# -*- coding: utf-8 -*-
"""Application.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/148du8431_JkTaH-totdocC2aUXzOWimL
"""


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('https://huggingface.co/spaces/Kleo/Sarcasm/tree/main')

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"

# Example usage
sentence = "Μεξικό: 25 νεκροί από την πτώση λεωφορείου στον γκρεμό"
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(
    fn=check_sarcasm,
    inputs="text",
    outputs="text",
    title="Sarcasm Detection",
    server_name="0.0.0.0",
    description="Enter a headline and check if it's sarcastic."
)

# Launch the interface
iface.launch()