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import gradio as gr | |
from transformers import BertTokenizer, TFBertForSequenceClassification | |
import tensorflow as tf | |
# Load tokenizer | |
tokenizer = BertTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1") | |
# Load model | |
model = TFBertForSequenceClassification.from_pretrained('lt12200028/Sarcasm-Detection-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( | |
fn=check_sarcasm, | |
inputs="text", | |
outputs="text", | |
title="Sarcasm Detection", | |
description="Enter a headline and check if it's sarcastic." | |
) | |
# Launch the interface | |
iface.launch(share=True) |