Delete application.py

application.py

@@ -1,120 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Application.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/148du8431_JkTaH-totdocC2aUXzOWimL
-"""
-
-from google.colab import drive
-drive.mount('/content/drive')
-
-pip install transformers
-
-pip install gradio
-
-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('/content/drive/MyDrive/Mini_Project/new_emdedding trial')
-
-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",
-    description="Enter a headline and check if it's sarcastic."
-)
-
-# Launch the interface
-iface.launch(share=True)
-
-from transformers import pipeline
-import gradio as gr
-
-model = pipeline("Sarcasm Detection" , model="/content/drive/MyDrive/Mini_Project/new_emdedding trial/tf_model.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)
-
-    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()
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
-model = AutoModelForSequenceClassification.from_pretrained("/content/drive/MyDrive/Mini_Project/new_emdedding trial",from_tf=True)
-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)
-