441b359c84f759bd2b3a36",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/input_text_tensor_default_meta.json?generation=1667855382021347"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_input_text_tensor_meta_json",
+        sha256 = "c6782f676220e2cc89b70bacccb649fc848c18e33bedc449bf49f5d839b3cc6c",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/input_text_tensor_meta.json?generation=1667855384891533"],
+    )
+
     http_file(
         name = "com_google_mediapipe_iris_and_gaze_tflite",
         sha256 = "b6dcb860a92a3c7264a8e50786f46cecb529672cdafc17d39c78931257da661d",
@@ -390,8 +402,8 @@ def external_files():
 
     http_file(
         name = "com_google_mediapipe_labels_txt",
-        sha256 = "536feacc519de3d418de26b2effb4d75694a8c4c0063e36499a46fa8061e2da9",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/labels.txt?generation=1665988394538324"],
+        sha256 = "4b9b26392f765e7a872372131cd4cee8ad7c02e496b5a1228279619b138c4b7a",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/labels.txt?generation=1667855388142641"],
     )
 
     http_file(
@@ -538,6 +550,18 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/model_without_metadata.tflite?generation=1661875850966737"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_movie_review_json",
+        sha256 = "89ad347ad1cb7c587da144de6efbadec1d3e8ff0cd13e379dd16661a8186fbb5",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/movie_review.json?generation=1667855392734031"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_movie_review_tflite",
+        sha256 = "3935ee73b13d435327d05af4d6f37dc3c146e117e1c3d572ae4d2ae0f5f412fe",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/movie_review.tflite?generation=1667855395736217"],
+    )
+
     http_file(
         name = "com_google_mediapipe_mozart_square_jpg",
         sha256 = "4feb4dadc5d6f853ade57b8c9d4c9a1f5ececd6469616c8e505f9a14823392b6",