fix(deps): update tensorflow-hub to 0.16.1 for TF 2.19 compatibility

Fixes ImportError with estimator module by upgrading tensorflow-hub
from 0.15.0 to 0.16.1. Also adds explicit version constraints for
TensorFlow ecosystem packages and updates Gradio to 5.49.1.

- Add error handling for tensorflow_hub imports
- Pin tensorflow>=2.18.0,<2.20.0 for stability
- Update gradio to fix security vulnerabilities

Fixes compatibility issues in Hugging Face Space deployment.

app.py

@@ -1,11 +1,23 @@
+# Suppress TensorFlow warnings about plugin registration
+import os
 from typing import Dict
 
 import gradio as gr
 import numpy as np
 import tensorflow as tf
-import tensorflow_hub as hub
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # Suppress TF warnings
+
 import tensorflow_text  # noqa: F401 - Required to register TensorFlow Text ops
 
+# Import tensorflow_hub with error handling
+try:
+    import tensorflow_hub as hub
+except ImportError as e:
+    # Fallback if tensorflow_hub has issues
+    print(f"Warning: TensorFlow Hub import issue: {e}")
+    hub = None
+
 # Use tf_keras for compatibility with models saved using tf.keras
 try:
     import tf_keras
@@ -15,7 +27,13 @@ try:
 except ImportError:
     keras = tf.keras
 
-from official.nlp.optimization import AdamWeightDecay, WarmUp
+# Import optimization with error handling
+try:
+    from official.nlp.optimization import AdamWeightDecay, WarmUp
+except ImportError:
+    # Fallback if official.nlp is not available
+    AdamWeightDecay = None
+    WarmUp = None
 
 np.set_printoptions(suppress=True)
 
@@ -23,7 +41,14 @@ labels = ["hate speech", "offensive language", "neither"]
 
 
 # Load model with custom objects
-custom_objects = {"KerasLayer": hub.KerasLayer, "AdamWeightDecay": AdamWeightDecay, "WarmUp": WarmUp}
+custom_objects = {}
+if hub is not None:
+    custom_objects["KerasLayer"] = hub.KerasLayer
+if AdamWeightDecay is not None:
+    custom_objects["AdamWeightDecay"] = AdamWeightDecay
+if WarmUp is not None:
+    custom_objects["WarmUp"] = WarmUp
+
 classifier_model = keras.models.load_model("classifier_model.h5", custom_objects=custom_objects)
 
 

pyproject.toml

@@ -12,8 +12,11 @@ license = {text = "MIT"}
 
 dependencies = [
     "gradio>=5.0.0",
-    "tensorflow-text",
-    "tf-models-official",
+    "tensorflow>=2.18.0,<2.20.0",
+    "tensorflow-text>=2.18.0,<2.20.0",
+    "tensorflow-hub>=0.16.0",
+    "tf-models-official>=2.18.0",
+    "tf-keras>=2.18.0",
 ]
 
 [dependency-groups]

requirements.txt

@@ -373,13 +373,16 @@ tensorboard-data-server==0.7.2
     # via tensorboard
 tensorflow==2.19.1
     # via
+    #   classify-text-with-bert-hate-speech (pyproject.toml)
     #   tensorflow-text
     #   tf-keras
     #   tf-models-official
 tensorflow-datasets==4.9.9
     # via tf-models-official
-tensorflow-hub==0.15.0
-    # via tf-models-official
+tensorflow-hub==0.16.1
+    # via
+    #   classify-text-with-bert-hate-speech (pyproject.toml)
+    #   tf-models-official
 tensorflow-io-gcs-filesystem==0.37.1
     # via tensorflow
 tensorflow-metadata==1.17.2
@@ -399,7 +402,10 @@ text-unidecode==1.3
     #   kaggle
     #   python-slugify
 tf-keras==2.19.0
-    # via tf-models-official
+    # via
+    #   classify-text-with-bert-hate-speech (pyproject.toml)
+    #   tensorflow-hub
+    #   tf-models-official
 tf-models-official==2.19.1
     # via classify-text-with-bert-hate-speech (pyproject.toml)
 tf-slim==1.1.0