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Create app.py
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app.py
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import gradio as gr
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from transformers import BertTokenizer, TFBertForSequenceClassification
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import tensorflow as tf
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# Load tokenizer
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tokenizer = BertTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
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# Load model
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model = TFBertForSequenceClassification.from_pretrained('new_emdedding trial')
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def check_sarcasm(sentence):
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tf_batch = tokenizer(sentence, max_length=128, padding=True, truncation=True, return_tensors='tf')
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tf_outputs = model(tf_batch.input_ids, tf_batch.token_type_ids)
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tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
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pred_label = tf.argmax(tf_predictions, axis=1)
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if pred_label == 1:
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return "Sarcastic"
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else:
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return "Not sarcastic"
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# Create a Gradio interface
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iface = gr.Interface(
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fn=check_sarcasm,
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inputs="text",
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outputs="text",
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title="Sarcasm Detection",
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description="Enter a headline and check if it's sarcastic."
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)
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# Launch the interface
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iface.launch(share=True)
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