Create app.py

app.py

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+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+import tf_keras as keras
+import pandas as pd
+from tensorflow.keras.models import load_model
+from official.nlp.data import classifier_data_lib
+from official.nlp.tools import tokenization
+import joblib
+
+model = load_model('best_model.h5', custom_objects={'KerasLayer': hub.KerasLayer})
+
+
+vocab_file = model.resolved_object.vocab_file.asset_path.numpy()
+do_lower_case = model.resolved_object.do_lower_case.numpy()
+tokenizer = tokenization.FullTokenizer(vocab_file,do_lower_case)
+
+# Parameters
+max_seq_length = 128
+label_list = 424
+dummy_label = 100
+
+
+# Define a function to preprocess the new data
+def get_feature_new(text, max_seq_length, tokenizer, dummy_label):
+    example = classifier_data_lib.InputExample(guid=None,
+                                               text_a=text.numpy().decode('utf-8'),
+                                               text_b=None,
+                                               label=dummy_label)  # Use a valid dummy label
+    feature = classifier_data_lib.convert_single_example(0, example, label_list, max_seq_length, tokenizer)
+    return feature.input_ids, feature.input_mask, feature.segment_ids
+
+def get_feature_map_new(text):
+    input_ids, input_mask, segment_ids = tf.py_function(
+        lambda text: get_feature_new(text, max_seq_length, tokenizer, dummy_label),
+        inp=[text],
+        Tout=[tf.int32, tf.int32, tf.int32]
+    )
+    input_ids.set_shape([max_seq_length])
+    input_mask.set_shape([max_seq_length])
+    segment_ids.set_shape([max_seq_length])
+    
+    x = {'input_word_ids': input_ids,
+         'input_mask': input_mask,
+         'input_type_ids': segment_ids}
+    
+    return x
+
+def preprocess_new_data(texts):
+    dataset = tf.data.Dataset.from_tensor_slices((texts,))
+    dataset = dataset.map(get_feature_map_new,
+                          num_parallel_calls=tf.data.experimental.AUTOTUNE)
+    dataset = dataset.batch(32, drop_remainder=False)
+    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
+    
+    return dataset