Update app.py

app.py

@@ -2,29 +2,15 @@ import gradio as gr
 import nlpaug.augmenter.word as naw
 import nlpaug.augmenter.char as nac
 import nlpaug.augmenter.sentence as nas
-import os
-import nltk
-from nlpaug.util.file.download import DownloadUtil
-
-# Download necessary NLTK resources
-nltk.download('averaged_perceptron_tagger')
-nltk.download('wordnet')
-
-# Function to download Word2Vec model if not available
-def download_word2vec():
-    word2vec_path = "GoogleNews-vectors-negative300.bin"
-    if not os.path.exists(word2vec_path):
-        print("Downloading Word2Vec model...")
-        DownloadUtil.download_word2vec(dest_dir='.')
-    return word2vec_path
 
 # Function for NLP augmentation
 def augment_text(text, method):
     if method == "Synonym Replacement":
-        aug = naw.SynonymAug(aug_src="wordnet", aug_max=3)
+        # Use BERT model for synonym replacement (contextual embeddings)
+        aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action='substitute')
     elif method == "Word Embedding Substitution":
-        word2vec_path = download_word2vec()  # Ensure the model is downloaded
-        aug = naw.WordEmbsAug(model_type='word2vec', model_path=word2vec_path, action="substitute")
+        # Use BERT for word embedding substitution instead of Word2Vec
+        aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action='substitute')
     elif method == "Contextual Word Insertion":
         aug = naw.ContextualWordEmbsAug(model_path="bert-base-uncased", action="insert")
     elif method == "Back Translation":