Update app.py

app.py

@@ -10,21 +10,21 @@ Original file is located at
 import gradio as gr
 from transformers import BertTokenizer, TFBertForSequenceClassification
 import tensorflow as tf
-from transformers import pipeline
+#from transformers import pipeline
 
 # Load tokenizer
-#tokenizer = BertTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
+tokenizer = BertTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
 
 # Load model
-#model = TFBertForSequenceClassification.from_pretrained('https://huggingface.co/spaces/Kleo/Sarcasm/tree/main')
+model = TFBertForSequenceClassification.from_pretrained('https://huggingface.co/spaces/Kleo/Sarcasm/tree/main')
 
-pipeline= pipeline(task="text-classification", model="https://huggingface.co/spaces/Kleo/Sarcasm/blob/main/tf_model.h5")
+#pipeline= pipeline(task="text-classification", model="https://huggingface.co/spaces/Kleo/Sarcasm/blob/main/tf_model.h5")
 def check_sarcasm(sentence):
-    #tf_batch = tokenizer(sentence, max_length=128, padding=True, truncation=True, return_tensors='tf')
-    #tf_outputs = model(tf_batch.input_ids, tf_batch.token_type_ids)
-    #tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
-    #pred_label = tf.argmax(tf_predictions, axis=1)
-    pred_label=pipeline(sentence)
+    tf_batch = tokenizer(sentence, max_length=128, padding=True, truncation=True, return_tensors='tf')
+    tf_outputs = model(tf_batch.input_ids, tf_batch.token_type_ids)
+    tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
+    pred_label = tf.argmax(tf_predictions, axis=1)
+    #pred_label=pipeline(sentence)
 
     if pred_label == 1:
         return "Sarcastic"