Update app.py

app.py

@@ -7,21 +7,24 @@ Original file is located at
     https://colab.research.google.com/drive/148du8431_JkTaH-totdocC2aUXzOWimL
 """
 
-
+import gradio as gr
 from transformers import BertTokenizer, TFBertForSequenceClassification
 import tensorflow as tf
+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="sequence-classification", model="https://huggingface.co/spaces/Kleo/Sarcasm/blob/main/fine-tuned-bert-gr.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)
+    #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"
@@ -33,18 +36,6 @@ sentence = "Μεξικό: 25 νεκροί από την πτώση λεωφορ
 result = check_sarcasm(sentence)
 print(result)
 
-import gradio as gr
-
-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)
-
-    if pred_label == 1:
-        return "Sarcastic"
-    else:
-        return "Not sarcastic"
 
 # Create a Gradio interface
 iface = gr.Interface(
@@ -53,7 +44,7 @@ iface = gr.Interface(
     outputs="text",
     title="Sarcasm Detection",
     server_name="0.0.0.0",
-    description="Enter a headline and check if it's sarcastic."
+    description="Enter a headline from the Greek news and check if it's sarcastic."
 )
 
 # Launch the interface