app.py

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('sarcasm_detection_gr/biGRU.hdf5')

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)